JP7684089B2 - Secondary battery management device, secondary battery management method, and program - Google Patents

Description

The present invention relates to a secondary battery management device, a secondary battery management method, and a program.

Environmental issues have been receiving a lot of attention, and in order to prevent global warming, it is necessary to reduce carbon dioxide emissions in all situations. Against this background, automobiles using gasoline engines, which are one of the major sources of carbon dioxide emissions, are being replaced by hybrid electric vehicles and electric vehicles. The performance of the on-board batteries that serve as the power source for electric vehicles and hybrid electric vehicles affects their driving performance, so in recent years, high-performance, lightweight lithium-ion batteries have been used, and the price of driving batteries is high, and the price of electric vehicles is higher than that of gasoline-powered vehicles in the same class. Users want long-term operation, and manufacturers want to keep costs down with an optimal battery capacity.
In general, the capacity of secondary batteries decreases with charging and discharging. In particular, it is known that the capacity of _lithium secondary batteries that use a combination of a positive electrode material containing lithium ions, such as _LiCoO2 or _{LiMnxNiyCo (1-xy) O2} (x+y>1), and a negative electrode material such as graphite or amorphous carbon material, is prone to deterioration even when fully charged.

As background technology in this technical field, the abstract of the following Patent Document 1 states that "the electric vehicle includes a battery remaining capacity detection unit 15b that detects the remaining battery capacity of the battery, a next trip information detection unit 15e that detects the departure time of the next trip using the electric vehicle and the required power consumption for the trip as next trip information, and a charge control unit 15c that determines the charging speed and charging amount to charge the required power consumption by the departure time of the next trip based on the remaining battery capacity and the next trip information."

The electric vehicle charging control device described in Patent Document 1 controls charging by taking into account battery degradation cost, which is the degree of impact on battery degradation, when charging multiple shared electric vehicles until the next trip, with the aim of preventing deterioration that would occur if the onboard battery is charged to full charge every time. Based on the remaining battery charge and the detected departure time and required power consumption for the next trip, the charging speed and charge amount that will charge the required power consumption by the departure time of the next trip are determined, and charging is performed to control charging of multiple vehicles with optimized charging speed and charge amount.

JP 2013-90360 A

In recent years, the online shopping market has expanded, and the volume of goods being shipped and delivered has increased. This calls for improved delivery efficiency, as well as a reduction in carbon dioxide emissions, as described in the background art. Against this background, it is expected that the number of vehicles powered by large secondary batteries, such as electric vehicles and plug-in hybrid vehicles, will increase for delivery and delivery vehicles. Electric vehicles and plug-in hybrid electric vehicles for delivery and delivery require large, high-capacity secondary batteries as their power source. For electric vehicles, the amount of energy required increases as the vehicle becomes larger, and the battery weight increases, creating a trade-off between payload and battery cost. The weight of the vehicle body and the performance of the battery affect driving, and in order to reduce logistics costs and be environmentally friendly, it is important that on-board batteries are operated efficiently with optimal battery capacity.

However, secondary batteries such as lithium-ion batteries deteriorate with each charge and discharge cycle, reducing capacity and increasing internal resistance. This causes fluctuations in the output of the secondary battery. The degree of deterioration of a secondary battery varies depending on the usage history of the secondary battery, such as the environment and method in which it has been used up to now.

As mentioned above, the technology that applies the contents of Patent Document 1 can speed up the charging time of electric vehicles. However, what is mainly taken into consideration is the degradation effect caused by rapid charging. For this reason, no consideration is given to how the battery will be used when the electric vehicle travels on its delivery journey, and the possibility of accelerated battery degradation cannot be avoided.

For example, when it comes to delivery and distribution trucks, they are not necessarily in an environment where they can avoid direct sunlight or glare from the road surface when they are operating, stopped, or parked during the day, and temperatures can get particularly high in the summer, so the higher the SOC (state of charge) is maintained, the faster the degradation will be. The characteristics of secondary batteries are greatly affected by temperature, and in particular, characteristics such as capacity and resistance change. The details will be explained with reference to Figure 1.

FIG. 1 is a diagram showing examples of various characteristics of a secondary battery (for example, a lithium ion battery).
First, graph G2 is a graph showing the resistance value ratio DCR of the internal resistance versus the temperature of the secondary battery. In graph G2, the horizontal axis is the temperature of the secondary battery, and the vertical axis is the resistance value ratio DCR. Here, the resistance value ratio DCR represents the ratio at each temperature, with the resistance value at 25°C being "1". The lower the temperature, the higher the resistance and the smaller the output.

Graph G4 is a graph showing the capacity retention rate versus the SOC and temperature of the secondary battery. In graph G4, the horizontal axis is the SOC of the secondary battery, and the vertical axis is the capacity retention rate when the secondary battery is stored for a specified period of time. Characteristics G4-25, G4-50, G4-65, and G4-80 are characteristics at battery temperatures of 25°C, 50°C, 65°C, and 80°C, respectively. As shown in graph G4, the higher the SOC and the higher the battery temperature, the lower the capacity retention rate.

Graph G6 shows the rate of increase in internal resistance when a secondary battery is stored for six months at various temperatures. As shown, the higher the temperature, the higher the rate of increase in resistance and the greater the degradation. Graph G8 shows the rate of increase in resistance when a secondary battery is stored for 190 days at 25°C at various SOCs. As shown, the higher the SOC, the higher the rate of increase in resistance and the greater the degradation. The example shown here is just one example, and the degradation characteristics differ depending on the type and specifications of the battery material, and degradation varies depending on how it is used.

As mentioned above, Patent Document 1 takes into consideration the degradation effect due to rapid charging. It does not take into consideration how the battery is used when the electric vehicle runs after charging, so it is impossible to avoid the possibility that the battery degradation will be accelerated depending on the driving conditions. Therefore, it would be preferable to provide an optimal charging navigation system that reflects the degree of degradation of the secondary battery module in vehicles that use batteries as a power source for delivery and distribution, and that is linked to a delivery and distribution system that efficiently charges in a short time and effectively utilizes the battery.
The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide a secondary battery management device, a secondary battery management method, and a program that reflect the state during driving to suppress the rate of deterioration of the secondary battery and enable stable driving.

In order to solve the above problem, the secondary battery management device of the present invention is characterized in that it comprises: an electric energy calculation unit that calculates a predicted amount of electric energy that is predicted to be required for a mobile body powered by a secondary battery to move along a specified planned travel route based on an electric energy database that stores required electric energy corresponding to the travel date and time or outside air temperature during travel and the travel route of the mobile body; and a driving start state determination unit that refers to a battery degradation database that records the past usage state of the secondary battery along with data indicating the correlation between the usage state and degradation state of the secondary battery, calculates a usage SOC range or usage voltage range of the secondary battery in which the predicted amount of electric energy can be secured according to the degradation state of the secondary battery, and determines a driving start condition, which is the voltage or SOC of the secondary battery in a fully charged state, based on the calculated usage SOC range or usage voltage range.

Specifically, the device is characterized by comprising: an electric energy calculation unit that calculates a predicted amount of electric energy that is predicted to be required for a mobile body powered by a secondary battery to move along a specified planned travel route based on an electric energy database that stores required electric energy corresponding to the travel date and time or the outside air temperature during travel and the travel route; and a travel start state determination unit that records the deterioration state of the secondary battery, or refers to a battery deterioration database that records the past usage state of the secondary battery together with data indicating the correlation between the usage state of the secondary battery and the deterioration state, calculates a usage SOC range or usage voltage range of the secondary battery in which the predicted amount of electric energy can be secured according to the deterioration state, determines a recommended voltage or recommended SOC of the secondary battery at the start of travel based on the calculated usage SOC range or usage voltage range, and determines a travel start condition, which is the voltage or SOC of the secondary battery in a fully charged state.
In a preferred embodiment described later, in a vehicle for delivery and distribution that uses a battery as a power source, charging is performed efficiently while reflecting the degree of deterioration of the secondary battery module, and the battery is utilized effectively. Furthermore, in a preferred embodiment, an optimal charging navigation system linked to a delivery and distribution system is provided. For this reason, the secondary battery management device according to the preferred embodiment calculates a predicted amount of power required based on previously accumulated driving route driving history during delivery and distribution, the current deterioration state of the secondary battery in the vehicle, the environmental temperature, the planned delivery route, and the load.

In a preferred embodiment, an SOC range to be actually applied is selected from at least two or more different SOC ranges based on the predicted power amount, the planned delivery route, the stopping time on the planned delivery route, and the battery temperature or air temperature, and a starting charging rate SOCs, which is the SOC at the start of driving or when charging is completed, is determined.

The present invention appropriately determines the amount of power required for driving, shortens the waiting time for charging while the vehicle is in operation, and appropriately determines the state of charge of the secondary battery. By using the battery under conditions that are less likely to deteriorate, it is possible to optimize the required battery capacity and improve the cost performance of the vehicle.

5A to 5C are diagrams illustrating examples of various characteristics of a secondary battery. 1 is a block diagram of a secondary battery management system according to a first preferred embodiment; FIG. 11 is a block diagram of a secondary battery management system according to a second preferred embodiment. FIG. 4 is an explanatory diagram of SOC region selection showing the relationship between the SOC and voltage of a secondary battery. FIG. 13 is a diagram showing a structure of time-series data in the third preferred embodiment. FIG. 13 is a block diagram of a secondary battery management system according to a fourth preferred embodiment. 13 is a flowchart of a travel start charging rate calculation program executed by a secondary battery management device of a fourth embodiment. FIG. 13 is a block diagram of a charging navigation system according to a fifth preferred embodiment. FIG. 13 is a block diagram showing a method for detecting a deterioration level of a secondary battery module of a vehicle BMS in a fifth embodiment. FIG. 13 is a schematic diagram of power amount correction data included in a power amount database according to the fifth embodiment. FIG. 13 is a schematic diagram of driving record data included in an electric power amount database according to a sixth embodiment. FIG. 13 is a block diagram of a secondary battery deterioration detection system according to a seventh preferred embodiment. 23 is a flowchart of a deterioration detection routine executed in a server machine according to the seventh embodiment. FIG. 4 is a diagram showing various characteristics of a secondary battery. FIG. 13 is a block diagram of an in-vehicle battery management system according to an eighth preferred embodiment. FIG. 13 is a block diagram of a secondary battery management system according to a ninth preferred embodiment. 5 is a diagram showing an example of changes over time in the battery voltage, charge/discharge current, and SOC of a secondary battery detected by a vehicle; FIG. 23 is a block diagram of a secondary battery management system according to a tenth preferred embodiment.

During storage of a secondary battery, it is preferable to keep the SOC at which the secondary battery remains as low as possible.
Therefore, in a preferred embodiment described below, in a vehicle for delivery and distribution that uses a battery as a power source, the degree of deterioration of the secondary battery module is reflected, charging is performed efficiently in a short time, and the battery is effectively utilized. Furthermore, in a preferred embodiment, an optimal charging navigation system linked to a delivery and distribution system is provided. Therefore, in the secondary battery management device according to the preferred embodiment, a predicted amount of power required is calculated based on the previously accumulated driving route driving history during delivery and distribution, the current deterioration state of the secondary battery in the vehicle, the environmental temperature, the planned delivery route, and the load.

In a preferred embodiment, an SOC range to be actually applied is selected from at least two or more different SOC ranges based on the predicted power amount, the planned delivery route, the stopping time along the planned delivery route, and the battery temperature or air temperature, and a starting charging rate SOCs, which is the SOC at the start of driving or the completion of charging, is determined.
Thus, according to a preferred embodiment, it is possible to ensure the travel distance (predicted power amount) in the area with the least deterioration, charge in a short time, appropriately manage the predicted power amount, use the battery efficiently, reduce the amount of battery installed to the minimum necessary, and improve cost performance.

[First embodiment]
FIG. 2 is a block diagram of a secondary battery management system S1 according to the first preferred embodiment.
The secondary battery management system S1 includes a secondary battery management device 100A (computer), an electric energy database 142, and a battery deterioration database 144.
The secondary battery management device 100A includes hardware as a general computer, such as a central processing unit (CPU), random access memory (RAM), read only memory (ROM), and hard disk drive (HDD), and the SSD or HDD stores an operating system (OS), application programs, various data, and the like. The OS and application programs are deployed in the RAM and executed by the CPU. In FIG. 2, the inside of the secondary battery management device 100A shows functions realized by application programs, etc., as blocks.

That is, the secondary battery management device 100A includes an electric power calculation unit 112 (electric power calculation means, electric power calculation process) and a running start state determination unit 114 (charging condition determination means, charging condition determination process). The secondary battery management device 100A manages a secondary battery (not shown) that is installed in the vehicle as a power source. Here, the secondary battery management device 100A may be a computer installed in the vehicle, or may be a computer separate from the vehicle.

The vehicle delivers luggage and the like along various travel routes. The power amount database 142 stores history information indicating the usage history of the vehicle in association with the standard required power amount. Here, the history information includes at least the travel date and time or the outside temperature at the time of travel, and route information indicating the travel route. For example, when the user inputs planned route information Rt (planned travel route) specifying the planned travel route, the power amount calculation unit 112 calculates the predicted power amounts W1 and W2 that are predicted to be required by the vehicle to travel the planned travel route based on the power amount database 142. Here, the predicted power amount W1 is the predicted power amount calculated based on the planned route information Rt, and the predicted power amount W2 is the result of correcting the predicted power amount W1 based on the deterioration state of the secondary battery, etc.

The battery degradation database 144 is configured to include correlation data that can estimate the battery usage conditions and changes in the battery capacity and resistance. However, instead of the current degradation state, past usage states (e.g., changes in ambient temperature, amount of power output, etc.) and degradation impact data may be stored. Here, the degradation impact data is data that describes the correlation between the usage conditions and degradation state of the secondary battery. The running start state determination unit 114 calculates the usage voltage region or usage SOC region of the secondary battery that can secure the predicted power amount W2 based on the battery degradation database 144.

The operating voltage range is the range from the voltage at the start of discharging the secondary battery to the voltage at the end of discharging when the vehicle travels along the planned travel route indicated by the planned route information Rt. The operating SOC range is the range from the SOC at the start of discharging to the SOC at the end of discharging. It is preferable that the travel start state determination unit 114 determines the voltage range to be used so as not to accelerate battery deterioration.

The driving start state determination unit 114 refers to the battery degradation database 144 and calculates the operating voltage range appropriate for operation. Then, based on the operating voltage range, it determines and outputs the driving start voltage Vs (driving start condition), which is the voltage of the secondary battery in the charging complete state, or the driving start charging rate SOCs (driving start condition), which is the SOC of the secondary battery in the charging complete state, as the charging complete condition.

Here, the actual running start voltage Vs may be the upper limit of the operating voltage range described above, or a voltage obtained by adding a small margin to that upper limit. Similarly, the running start charge rate SOCs may be an SOC corresponding to the upper limit of the operating voltage range, or an SOC corresponding to a voltage obtained by adding a small margin to that upper limit. Hereinafter, Vs and/or SOCs may be collectively referred to as the "running start condition (SOCs, Vs)."

When the vehicle ECU recognizes the driving start condition (SOCs, Vs) output by the driving start state determination unit 114, it sets the driving start condition (SOCs, Vs) in the charging device (not shown). As a result, the charging device starts charging the secondary battery. Then, when the SOC or output voltage of the secondary battery reaches the driving start condition (SOCs, Vs), the charging device ends the charging operation.
However, when the SOC or battery voltage of the secondary battery 11 is equal to or higher than the running start condition (SOCs, Vs), the vehicle can skip the charging operation and start running.

[Second embodiment]
3 is a block diagram of a secondary battery management system S2 according to a preferred second embodiment. In the following description, parts corresponding to those in the first embodiment are given the same reference numerals, and the description thereof may be omitted.
The secondary battery management system S2 includes a charging device 200, a secondary battery management device 100B (computer), an electric energy database 142, and a battery deterioration database 144.

The hardware configuration of the secondary battery management device 100B is the same as that of the secondary battery management device 100A of the first embodiment. And, like the secondary battery management device 100A, the secondary battery management device 100B includes an electric energy calculation unit 112 and a running start state determination unit 114. Furthermore, the secondary battery management device 100B includes a database update unit 118 and an analysis unit 120.

This embodiment also manages multiple vehicles 10 (mobiles). However, FIG. 3 illustrates only one vehicle 10. Each vehicle 10 is provided with a secondary battery 11 as a power source and a data processing unit 12. The data processing unit 12 measures the position information of the vehicle 10 and the state of the secondary battery 11 (voltage, current, etc.) at a predetermined sampling period, and accumulates and outputs a certain amount of the results as time-series data D10. The data processing unit 12 then transmits the time-series data D10 to the analysis unit 120 of the secondary battery management device 100B wirelessly or via a wired connection. The transmission timing may be real-time, but transmission is performed at an appropriate timing depending on the communication load and the amount of data that can be accumulated. For example, transmission is performed when parking, after stopping, or when the starter is started during one run.

The analysis unit 120 also analyzes the time series data D10 received from multiple vehicles 10, and generates data to be stored in the power amount database 142 and the battery deterioration database 144 based on the contents of the data. The database update unit 118 updates the power amount database 142 and the battery deterioration database 144 based on the data generated by the analysis unit 120.

In the present embodiment, the power database 142 stores history information and standard required power amounts in association with each other, similar to the first embodiment. The history information in this embodiment also includes the travel date and time, the outside air temperature during travel, route information such as a delivery route, location information of the vehicle 10, the usage time, the volume of the vehicle 10, the load of the vehicle 10, a vehicle ID that identifies the vehicle 10, and a driver ID that identifies the driver.

The amount of power required by the vehicle 10 varies depending on the weight and load of the vehicle 10, even for the same route, and the power consumption also varies. Therefore, by accumulating and using the relationship between the vehicle weight and load and the amount of power required, the predicted power amounts W1 and W2 can be estimated more accurately. It is also preferable to store the weight and load of the vehicle 10 in conjunction with a truck scale. Furthermore, the vehicle weight and load may be values calculated by adding up the vehicle type data, the sum of the weights of the loaded luggage, the weight of the driver, etc. The power amount calculation unit 112 refers to this power amount database 142 to calculate the predicted amount of power W1 for the vehicle 10 to travel from now on.

Furthermore, the power calculation unit 112 acquires the degradation state of the secondary battery 11 based on the degradation level SOH detected by the in-vehicle BMS or the charger, corrects the predicted power amount W1 based on the current degradation state of the secondary battery 11 and the assumed temperature Tass, and outputs the correction result as the predicted power amount W2. Here, the assumed temperature Tass may be, for example, a temperature acquired from a temperature sensor (not shown) attached to the secondary battery 11, or may be the air temperature during the delivery time period predicted by a weather forecast or the like.

In addition, it is preferable that the driving start state determination unit 114 determines the charging completion condition, i.e., the driving start condition (SOCs, Vs), from the usage conditions using artificial intelligence that performs learning processing using supervised learning on the battery deterioration database 144.

Here, the driving start state determination unit 114 may store the relationship between the deterioration conditions of the secondary battery 11 and the optimal driving start conditions (SOCs, Vs) as data in a map (table) format, and determine the driving start conditions (SOCs, Vs) by referring to this map-format data. Also, the relationship between the usage conditions of the secondary battery 11 and the optimal driving start conditions (SOCs, Vs) may be stored in map format. Furthermore, the relationship between the battery usage conditions, the amount of power, and the optimal driving start conditions (SOCs, Vs) may be stored in map format.

The charging control unit 124 determines a charging request (output, time, and other information) based on the above-mentioned driving start conditions (SOCs, Vs) and the duty ratio Dtr, and controls the charging device 200 based on the determined charging request. The function of the driving start state determination unit 114 is similar to that of the first embodiment, but the driving start state determination unit 114 of this embodiment receives an expected temperature Tass, which is an expected value of the environmental temperature of the target secondary battery 11.

Then, the running start state determination unit 114 sets the SOCs or Vs so that the running start condition (SOCs, Vs) for the same predicted power amount W2 becomes lower as the assumed temperature Tass becomes higher. Here, the running start state determination unit 114 may store the relationship between the deterioration condition of the secondary battery 11 and the running start condition (SOCs, Vs) as a two-dimensional map (table). In addition, the running start state determination unit 114 may implement the relationship between the usage condition of the secondary battery 11 and the optimal running start voltage Vs in a map. Furthermore, the running start state determination unit 114 may implement the relationship between the usage condition of the secondary battery 11, the predicted power amount W2, and the optimal running start voltage Vs in a map.

Here, a more detailed description will be given of the operation of the running start state determination unit 114. An example of an operation for determining a usable SOC range by referring to the current SOC of the secondary battery 11 will be described with reference to Fig. 4 .
FIG. 4 is a diagram showing the relationship between the SOC and the voltage of the secondary battery 11. As shown in FIG.
The horizontal axis in Fig. 4 is SOC, with the left end being 100% and the right end being 0%. As shown in the figure, the open circuit voltage OCV of the secondary battery 11 and the closed circuit voltage CCV when a predetermined current flows decrease as the SOC decreases. The hatched areas EA1 and EA2 are examples of voltage areas (or SOC areas) in which the above-mentioned predicted power amount W2 can be secured, and these are candidates for the operating voltage area or operating SOC area.

Note that in addition to those shown in the figure, various voltage ranges or SOC ranges can be considered as candidates for the operating voltage range or operating SOC range. The SOC in the low-voltage side range EA1 ranges from the lower limit value SOC1L to the upper limit value SOC1H, and the difference between the two (SOC1H-SOC1L) is called the SOC range ΔSOC1. Similarly, the SOC in the high-voltage side range EA2 ranges from the lower limit value SOC2L to the upper limit value SOC2H, and the difference between the two (SOC2H-SOC2L) is called the SOC range ΔSOC2.

The upper limit values SOC1H and SOC2H shown in FIG. 4 are candidate values for the above-mentioned running start charging rate SOCs. Incidentally, as previously shown in graphs G6 and G8 in FIG. 1, the rate of deterioration of lithium-ion batteries tends to be faster as the storage temperature increases and as the SOC during storage increases. Therefore, the running start state determination unit 114 takes into account these deterioration characteristics and the temperature during operation, sets a usage SOC range in which the secondary battery 11 is as unlikely to deteriorate as possible, and outputs the upper limit value (e.g., SOC1H, SOC2H) of the usage SOC range as the running start charging rate SOCs. Alternatively, the running start state determination unit 114 may output the open circuit voltage OCV corresponding to the upper limit value of the usage SOC range as the running start voltage Vs.

This allows the secondary battery 11 to be operated while ensuring the necessary predicted power amount W2 and suppressing deterioration. Furthermore, the charging time can be shortened compared to when the secondary battery 11 is always fully charged, making it possible to operate the vehicle 10 efficiently. For example, in summer, the temperature is high and the secondary battery 11 is likely to become hot due to radiant heat and waste heat, which makes it easier for deterioration of the secondary battery 11 to progress. Therefore, in summer, it is preferable to set the SOC region to be used on the low SOC side (for example, region EA1 in Figure 4) and shorten the charging time. In this case, the upper limit value SOC1H is selected as the driving start charging rate SOCs.

Conversely, in winter when temperatures are low, as shown in graph G2 of FIG. 1, the resistance ratio DCR increases, so the closed circuit voltage CCV during discharge tends to be lower than the open circuit voltage OCV. This results in a smaller battery capacity that can actually be used compared to the actual SOC (which is generally highly dependent on temperature and rate characteristics). On the other hand, as shown in graph G6, the rate of deterioration slows down at low temperatures below 20°C. Therefore, in winter, it is preferable to prioritize securing the amount of power and set the SOC region to the high SOC side (for example, region EA2 in FIG. 4). In this case, the upper limit value SOC2H is selected as the driving start charging rate SOCs.

The rate at which the deterioration of a secondary battery, particularly a lithium-ion battery, progresses varies depending on the range of SOC used, the temperature at which it is used, the current flowing through it, the operating voltage range, etc. For this reason, the driving start state determination unit 114 selects the optimal SOC range to be used from a database of these relationships in the form of an equation or map.

[Third embodiment]
Next, a preferred third embodiment will be described. In the following description, the same reference numerals are used to designate parts corresponding to those in the other embodiments described above, and the description thereof may be omitted.
Fig. 5 is a diagram showing the structure of the time series data D10 in the third embodiment. The configuration of the third embodiment is similar to that of the second embodiment (Fig. 3), but the time series data D10 output by the data processing unit 12 of each vehicle 10 is configured as shown in Fig. 5.

Each row in FIG. 5 is a record of the time series data D10, and the database update unit 118 (see FIG. 3) creates one record for each predetermined sampling period while the vehicle 10 is traveling. Each record also includes the date D11, the time D12, the vehicle longitude D13, the vehicle latitude D14, the battery temperature D15, the battery voltage D16, the battery current D17, and the amount of power D18.

The date D11 and time D12 are the date and time of the sampling timing. The vehicle longitude D13 and vehicle latitude D14 are the longitude and latitude of the vehicle 10 at the sampling timing. The battery temperature D15, battery voltage D16, and battery current D17 are the temperature, output voltage, and output current of the secondary battery 11 at the sampling timing. The amount of power D18 is the amount of power output from the secondary battery 11 up to the sampling timing after the secondary battery 11 was last charged.

The data processing unit 12 of each vehicle 10 wirelessly transmits the time series data D10 to the analysis unit 120 in real time at each sampling timing. As a result, the analysis unit 120 generates data to be stored in the power amount database 142 and the battery deterioration database 144 in real time. The database update unit 118 then updates the power amount database 142 and the battery deterioration database 144 in real time based on the data generated by the analysis unit 120.

However, the data processing unit 12 of each vehicle 10 does not necessarily need to transmit the time series data D10 to the analysis unit 120 in real time. For example, the data processing unit 12 may start accumulating the time series data D10 after charging of the secondary battery 11 is completed, and then supply the accumulated time series data D10 to the analysis unit 120 when the vehicle 10 is again connected to the charging device 200 for charging. In this case, the database update unit 118 updates the power amount database 142 and the battery deterioration database 144 while the vehicle 10 is connected to the charging device 200.

The analysis unit 120 also accumulates the time for each SOC class width for the relationship between time and SOC based on the time series data D10. Then, it detects the time in the time series data during which the SOC remains in a predetermined range where it is approximately constant for a predetermined time or more. The detected time is, for example, a "parking period" during which the SOC does not fluctuate, i.e., the battery usage is low. Even during the parking period, the vehicle 10 supplies power to auxiliary equipment, etc., so the SOC fluctuates slightly, but it can be considered to be approximately constant. The SOC during this parking period is called the pause section stay SOC. The analysis unit 120 extracts the pause section stay SOC at this time along with the stay time. For example, in the section Tr10 from time t14 to t20 in FIG. 5, the vehicle longitude D13 and vehicle latitude D14 are constant values (L13 and Lo13). Therefore, the analysis unit 120 identifies the period Tr10 as the parking section.

It is more preferable that the driving start state determination unit 114 has a function of receiving the assumed temperature Tass of the secondary battery 11, and a function of determining the driving start conditions (SOCs, Vs) so that the rest section stay SOC of the secondary battery 11 is low when the received assumed temperature Tass is higher than the threshold temperature of the acceptable deterioration behavior.

The analysis unit 120 also calculates the relationship between the standard amount of power and temperature during driving based on the pattern obtained by analyzing the driving period and stay period of the vehicle 10. Furthermore, the analysis unit 120 divides route information such as a delivery route into multiple partial routes, and analyzes the relationship between the average battery current D17, the SOC fluctuation range (ΔSOC), the central SOC (the median SOC of the partial routes), etc. for each partial route.

The analysis unit 120 then stores the analysis results as a "detailed route travel database" in the power amount database 142 via the database update unit 118. The travel start state determination unit 114 then predicts the degradation state of the secondary battery 11 in the divided partial route based on the relationship between this detailed route travel database and the battery voltage D16. This allows the travel start state determination unit 114 to determine a usable SOC range in which degradation is suppressed in the predicted value of the degradation state.

The analysis unit 120 also extracts data necessary for the power amount database 142, and updates the power amount database 142 via the database update unit 118 based on the extracted data.
In addition, the route information is preferably linked to a time-prioritized, shortest distance route recommended by a navigation system (not shown). Furthermore, the navigation system is preferably capable of proposing a one-stroke route that reflects traffic regulations such as one-way streets and takes into account efficient delivery orders for each time period, and the route information is preferably linked to this one-stroke route. This makes it possible to calculate a typical predicted energy Wrec during delivery.

As the route delivery history is accumulated, the power amount database 142 is enriched, thereby improving the accuracy of calculation of the predicted power amount W2 for a typical route.
During this history accumulation, the analysis unit 120 calculates the degree of deterioration SOHQ (deterioration state) related to the capacity and the degree of deterioration SOHR (deterioration state) related to the resistance, and records these calculation results in the battery deterioration database 144. However, the degrees of deterioration SOHQ and SOHR may be calculated not only by the BMS inside the vehicle or the secondary battery management device 100A, but also by the charging device 200. For example, the degrees of deterioration SOHQ and SOHR may be detected based on the voltage, current, temperature, etc. of the secondary battery 11 when the charging device 200 charges the secondary battery 11.

The battery deterioration database 144 stores the usage conditions of the secondary battery 11, the internal resistance of the secondary battery 11, and correlation data that can estimate changes in capacity. As described above, the usage conditions of the secondary battery 11 include at least two or more of the temperature, current, usage voltage range, usage SOC range, amount of electricity used, and rest ratio. Here, the deterioration calculation unit may be any of a time series, daily unit, cycle unit, or number of times unit.

Furthermore, by using the analysis log of the SOC and temperature residence time of the secondary battery 11 in the vehicle 10, the deterioration degree can be calculated for the operating range of the secondary battery 11 by multiplying the deterioration coefficient by a weighting coefficient γ, and deterioration can be taken into account in a simple manner. In this embodiment, the deterioration estimation can be performed not only by the above-mentioned method, but also by using correlation data between the operating range of the SOC of the secondary battery 11, the temperature, and deterioration.

The running start state determination unit 114 may determine the running start charging rate SOCs of the secondary battery 11 based on the predicted power amount W2 and the current SOC as follows. First, in a discharge curve reflecting the current deterioration of the secondary battery 11, the upper limit SOC value at which the predicted power amount W2 can be secured is calculated from the SOC of the output lower limit value (e.g., 10%) toward the higher SOC side. This SOC is called SOCreq.

Next, the running start state determination unit 114 calculates the lower limit SOC when the predicted power amount W2 is output from the current SOC, _SOCpre , and if the calculated SOC is higher than the above-mentioned output lower limit SOC, it may set the current SOC, _SOCpre , as the running start charging rate SOCs. On the other hand, if the SOC calculated from the current SOC is equal to or lower than the output lower limit SOC, it may set the running start charging rate SOCs to SOCreq.

[Fourth embodiment]
6 is a block diagram of a secondary battery management system S4 according to a preferred fourth embodiment. In the following description, the same reference numerals are used to designate parts corresponding to the parts of the other embodiments described above, and the description thereof may be omitted.
The secondary battery management system S4 includes a charging device 200, a secondary battery management device 100D (computer), an electric energy database 142, a battery deterioration database 144, and an operation schedule planning unit 146.

The operation schedule planning unit 146 plans operation schedules for each vehicle 10 according to the weight, dimensions, and delivery destination locations of the packages to be delivered by the multiple vehicles 10. The secondary battery management device 100D has a configuration similar to that of the secondary battery management device 100B of the second embodiment (see FIG. 3), and further includes a charging schedule planning unit 122. The charging schedule planning unit 122 plans a charging schedule for each vehicle 10, i.e., charging start times, charging end times, etc., based on the operation schedule output by the operation schedule planning unit 146.

The secondary battery management device 100D in this embodiment may have a duty ratio determination unit 116. The duty ratio determination unit 116 determines the duty ratio Dtr, which is the proportion of the current-carrying state until charging is completed. Here, the current-carrying state is a state in which a current is supplied to the secondary battery 11. In addition, a state in which no current is supplied to the secondary battery 11, or a state in which a current smaller than the threshold current is supplied, is a pause state.

The duty ratio determination unit 116 has a function of determining the degradation state of the secondary battery 11 based on the battery degradation database 144, and lowering the duty ratio Dtr as the degradation of the secondary battery 11 progresses. The charging control unit 124 controls the completion time of the charging device 200 based on the above-mentioned running start conditions (SOCs, Vs) and the duty ratio Dtr. By controlling the rest time after charging according to the degree of battery degradation, it is possible to further suppress battery degradation. Note that the secondary battery management device 100D can also be configured without the duty ratio determination unit 116.

This embodiment is particularly suitable for use in commercial last-mile delivery systems where an electric vehicle is used as the vehicle 10. The operation schedule planning unit 146 sets an optimal standard route RS(t) for home-to-home delivery by time period in the delivery area. Here, t is the delivery start time or delivery time period.

When the charging device 200 is installed only at the business office, the power calculation unit 112 cooperates with the delivery or logistics system, and the operation schedule planning unit 146 sorts the delivery packages by time period and calculates the predicted amount of power W1 required for each route in the delivery area based on the power database 142. Here, the predicted amount of power W1 is also a function of the delivery start time t, so hereinafter it may be referred to as "predicted amount of power W1(t)".

The power calculation unit 112 further corrects the predicted power amount W1(t) based on conditions such as the load on the vehicle 10, the current deterioration level of the secondary battery 11, the air temperature, the assumed temperature Tass, and weather, to determine the predicted power amount W2(t). The running start state determination unit 114 determines the use SOC region (e.g., regions EA1 and EA2 in FIG. 4) based on the determined predicted power amount W2(t) and the assumed temperature Tass, and determines the running start condition (SOCs, Vs).

At this time, the driving start state determination unit 114 refers to the battery deterioration database 144 and determines an upper SOC value so that the predicted power amount W2 can be secured and the minimum SOC during use does not fall below the SOC lower limit value (e.g. 10%). Then, the driving start state determination unit 114 outputs the driving start condition (SOCs, Vs) based on the calculation result. At this time, if the current voltage of the secondary battery 11 is higher than the driving start voltage Vs, delivery is started without charging. On the other hand, if the current voltage of the secondary battery 11 is lower than the driving start voltage Vs, it is recommended that the secondary battery 11 be charged by the charging device 200. This can save and shorten the overall charging time.

The amount of electricity required to deliver a large amount of luggage differs between when a single fully charged vehicle 10 is used to deliver the luggage and when multiple vehicles 10 deliver the luggage over separate time periods. In general, when there is a large amount of luggage, when multiple vehicles 10 deliver the luggage over separate time periods, the charging time of the vehicle 10 can be shortened and deterioration of the secondary battery 11 can be suppressed. Therefore, the operation schedule planning unit 146 uses a data selection system that utilizes machine learning to distribute luggage without a specified arrival time along the delivery route.

The charging schedule planning unit 122 controls the charging schedule so that the secondary battery 11 is charged during the time period when each vehicle is parked at the office (near the charging device 200). This allows the secondary battery management system S4 to deliver packages efficiently without reducing customer satisfaction. Furthermore, the secondary battery management system S4 can also suppress battery deterioration in the vehicle 10, which is an electric vehicle, making it possible to reduce excess battery capacity.

FIG. 7 is a flowchart of a start-of-travel charging rate calculation program executed by the secondary-battery management unit 100D of the fourth embodiment.
7, the power amount calculation unit 112 receives planned route information Rt of the planned travel route, the vehicle weight of the vehicle 10, the load of the vehicle 10, and the estimated temperature Tass at the time of delivery. Then, the power amount calculation unit 112 accesses the power amount database 142 based on the received information to obtain the predicted power amount W1. As described above, the power amount database 142 stores history information including the travel date and time, the outside air temperature at the time of travel, etc., in association with the standard required power amount.

Next, in step S12, the power calculation unit 112 acquires the assumed temperature Tass and, at the same time, acquires the current deterioration information (deterioration levels SOHQ, SOHR, etc.) of the secondary battery 11. Then, based on the acquired information and the battery deterioration database 144, the predicted power amount W1 is corrected as necessary to calculate the predicted power amount W2.

The predicted power amount W2 can be calculated, for example, based on the correction formula "W2 = α x W1 + βd". Here, α is a correction coefficient, and βd is an auxiliary power margin, etc. Alternatively, the predicted power amount W2 may be calculated by correcting the predicted power amount W1 by a multiple, for example, based on the correction formula "W2 = α x W1 x βdd". Here, βdd is an auxiliary power margin, etc., based on a multiple. Furthermore, the predicted power amount W2 may be calculated by increasing or decreasing a fixed value with respect to the predicted power amount W1.

Next, in step S13, the driving start state determination unit 114 determines whether the predicted power amount W2 can be output with the current SOC of the secondary battery 11. That is, when the current SOC is set to the maximum SOC, it determines whether the predicted power amount W2 can be obtained in the SOC range up to the SOC lower limit value (e.g., 10%). If the predicted power amount W2 can be output, it is determined as "Yes" and the process proceeds to step S18. In step S18, the current SOC is output as the driving start charging rate SOCs. On the other hand, if the predicted power amount W2 cannot be output with the current SOC of the secondary battery 11, it is determined as "No" in step S13 and the process proceeds to step S14.

In step S14, the driving start state determination unit 114 calculates the SOC region in which the predicted power amount W2 can be obtained with the SOC lower limit value as the minimum value, i.e., SOC region EA01 (not shown). Next, when the process proceeds to step S15, the driving start state determination unit 114 calculates the SOC region in which the predicted power amount W2 can be obtained, i.e., SOC region EA02 (not shown), centered around SOC 50%, which is a region with little degradation.

Next, when the process proceeds to step S16, the driving start state determination unit 114 calculates the SOC region in which the predicted power amount W2 can be obtained with the SOC upper limit value (e.g., 100%) as the maximum value, that is, the SOC region EA03 (not shown). Next, when the process proceeds to step S17, the driving start state determination unit 114 refers to the battery deterioration database 144 and selects, as the use SOC region, one of the SOC regions EA01, EA02, and EA03 in which the deterioration of the secondary battery 11 is predicted to be the smallest. Next, when the process proceeds to step S19, the driving start state determination unit 114 outputs the maximum value of the use SOC region as the driving start charging rate SOCs.

In the above example, the driving start conditions (SOCs, Vs) are determined by calculating three SOC ranges EA01, EA02, and EA03 and determining the usable SOC range from among them, but the method of determining the driving start conditions (SOCs, Vs) is not limited to this. For example, multiple SOCs that are candidates for the driving start charge rate SOCs may be determined in advance, and for each of them, the SOC range in which the predicted power amount W2 can be secured may be calculated, and the usable SOC range may be determined by referring to the battery deterioration database 144 for each calculated SOC range. Note that if there are multiple calculated SOC ranges with the same degree of deterioration, it is preferable to select the SOC range on the higher SOC side that provides the longest driving range.

[Fifth embodiment]
8 is a block diagram of a charging navigation system S5 according to a preferred fifth embodiment. In the following description, the same reference numerals are used to designate parts corresponding to those in the other embodiments described above, and the description thereof may be omitted.
The charging navigation system S5 includes a secondary battery management device 100E (computer) and a vehicle 10. The charging navigation system S5 is also connected to a weather forecast system 131, a delivery navigation system 132, and a truck scale system 133.

Here, the weather forecast system 131 distributes weather forecasts such as the current temperature and weather, as well as future temperature and weather. The delivery navigation system 132 determines the luggage to be loaded onto each vehicle 10 and the transportation route based on the destination, weight, dimensions, etc. of the multiple luggage to be transported. The truck scale system 133 stores and manages the vehicle ID, vehicle weight, and load of each vehicle 10.

The vehicle 10 includes a secondary battery 11 as a power source, a data processing unit 12, a vehicle BMS (Battery Management System) 16, and an operation history recording unit 18. The vehicle BMS 16 detects the state of the secondary battery 11. The operation history recording unit 18 records the operation history of the vehicle 10. The operation history recording unit 18 may be disposed inside the vehicle BMS 16.

Figure 9 is a block diagram of the battery deterioration detection unit of the vehicle BMS 16 in the fifth embodiment. The vehicle BMS 16 is equipped with a SOH calculation unit 16a. The SOH calculation unit 16a calculates the deterioration level SOH based on the voltage, current, and temperature of the secondary battery 11. Note that the deterioration level SOH is a general term for the deterioration levels SOHQ, SOHR, etc. described above.

Returning to FIG. 8, the hardware configuration of the secondary battery management device 100E is the same as that of the secondary battery management device 100A of the first embodiment. The secondary battery management device 100E includes a battery operating range determination and selection unit 103, a delivery schedule vehicle condition input unit 104, a predicted power amount calculation unit 105 (power amount calculation unit), and a power amount database 142. The battery operating range determination and selection unit 103 also includes a predicted power amount correction unit 106 (power amount calculation unit), a running start state determination unit 114, a data analysis unit 109, and a battery deterioration database 144.

The scheduled delivery vehicle condition input unit 104 in the secondary battery management device 100E acquires the battery deterioration levels SOHQ, SOHR, cell temperature Tcell, and the current vehicle SOC from the vehicle BMS 16 via the data processing unit 12. The scheduled delivery vehicle condition input unit 104 also acquires the ambient temperature Ta, such as air temperature, and weather information from the weather forecast system 131, and acquires planned route information Rt of the planned route for shipping and delivery from a delivery navigation system 132 used by logistics companies, etc. Furthermore, the scheduled delivery vehicle condition input unit 104 acquires the vehicle ID, vehicle weight, and load in conjunction with each other from a truck scale system 133 owned by the logistics company.

The predicted power calculation unit 105 extracts the necessary reference conditions to refer to the power database 142 and calculates the predicted power amount W1. If there is a difference between the current driving conditions and the reference conditions for the predicted power amount W1, the battery operating range determination and selection unit 103 corrects the predicted power amount W1 in the predicted power amount correction unit 106 to obtain the predicted power amount W2. As described above, the predicted power amount W2 may be calculated based on, for example, "W2 = α x W1 + βd" or "W2 = α x W1 x βdd", or the predicted power amount W2 may be calculated by increasing or decreasing a fixed value for the predicted power amount W1. Below, with reference to FIG. 10, an example of calculating the predicted power amount W2 by correcting the predicted power amount W1 by a multiple will be described.

Figure 10 is a schematic diagram of the power amount correction data D30 included in the power amount database 142 of the fifth embodiment. Each row in Figure 10 is a record of the power amount correction data D30, and each record includes a route D31, power amount D32, winter coefficient D33, summer coefficient D34, and load coefficients D35 and D36 for loads of 50% and 80%. The power amount D32 is the amount of power required to travel each route D31 when an undegraded secondary battery 11 at 20°C is used and the load is 100%.

The winter coefficient D33 is a coefficient by which the amount of power D32 should be multiplied in winter, and the summer coefficient D34 is a coefficient by which the amount of power D32 should be multiplied in summer. The load coefficients D35 and D36 are coefficients by which the amount of power D32 should be multiplied when the load is 50% and 80%, respectively. When the vehicle 10 travels along route A and route B, the predicted amount of power W1 can be calculated as the sum of the amounts of power D32 of both routes, that is, "W1 = Wa1 + Wa2". When the planned travel conditions are summer, the amount of cargo is 50%, and an undegraded secondary battery 11 is used, the predicted amount of power W2 can be calculated by applying the respective summer coefficients D34 and load coefficients D35 to the predicted amount of power W1, and calculating "W2 = Wa1 x α1 x β1 + Wa2 x α1 x β2".

In FIG. 8, the predicted power amount correction unit 106 also references the battery deterioration database 144 to determine the predicted power amount W2. As in the first to fourth embodiments, the driving start state determination unit 114 calculates the battery's usable SOC range using the deterioration information of the secondary battery 11, the battery temperature, and the current SOC. That is, the driving start state determination unit 114 calculates the usable SOC range so that the predicted power amount W2 can be secured, the charging time is short, and deterioration of the secondary battery 11 during use is small.

Then, the driving start state determination unit 114 notifies the vehicle 10 and the charging device 200 (see FIG. 3) of the maximum value of the use SOC region as the driving start charging rate SOCs. The charging device 200 does not charge if the current SOC value exceeds the driving start charging rate SOCs, and charges up to the driving start charging rate SOCs if the current SOC is less than the driving start charging rate SOCs.

The vehicle 10's operation history recording unit 18 acquires and stores the driving date and time, driving time, location information, battery voltage, battery current, battery temperature, power usage, etc. in chronological order, and communicates the stored results to the secondary battery management device 100E via the data processing unit 12 by wireless or wired communication. The battery operating range determination and selection unit 103 then stores the communicated data. The data analysis unit 109 creates data for the power amount database 142 and adds it to the power amount database 142. The data analysis unit 109 also analyzes and extracts items to be stored in the battery deterioration database 144, such as the deterioration levels SOHQ, SOHR, and driving conditions, and adds the results to the battery deterioration database 144.

As a result, various data are further accumulated each time the vehicle 10 travels, making it possible to select a more appropriate predicted power amount W2. As described above, the vehicle can travel by charging only the amount of power corresponding to the required predicted power amount W2 into the secondary battery 11, and by using the degradation suppression area, efficient delivery and distribution is possible while suppressing deterioration of the secondary battery 11. Furthermore, efficient battery utilization is possible, the margin can be made smaller than with conventional on-board battery amounts, and a vehicle with an appropriate battery amount can be produced, thereby improving cost performance.

Sixth Embodiment
Next, a preferred sixth embodiment will be described. In the following description, the same reference numerals are used to designate parts corresponding to the parts of the other embodiments described above, and the description thereof may be omitted. Also, the configuration of the sixth embodiment is the same as that of the fifth embodiment (see FIG. 8), and therefore illustration thereof will be omitted.

Generally, when a battery is continuously charged and discharged at a large current, deterioration tends to accelerate. To prevent this, in this embodiment, the relationship between deterioration of the secondary battery 11 and the battery's downtime ratio is acquired in advance. Then, the battery deterioration database 144 stores information on the downtime Tr (not shown) during which the secondary battery 11 is not energized and the energization time Tc (not shown) during which the secondary battery 11 is energized. Using this accumulated information, it is preferable to create a route such that the locations with long downtime Tr are on the low SOC side in the latter half of the route. This makes it possible to prevent deterioration caused by the secondary battery 11 being maintained at a high SOC.

In addition, when the secondary battery 11 is generally charged quickly, the amount of heat generated increases due to the large current, causing the internal temperature to rise and the battery to deteriorate. Therefore, it is preferable to provide a certain amount of downtime during which the secondary battery 11 is not used before delivery after the rapid charge. The current duty of the secondary battery 11 can be calculated by "Rduty = tc/(tc + tr)". It is preferable to create a map of the current value and the threshold value Rdth of the current duty that accelerates deterioration so that the current duty is equal to or less than the threshold value Rdth that accelerates deterioration. Then, after setting the running start charging rate SOCs by referring to this map, the recommended delivery start time is displayed on a display (not shown), and the delivery can be scheduled to start after the end of the downtime after charging. In addition, if the charging time until the end of charging of the charge command to the charger is set to the total time from the time the charging completion voltage is reached through the downtime, and the downtime is included in the charging completion time, delivery can be started at the same time as the charging completion. By applying this embodiment, it is possible to further suppress battery deterioration and to efficiently deliver and distribute the goods.

As described above, the running start state determination unit 114 may store the relationship between the deterioration conditions of the secondary battery 11 and the optimal running start conditions (SOCs, Vs) as data in a map (table) format, and may determine the running start conditions (SOCs, Vs) by referring to this map-format data. Also, the relationship between the usage conditions of the secondary battery 11 and the optimal running start conditions (SOCs, Vs) may be stored in a map format. Furthermore, the relationship between the usage conditions of the battery, the amount of power, and the optimal running start conditions (SOCs, Vs) may be stored in a map format. In this way, by mapping and implementing the relationship between various parameters, it is possible to reduce the amount of data and the amount of calculation during implementation, and it is possible to perform calculations at high speed with a margin in the system storage. In other words, the running start charge rate SOCs can be calculated more quickly, and it is possible to build a system with an inexpensive device.

Furthermore, the battery operating range determination and selection unit 103 may update the power amount database 142 in real time. That is, by communication from the vehicle 10, the time series latitude, longitude, SOC of the secondary battery 11, battery voltage, battery current, power amount, battery temperature, and environmental temperature may be recorded together with the driver ID or vehicle ID. Then, by referring to the load weight of the vehicle 10 and the delivery completion record, a weight change correspondence database that describes the relationship between weight change and required power amount in the power amount database 142 may be constructed. From the created weight change correspondence database, data for learning may be extracted, and the extracted data may be subjected to supervised learning to output the predicted power amount W2. Alternatively, the predicted power amount W2 may be output by selecting a condition in which the Euclidean distance or Mahalanobis distance of the specified item is small from the weight change correspondence database.

Figure 11 is a schematic diagram of driving history data D40 included in the power amount database 142 of this embodiment. Each row in Figure 11 is a record of the driving history data D40, and each record includes a record number D42, a route condition parameter section D44, a deterioration information section D46, and a required power amount section D48.

The record number D42 is a number that identifies the record and is a unique number in the range of "1" to "N". The route condition parameter section D44 has m feature parameters k1 to km. Here, the feature parameters k1 to km include the driving date and time, vehicle ID, driver ID, temperature, delivery route, load, vehicle weight, and location information (area) in the record, and may also include battery temperature, weather, and delivery time zone information. The deterioration information section D46 includes the deterioration levels SOHQ and SOHR at the time the record was obtained. The required power section D48 is the power consumption amounts Wc1 to Wcn consumed by the vehicle 10 during the driving related to each record. These power consumption amounts Wc1 to Wcn become the objective variables when performing correlation analysis and learning processing.

In this embodiment, the secondary battery management device 100E calculates the predicted power amount W2 for the planned driving route based on this driving performance data D40. First, the delivery schedule vehicle condition input unit 104 receives planned delivery route information Rt, temperature, weather, vehicle ID, driver ID, load, vehicle weight, vehicle 10 position information, and the battery deterioration level of the secondary battery 11. For the items input to the delivery schedule vehicle condition input unit 104, the predicted power amount calculation unit 105 divides each value by the average value of the driving performance data D40, standardizes, and calculates the difference from the input conditions for each item. Furthermore, the difference is multiplied by the weighting coefficient for each item, added, and the square root is calculated. Specifically, the calculation shown in the following formula (1) is performed.

In formula (1), z1 is a value obtained by normalizing the value of the input item, and z is a value obtained by normalizing the value of the item in the driving performance data D40. Furthermore, d is the least square error. The predicted power amount calculation unit 105 searches for driving performance information for which this least square error d is closest to 0, i.e., the Euclidean distance is closest, and calculates the power in the searched driving performance information as the predicted power amount W1. Furthermore, the predicted power amount calculation unit 105 may output the predicted power amount W1 when the least square error d is less than a predetermined threshold value.

The predicted power amount correction unit 106 selects the information with the greatest distance between the obtained predicted power amount W1 and the selected driving performance information. The predicted power amount correction unit 106 then selects a coefficient from the power amount correction data D30 shown in FIG. 10 for the item related to the difference, and calculates the predicted power amount W2 by correcting the predicted power amount W1. The driving start state determination unit 114 also selects an SOC region in which the degree of deterioration estimated from the parameters when the battery is in use is small, and determines the driving start charging rate SOCs.

Furthermore, if the difference between the charging rate SOCs at the start of driving and the current SOC value is greater than the amount of power that can be charged at the threshold Ctime_th (not shown) that is the allowable charging time with a normal charging current, the battery operating range determination and selection unit 103 commands the charging device 200 (see FIG. 3) to perform rapid charging, thereby shortening the charging time. This allows the secondary battery management device 100E to establish a charging schedule that does not interfere with route driving, making it possible to deliver and distribute packages efficiently.

[Seventh embodiment]
12 is a block diagram of a secondary battery deterioration detection system S7 according to a preferred seventh embodiment. In the following description, the same reference numerals are used to designate parts corresponding to the parts of the other embodiments described above, and the description thereof may be omitted.
The secondary battery deterioration level detection system S7 includes a charging device 200 and a server machine 210. The vehicle 10 that is the subject of this embodiment includes, similarly to the vehicle 10 in the fifth embodiment (see FIG. 8 ), a secondary battery 11 and a vehicle BMS 16. This secondary battery 11 is the subject of deterioration detection.

The charging device 200 is configured to transmit information such as the state of charge (SOC), output current I, output voltage V, etc. obtained from the communication protocol in the charging cable during charging to the server machine 210 via wireless or wired communication.
The server machine 210 is a deterioration diagnosis device for the secondary battery 11, and its hardware configuration is similar to that of the secondary-battery management device 100A of the first embodiment (see FIG. 2).

In FIG. 12, the inside of the server machine 210 shows functions realized by application programs and the like as blocks. That is, the server machine 210 has a resistance/capacity calculation unit 212 and a deterioration detection unit 214. The resistance/capacity calculation unit 212 and the deterioration detection unit 214 cooperate with each other and are capable of sharing information. Note that the server machine 210 may be external to the charging device 200, or part or all of the server machine 210 may be built into the charging device 200.

When the vehicle 10 is connected to the charging device 200, the secondary battery 11 is charged up to the charging rate SOCs at the start of driving. At this time, the charging device 200 acquires information such as the SOC, output current I, and output voltage V of the secondary battery 11 from the vehicle 10. The charging device 200 transmits the acquired charging information to the resistance and capacity calculation unit 212. The resistance and capacity calculation unit 212 calculates the current capacity and resistance values of the secondary battery from the acquired information, and the deterioration detection unit 214 compares these with the initial values to calculate the deterioration levels SOHQ and SOHR.

The deterioration detection unit 214 can transmit information on the deterioration level to the terminal 220. Also, information on multiple vehicles 10 can be stored in a single server machine 210. This allows the actual deterioration database to be enriched. Then, by referencing the deterioration information of the same vehicle model from the database, it becomes possible to more accurately predict the deterioration tendency of the battery.

FIG. 13 is a flowchart of a deterioration detection routine executed by the server machine 210. As shown in FIG.
First, in step S31, the resistance/capacity calculation unit 212 acquires the voltage, current, and charging time from the charging device 200. Here, the "charging time" is the time elapsed since the start of charging. Next, in step S32, the resistance/capacity calculation unit 212 estimates the battery temperature with respect to the charging time based on the charging curve acquired in advance by learning.

Next, when the process proceeds to step S33, the resistance/capacity calculation unit 212 obtains the relationship between SOC and voltage as shown in graphs G11 and G12 in FIG. 14 (details will be described later) based on the obtained voltage value, charging time, current value, and charging rate SOC. Next, when the process proceeds to step S34, the resistance/capacity calculation unit 212 searches for the relationship between the SOC and the open circuit voltage OCV of this secondary battery 11 based on the database stored in the resistance/capacity calculation unit 212.

Next, when the process proceeds to step S35, the resistance/capacity calculation unit 212 calculates a provisional resistance value R0 by dividing the difference between the charging voltage V and the open circuit voltage OCV at the same SOC by the charging current I, as shown in graph G12 in FIG. 14. Here, the provisional resistance value R0 is a provisional value of the resistance value R, which is the internal resistance of the secondary battery 11.

Here, the resistance/capacity calculation unit 212 stores a database of correlations between SOC, battery temperature, and resistance value R. Next, when the process proceeds to step S36, the resistance/capacity calculation unit 212 corrects the provisional resistance value R0 based on this database of correlations and a temperature coefficient based on the battery temperature estimated in step S32, and calculates a provisional resistance value R1.

Next, when the process proceeds to step S37, the resistance/capacity calculation unit 212 calculates a provisional capacity value Q0 based on the charging current I and the charging time. Here, the provisional capacity value Q0 is a provisional value of the current capacity value Q of the secondary battery 11. The resistance/capacity calculation unit 212 also performs corrections to the provisional capacity value Q0 and the provisional resistance value R1 to calculate the capacity value Q and the resistance value R. Specifically, the capacity value Q and the resistance value R are obtained by converting them into values obtained under standard temperature, current and time conditions such as the measurement temperature in the catalog display specifications.

Next, when the process proceeds to step S38, the deterioration detection unit 214 obtains the ratio between the acquired capacity value Q and resistance value R and the capacity value Q and resistance value R of the secondary battery 11 when it is not in use, and calculates the deterioration levels SOHQ and SOHR from this. In other words, the server machine 210 outputs these deterioration levels SOHQ and SOHR as deterioration information of the secondary battery 11.

Here, the deterioration information of the secondary battery 11 will be further described.
The deterioration state of the secondary battery 11 is the SOHR calculated using the internal resistance (hereinafter also simply referred to as "resistance") of the battery and/or the SOHQ calculated using the full charge capacity of the battery. The SOHR is the degree of deterioration calculated based on the internal resistance of the battery, and represents the rate of increase in the internal resistance of the battery that increases with battery deterioration, and is defined by the following formula (2).

SOHR=100×R1(SOC,T)/R0(SOC,T)...(2)

In formula (2), R1(SOC, T) represents the current (deteriorated) internal resistance [Ω] of the secondary battery 11. R0(SOC, T) represents the internal resistance [Ω] of the secondary battery 11 when it is new.
The SOHQ calculated using the capacity is the current capacity divided by the initial capacity. Although the SOHR in the above formula (1) is expressed as a percentage, it is not necessarily limited to a percentage as long as it is the ratio of the current capacity to the initial capacity.
Here, an example of a method for calculating the deterioration degree diagnosis due to deterioration will be shown using graphs G11 and G12 in FIG.

FIG. 14 is a diagram showing the voltage characteristics of the secondary battery 11. As shown in FIG.
First, the graph G11 in FIG. 14 shows an example of the voltage-charging time characteristic during charging. In the graph G11, the horizontal axis is the elapsed time after the start of charging, and the vertical axis is the voltage of the secondary battery 11. The characteristic G110 in the figure is a voltage curve that can be regarded as an open circuit voltage OCV or a charging voltage at a low rate, and can be considered to be almost the OCV. The characteristic G112 shows the voltage change during rapid charging. The open circuit voltage (OCV) at the start of rapid charging is OCV _ini . The amount of electricity Qch is charged during the charging time until the elapsed time tend. In either characteristic, the open circuit voltage OCV increases as the charging time elapses. However, according to the rapid charging characteristic G112, the amount of electricity Qch equivalent to full charging is supplied to the secondary battery 11 before a relatively short elapsed time tend has elapsed.

Graph G12 in FIG. 14 shows the relationship between the SOC and the voltage of the secondary battery 11. In the graph G12, characteristic G120 shows the relationship between the SOC and the open circuit voltage OCV, and characteristic G122 shows the relationship between the SOC and the charging voltage Vc. G122 shows the voltage curve G112 of the graph G11 corresponding to the SOC. The open circuit voltage OCV _ini at the start of charging of G112 is converted to SOC from the relationship between SOC and OCV, and the corresponding SOC value is obtained as SOC _ini . The amount of electricity Qch of the graph G11 charged from the SOC _ini is converted to SOC to obtain SOCend. Here, the charging voltage Vc is the voltage that appears at the secondary battery 11 when a predetermined charging current is supplied to the secondary battery 11. As shown in the figure, for the same SOC value, the charging voltage Vc is higher than the open circuit voltage OCV. The difference between the two is called the voltage difference ΔV (socx). The voltage difference ΔV (socx) is mainly a voltage drop across the internal resistance of the secondary battery 11. When the SOC of the secondary battery 11 is charged from SOCini to SOCend, the charged amount of electricity Qch is equal to the amount of electricity Qch shown in graph G11.

The resistance/capacity calculation unit 212 (see FIG. 13) can obtain the specifications of the secondary battery 11 applied to the vehicle 10 from a database inside the resistance/capacity calculation unit 212 based on information about the vehicle 10. The specifications include an OCV curve corresponding to the characteristic G120. Therefore, the resistance/capacity calculation unit 212 obtains SOCini, which is the SOC at the start of charging, based on the open circuit voltage OCV at the start of charging and the characteristic G120.

Furthermore, the resistance/capacity calculation unit 212 measures the charging voltage Vc and the open circuit voltage OCV at the same SOC, and calculates the voltage difference ΔV (socx) based on both. Furthermore, the resistance/capacity calculation unit 212 divides this voltage difference ΔV (socx) by the charging current I to obtain the resistance value R of the internal resistance of the secondary battery 11. In particular, it is preferable to obtain the resistance value R immediately after current is applied to the secondary battery 11, before the SOC fluctuates and because there is little effect from reaction resistance. When the secondary battery 11 is a lithium ion battery, it is known that the resistance value R calculated as described above depends on the SOC.

Therefore, the relationship between the resistance value R and the SOC can be expressed as a function or a table.
For example, when the secondary battery 11 is charged from a certain SOCa (not shown) to another SOCb (not shown), a correction calculation may be performed by subtracting the respective resistance values R. If the voltage and current at the end of charging and discharging the secondary battery 11 are Vlast and Ilast, respectively, and the open circuit voltage of the battery detected a predetermined time after the end of charging and discharging is OCV, the resistance value R in the resistance curve can be calculated by the following formula (3).

R=(|OCV-Vlast|)/Ilast...(3)

Furthermore, when the charging current I is constant, and the closed circuit voltage of the battery is CCV, the resistance value R can also be calculated by the following equation (4):

R=(OCV-CCV)/I...(4)

The resistance value R can also be calculated by other methods. For example, a linear approximation equation "CCV = Hx + J" can be calculated based on the current value x at a certain SOC and the closed circuit voltage CCV. The resistance value R can be calculated based on the coefficient H in this approximation equation. Here, if the constant J is equal to the open circuit voltage OCV, the coefficient H can be considered to be equal to the resistance value R.

In addition, the capacitance value Q can be calculated based on the difference between SOCini, which is the SOC at the start of charging, and SOCend, which is the SOC at the end of charging, i.e., the fluctuation range of the SOC, and the amount of electricity Qch, using the following equation (5).

Q=Qch/(SOCend-SOCini)×100...(5)

Eighth embodiment
15 is an example of a vehicle battery system according to a preferred embodiment 8. In the following description, parts corresponding to those in the other embodiments described above are given the same reference numerals, and the description thereof may be omitted.
The vehicle battery system S8 is implemented inside the vehicle 10, and includes a measurement value detection unit 22, a battery control unit 24, a load control unit 26, and a higher-level control unit 28.

The secondary battery 11 is configured by connecting a plurality of single cells 11a, 11b, etc., each having a positive electrode and a negative electrode, in series. The secondary battery 11 is connected to a load device (not shown) and supplies power to the load device. The measurement value detection unit 22 detects various information related to the state of the secondary battery 11, such as the total current, total voltage, environmental temperature, maximum temperature, average temperature, and minimum temperature of the secondary battery 11, and the temperature and voltage of each of the single cells 11a, 11b. The data detected by the measurement value detection unit 22 is input to the battery control unit 24. It is preferable that the measurement value detection unit 22 also detects the type or model of each of the single cells 11a, 11b, in other words, the individual characteristics of the single cells 11a, 11b.

The battery control unit 24 calculates the current state of charge (SOC, etc.) of the secondary battery 11 based on the data input from the measurement value detection unit 22, and performs processes such as detecting abnormal states, calculating the power that can be input and output, and generating a temperature control command. Each signal output from the battery control unit 24 is supplied to the load control unit 26 and the upper control unit 28. The upper control unit 28 outputs a control command to the load control unit 26 based on the supplied signal. This control command specifies, for example, the maximum power or maximum amount of power that can be consumed by the load device. The load control unit 26 controls the load device based on the control commands input from the upper control unit 28 and the information input from the battery control unit 24.

[Ninth embodiment]
16 is a block diagram of a secondary battery management system S9 according to a preferred ninth embodiment. In the following description, the same reference numerals are used to designate parts corresponding to the parts of the other embodiments described above, and the description thereof may be omitted.
The secondary battery management system S9 includes a detection unit 30 and a battery operating range determination and selection unit 40.

The hardware configuration of the battery operating range determination and selection unit 40 is similar to that of the secondary battery management device 100A (see FIG. 2) of the first embodiment. In FIG. 16, the inside of the battery operating range determination and selection unit 40 is shown as a block, with functions realized by application programs and the like.
That is, the battery operating range determination and selection unit 40 includes a data accumulation unit 41 , a data selection unit 42 , a deterioration calculation unit 43 , a driving parameter calculation unit 45 , a deterioration prediction unit 46 , and a driving start state determination unit 114 .

The detection unit 30 detects data such as the voltage, current, and temperature of the secondary battery 11 (see FIG. 15) together with time information such as date and time, and outputs the detection result to the battery operating range determination and selection unit 40. At this time, the SOC information notified to the charging device 200 (see FIG. 12) and calculated is also input to the battery operating range determination and selection unit 40. The function of the running start state determination unit 114 is the same as that of the fifth embodiment (see FIG. 8). The data selection unit 42 selects data (including data related to battery temperature and running history) acquired by the detection unit 30 or the measurement value detection unit 22 (see FIG. 15) in the vehicle 10 (see FIG. 3) and data output from the detection unit 30 that meets preset conditions, and outputs the data to the data storage unit 41.

The battery temperature and driving history may be obtained by acquiring the measured values from the measurement value detection unit 22 shown in FIG. 15 via an OBD (On-board diagnostics) terminal (not shown) or any other terminal. The data storage unit 41 includes an SOC-OCV data storage unit 41a, an SOC-R data storage unit 41b, an operating parameter data storage unit 41c, and a database 44.

The data selection unit 42 acquires the open circuit voltage of the secondary battery 11 (see FIG. 15) for an SOC that meets the conditions at regular intervals or regular counts, and stores the voltage in the SOC-OCV data storage unit 41a. Similarly, the data selection unit 42 acquires data on the resistance value of the secondary battery 11 for that SOC, and stores the data in the SOC-R data storage unit 41b. Furthermore, the data selection unit 42 acquires and stores data on the current, current flow time, SOC fluctuations, etc. for a short period of time, for example, several minutes, as usage history data corresponding to the usage time of the secondary battery 11, based on the data output from the measurement value detection unit 22 (see FIG. 15), in the operating parameter data storage unit 41c.

The database 44 contains various data used for estimating deterioration. These include, for example, a prediction formula for estimating deterioration, initial parameter values, and parameter change amounts. The deterioration calculation unit 43 uses data from the database 44, the SOC-OCV data accumulation unit 41a, and the SOC-R data accumulation unit 41b to calculate the capacity and resistance values, which are the internal state of the secondary battery 11, and calculates the deterioration state of the secondary battery 11. Here, the SOC-OCV data accumulation unit 41a and the SOC-R data accumulation unit 41b also accumulate initial data for the secondary battery 11 in advance.

The deterioration calculation unit 43 estimates the current deterioration state of the secondary battery 11 and outputs the result to the upper control unit 28 (see FIG. 15) and the deterioration prediction unit 46. The deterioration prediction unit 46 performs a time-series deterioration prediction using the parameters calculated by the operating parameter calculation unit 45 and outputs the result as deterioration prediction data. The running start state determination unit 114 determines the usable SOC region and outputs the running start condition (SOCs, Vs).

Here, the operation of the operating parameter calculation unit 45 will be described in detail.
The operating parameter calculation unit 45 calculates various operating parameters related to the operating conditions of the secondary battery 11 from the data selected by the data selection unit 42, based on the usage history data accumulated in the operating parameter data accumulation unit 41c in the data accumulation unit 41. The operating parameters of the secondary battery 11 include, for example, the amount of power, the downtime ratio, the travel distance, the operating center SOC during operation, the operating voltage range, the upper operating voltage limit, the lower operating voltage limit, the average current, the maximum and minimum currents, the battery temperature, the environmental temperature, the heat generation factor, the effective current, the operating average amount of electricity, the charge capacity, the discharge capacity, the SOC fluctuation range, the conductive ratio per unit time, and the downtime ratio per unit time.

Note that the operating parameters are not limited to these. The operating parameter calculation unit 45 can use parameters that can be derived from various operating conditions as operating parameters for the secondary battery 11. Furthermore, the data selection unit 42 selects data within a predetermined range detected by the measurement value detection unit 22 as data to be stored in the data storage unit 41 and used to calculate parameters related to the use of the secondary battery 11.

Vehicles such as electric vehicles require input and output from a battery when accelerating or decelerating while traveling. The battery receives regenerative current during deceleration and discharges when traveling or stopped. Fig. 17 is a diagram showing an example of time-dependent changes in the battery voltage V, charge/discharge current I, and SOC of the secondary battery 11 detected by the vehicle.
Graph G21 indicates the battery voltage V of the secondary battery 11, graph G22 indicates the charge/discharge current I, and graph G23 indicates the change in SOC. With regard to the charge/discharge current I, a positive value is the charge current, and a negative value is the discharge current. Hereinafter, an example of data selection performed by the data selection unit 42 will be described with reference to FIG.

In a vehicle system, when a battery is used as a power source for driving, the detected current value may not completely match zero due to sensor errors and current supply to auxiliary equipment and control circuits even if there is a downtime. A section within a certain current width that can be considered as the downtime of the battery is considered to be the downtime, and the current of this certain current width is hereinafter described as a current value of "0". In the period from time t ₀ to t ₀₁ in FIG. 17, a discharge current flows to the secondary battery 11, and at time t ₀₁ , the charge/discharge current I becomes "0". Then, at time t _c1 , a discharge current starts to flow again to the secondary battery 11. In this way, the secondary battery 11 is intermittently discharged during the period from time t ₀ to t _ch . Furthermore, during the period from time t _ch to t ₀₅ , the secondary battery 11 is charged and the voltage rises.

Then, from time t ₀₅ to t _c , there is a period during which the charge/discharge current I is zero (excluding the measurement error). This period is longer than the predetermined pause threshold period Tr _th . In this way, when the time elapsed since the end of energization exceeds the pause threshold period Tr _th , the data selection unit 42 records the battery voltage V (V ^tp1 ) at the timing immediately before energization starts again (immediately before time t _c in the illustrated example) as the open circuit voltage OCV. Furthermore, the data selection unit 42 also records the SOC (SOC ^tp1 ) at that timing.

Similarly, the charge/discharge current I is zero from time t ₀₆ to time t _cp , and this period is longer than the pause threshold period Tr _th . Therefore, the data selection unit 42 records the battery voltage V (V ^tp2 ) at the timing immediately before the current flow starts again as the open circuit voltage OCV, and also records the SOC (SOC ^tp2 ) at the same timing. In this way, by accumulating the relationship between the SOC and the open circuit voltage OCV, the SOC-OCV characteristic can be approximately determined.

The data selection unit 42 also detects the timing at which the absolute value |I| of the charge/discharge current I changes sharply (within a predetermined time) from 0 to a predetermined current threshold value _Ij or more. In the example of FIG. 17, the time t _cn corresponds to this timing. The data selection unit 42 records the battery voltage V _cn , the state of charge SOC _cn , and the charge/discharge current I _cn at the detected timing (time t _cn ). Furthermore, the data selection unit 42 records the battery voltage V _{cn -} 1 (not shown) and the state of charge SOC _cn-1 (not shown) at time t cn-1 (not shown) immediately before the secondary battery 11 enters a closed circuit state.

The data selection unit 42 then calculates the resistance value R of the secondary battery 11 by first-order approximation based on the battery voltages _Vcn , Vcn _-1 , the states of charge _SOCcn , SOCcn _-1 , and the charge/discharge current _Icn . The data selection unit 42 also detects the current flow end time at which the absolute value |I| of the charge/discharge current I changes to 0. In the illustrated example, this corresponds to times _t01 , _t02 , _t03 , _t04 , _t05 , etc. These are hereinafter collectively referred to as current flow end time _t0x .

The data selection unit 42 also identifies the time when current supply to the secondary battery 11 starts after the current supply end time t _0x . In the illustrated example, these times correspond to t _c1 , t _c2 , t _c3 , etc. These are hereinafter collectively referred to as current supply start time t _cx . The data selection unit 42 records the battery voltage V and SOC at the current supply end time t _0x and the current supply start time t _cx as the voltage and SOC before and after charging and discharging the secondary battery 11, respectively.

For example, the data selection unit 42 records the SOC at times _t01 , _t02 , and _t03 as states of charge SOCa, SOCb, and SOCc, and records the battery voltages _V01 , _V02 (not shown), and _V03 (not shown) at the same times as open circuit voltages. The data selection unit 42 also records the state of charge _SOC , charge/discharge current I, and battery voltages _Vc1 , Vc2 (not shown), and _Vc3 (not shown) at times tc1, _tc2 , _{and tc3} , for example.

The data selection unit 42 also records the time from a certain current flow end time t _0x to the immediately following current flow start time t _cx (e.g., time t ₀₁ to time t _c1 ) as a down time Tr (not shown), and records the time from a certain current flow start time t _cx to the immediately following current flow end time t _0x (e.g., time t _c1 to time t ₀₂ ) as a current flow time Tc (not shown).The data selection unit 42 then calculates an integrated value of the charge/discharge current I during each current flow time Tc, and records time-series data of the capacitance value Q (not shown) based on this.

The data selection unit 42 also calculates the input and output power based on the battery voltage V and the charge/discharge current I during each current-flow time Tc, and calculates and records the amount of input and output power by integrating the input and output power. Furthermore, the data selection unit 42 determines the total value ΣTr of the downtime Tr within a certain period and the total value ΣTc of the current-flow time Tc, and extracts ΣTc/(ΣTc+ΣTr) as the current-flow ratio.

The data selection unit 42 also integrates the charge/discharge current I for each current polarity over a certain period of time, and divides the result by the current flow time to calculate the average charge current and average discharge current, which are then recorded together with the battery temperature. The data selection unit 42 also records the amount of electricity that changes during current flow and the current flow time based on the charge/discharge current I accumulated as described above.

16, the operating parameter calculation unit 45 can analyze a wide variety of operating parameters. These parameters are not specifically illustrated, but include, for example, the start voltage Vini, the end voltage Vlast, the maximum voltage Vmax, the minimum voltage Vmin, the cell temperature Tcell, the environmental temperature Ta, the charge capacity Qc, the discharge capacity Qd, the fluctuation in the amount of electricity ΔQ per unit time, the maximum current Imax, the minimum current Imin, the average current Iave, the effective current Ie, the operating center voltage Vcenter, the residence time ratio Ratet in each voltage region, the conductive polarity ratio tp per unit time, the current-carrying time ratio ts, the rest time ratio tr, the upper and lower SOC limits (SOCmax, SOCmin), the upper and lower voltage limits (Vmax, Vmin), the SOC fluctuation range ΔSOC, and the current-carrying time ttotal. The conductive polarity ratio tp is an index indicating how many times the polarity of charging and discharging has changed within a certain current-carrying time.

As shown in FIG. 17, in the secondary battery 11, the SOC also fluctuates in response to fluctuations in the charge/discharge current I. The maximum value of the SOC at this time is taken as the maximum SOC, the minimum value of the SOC is taken as the minimum SOC, and the SOC fluctuation range ΔSOC is calculated from the difference between the two. The data selection unit 42 extracts, for example, the amount of electricity ΔQc of the portion where charging is continuing and the amount of electricity ΔQd of the portion where discharging is continuing from the current waveform. The data selection unit 42 then calculates the amount of electricity fluctuation ΔQ per unit time by integrating these values within a unit time and dividing by the average value.

In addition, the data selection unit 42 can analyze the operating parameters using the heat generation factor integrated value ^I2t defined as an index of heat generation, the amount of change in battery temperature ΔT, etc. The number of data used in the analysis of the operating parameters can be determined arbitrarily.

The operating parameter calculation unit 45 also analyzes operating parameters for several hours to several tens of days, corresponding to the accumulation period of data accumulated in the data accumulation unit 41, and approximates these parameters to a pattern corresponding to the operating state. Furthermore, the calculated deterioration state of the secondary battery 11 and the operating parameters at that time may be accumulated in chronological order as a history set, and the conditions and changes in capacity and resistance when the operating method is changed may be recorded. This has the effect of making it easy to discover abnormalities in the same secondary battery 11.

There may also be cases where temperature data during use of the secondary battery 11 is not output via an OBD terminal (not shown) of the vehicle 10. In such cases, it is advisable to record the air temperature and battery temperature while the vehicle is running and when the vehicle is stopped in the terminal 220 (see FIG. 12 ) and use the recorded data as the temperature history of the secondary battery 11.
In this way, according to this embodiment, it is possible to appropriately detect information relating to the weight of the vehicle 10 and the deterioration state of the secondary battery 11, and obtain the capacity, resistance, etc. of the secondary battery 11, and by using highly accurate deterioration prediction information, it is possible to specify appropriate driving start conditions (SOCs, Vs).

[Tenth embodiment]
18 is a block diagram of a secondary battery management system S10 according to a preferred embodiment 10. In the following description, parts corresponding to the parts of the other embodiments described above are given the same reference numerals, and the description thereof may be omitted.
The secondary battery management system S10 includes a charging device 200, a secondary battery management device 100F (computer), an electric energy database 142, and a battery deterioration database 144.

The hardware configuration of the secondary battery management device 100F is similar to that of the secondary battery management device 100D of the fourth embodiment (see FIG. 6). Like the secondary battery management device 100D, the secondary battery management device 100F includes an electric energy calculation unit 112, a running start state determination unit 114, a duty ratio determination unit 116, a database update unit 118, an analysis unit 120, and a charge control unit 124. These functions are also similar to those of the fourth embodiment.
According to this embodiment, the charging control unit 124 in the secondary-battery management device 100F controls the charging device 200 based on the duty ratio Dtr, so that the configuration of each vehicle 10 can be simplified.

[Effects of the embodiment]
As described above, the secondary battery management devices 100A, 100B, 100D, 100E, and 100F according to the preferred embodiments include an electric energy calculation unit (105, 106, 112) that calculates a predicted amount of electric energy W2 that is predicted to be required for a moving body (10) to move along a designated planned moving route (Rt) based on an electric energy database 142 that stores a required amount of electric energy corresponding to the moving date and time or outside air temperature during moving of the moving body (10) driven by the secondary battery 11, and a state of deterioration (SOHQ, SOHR) of the secondary battery 11 or a secondary battery 12 that stores the state of deterioration (SOHQ, SOHR) of the secondary battery 11. and a running start state determination unit 114 that refers to a battery degradation database 144 that records the past usage states of the secondary battery 11 together with data showing the correlation between the usage state and the degradation state (SOHQ, SOHR) of the secondary battery 11, calculates a usage SOC region or usage voltage region of the secondary battery 11 that can secure a predicted power amount W2 according to the degradation state (SOHQ, SOHR), and determines a running start condition (SOCs, Vs) that is the voltage or SOC of the secondary battery 11 in a charged state based on the calculated usage SOC region or usage voltage region. This makes it possible to appropriately determine the charging state of the secondary battery 11 based on the degradation state (SOHQ, SOHR) of the secondary battery 11.

The power database 142 also stores the required power amount corresponding to the weight of the moving body (10) in addition to the travel date and time or the outside air temperature during travel and the travel route, and it is more preferable that the power amount calculation unit (105, 106, 112) calculates the predicted power amount W2 based on the weight of the moving body (10) and the planned travel route (Rt). This makes it possible to more appropriately determine the charge state of the secondary battery 11 in accordance with various planned travel routes (Rt) of moving bodies (10) having various weights.

The secondary battery 11 can be charged while alternately repeating a powered state and a resting state, and it is more preferable to further include a duty ratio determination unit 116 that determines the duty ratio based on the battery deterioration database 144 so that the duty ratio of the powered state is lowered as the deterioration of the secondary battery 11 progresses. This allows the duty ratio determination unit 116 to determine an appropriate duty ratio according to the deterioration state of the secondary battery 11.

More preferably, the driving start state determination unit 114 stores the relationship between the deterioration state (SOHQ, SOHR) and the driving start condition (SOCs, Vs) as map-format data, and determines the driving start condition (SOCs, Vs) by referring to the map-format data. This makes it possible to reduce the amount of data and the amount of calculations during implementation, and allows for high-speed calculations with ample system storage.

The power database 142 stores the required power amount corresponding to the travel date and time or the outside temperature during travel, the travel route, the weight of the mobile body, and the driver identification information that identifies the driver of the mobile body, and the travel start state determination unit 114 determines the travel start conditions using artificial intelligence that has performed learning processing through supervised learning, and it is even more preferable to further include a database update unit 118 that updates the power database 142 and the battery deterioration database 144 in real time according to the travel status of the mobile body. This allows the power database 142 and the battery deterioration database 144 to be updated in real time according to the actual operating status of the mobile body (10).

Moreover, it is more preferable that the driving start state determination unit 114 has a function of receiving the assumed temperature Tass of the secondary battery 11, and a function of determining the driving start conditions (SOCs, Vs) such that the higher the received assumed temperature Tass, the lower the staying SOC, which is the SOC at which the secondary battery 11 stays for a predetermined time or more. This makes it possible to determine appropriate driving start conditions (SOCs, Vs) corresponding to the assumed temperature Tass.

Moreover, like the secondary battery management devices 100B and 100D, it is even more preferable to further include a charging control unit 124 that controls the charging device 200 that charges the secondary battery 11 based on the driving start conditions (SOCs, Vs) supplied from the driving start state determination unit 114 and the duty ratio Dtr determined by the duty ratio determination unit 116. This allows the secondary battery 11 to be charged with an appropriate duty ratio Dtr.

Moreover, like the secondary battery management device 100D, it is even more preferable to further include a charging schedule planning unit 122 that communicates with an operation schedule planning unit 146 that plans an operation schedule for the mobile body (10) and plans a charging schedule for the mobile body. This makes it possible to plan a charging schedule that is compatible with the operation schedule of the mobile body (10).

[Modification]
The present invention is not limited to the above-mentioned embodiment, and various modifications are possible. The above-mentioned embodiment is exemplified to explain the present invention in an easy-to-understand manner, and is not necessarily limited to those having all the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, it is possible to delete a part of the configuration of each embodiment, or to add or replace other configurations. In addition, the control lines and information lines shown in the figure show those that are considered necessary for explanation, and do not necessarily show all the control lines and information lines necessary on the product. In reality, it may be considered that almost all the configurations are connected to each other. Possible modifications of the above-mentioned embodiment are, for example, as follows.

(1) The vehicle 10 described above can be applied not only to delivery trucks, but also to electric buses with fixed routes, road battery vehicles, etc., and makes it possible to effectively utilize secondary batteries by optimizing battery capacity while suppressing deterioration and balancing usage period and cost. Furthermore, the mobile body in the present invention is not limited to the vehicle 10, but also includes running robots, flying robots, flying objects, ships, etc. that use secondary batteries as a power source.

(2) The hardware of the secondary battery management devices 100A, 100B, 100D, and 100E in the above embodiments can be realized by a general computer, so the flowcharts shown in Figures 7 and 13 and other programs that execute the various processes described above may be stored on a storage medium or distributed via a transmission path.

(3) In the above embodiment, the processes shown in Figures 7 and 13 and the other processes described above are described as software processes using a program, but some or all of them may be replaced with hardware processes using an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array), etc.

(4) The various processes performed in the above embodiment may be executed by a server computer via a network (not shown), and the various data stored in the above embodiment may also be stored in the server computer.

10. Vehicles (moving objects)
11 Secondary batteries 100A, 100B, 100D, 100E Secondary battery management device (computer)
105 predicted power amount calculation unit (power amount calculation unit)
106 predicted power amount correction unit (power amount calculation unit)
112 Electric energy calculation unit (electric energy calculation means, electric energy calculation process)
114: Running start state determination unit (charging condition determination means, charging condition determination process)
116 Duty ratio determination unit 118 Database update unit 122 Charging schedule planning unit 124 Charging control unit 142 Power amount database 144 Battery deterioration database 146 Operation schedule planning unit 200 Charging device Rt Planned route information (planned travel route)
Vs Running start voltage (running start condition)
Dtr Duty ratio SOCs Charge rate at start of running (start condition of running)
Tass Estimated temperature W2 Estimated power consumption SOHQ, SOHR Degree of deterioration (deterioration state)

Claims (10)

an electric power calculation unit that calculates a predicted amount of electric power required for a moving body driven by a secondary battery to move along a designated planned movement route based on an electric power database that stores a required electric power amount corresponding to a movement date and time or an outside temperature during movement and a movement route of the moving body;
a battery degradation database that records the past usage states of the secondary battery along with data indicating the correlation between the usage state and degradation state of the secondary battery, calculates a usage SOC region or usage voltage region of the secondary battery in which the predicted amount of power can be secured according to the degradation state of the secondary battery, and determines a running start condition, which is the voltage or SOC of the secondary battery in a fully charged state, based on the calculated usage SOC region or usage voltage region. the power amount database stores the required power amount corresponding to the travel date and time or the outside air temperature during the travel, the travel route, and a weight of the moving object;
The secondary battery management device according to claim 1 , wherein the power amount calculation unit calculates the predicted power amount based on a weight of the moving object and the planned moving route. The secondary battery is capable of being charged while alternately repeating a power-on state and a power-off state,
The secondary battery management device according to claim 2, characterized in that the rest time after charging is controlled by a duty ratio determination unit that uses information on battery deterioration and current conduction ratio based on the battery deterioration database and determines the ratio of the current conduction time until charging is completed to the rest time after current conduction. The secondary battery management device according to any one of claims 1 to 3 , characterized in that the driving start state determination unit stores the relationship between the usage conditions and the driving start conditions as map-format data, and determines the driving start conditions by referring to the map-format data. the power amount database stores the required power amount in association with the travel date and time or the outside air temperature during travel, the travel route, the weight of the mobile body, and driver identification information that identifies a driver of the mobile body;
The running start condition determination unit determines the running start condition by an artificial intelligence that has undergone a learning process by supervised learning,
4. The secondary battery management device according to claim 3, further comprising a database update unit that updates the power amount database and the battery deterioration database in real time according to a movement state of the mobile object. The secondary battery management device according to claim 1, characterized in that the driving start state determination unit has a function of receiving an estimated temperature of the secondary battery, and a function of determining the driving start condition so that the higher the received estimated temperature, the lower the staying SOC, which is the SOC at which the secondary battery stays for a predetermined time or more. The secondary battery management device according to claim 3, further comprising a charging control unit that controls a charging device that charges the secondary battery based on the driving start condition supplied from the driving start state determination unit and the ratio determined by the duty ratio determination unit. The secondary battery management device according to claim 3 , further comprising a charging schedule planning unit that communicates with a driving schedule planning unit that plans a driving schedule for the mobile body and that plans a charging schedule for the mobile body. an energy calculation step of calculating a predicted energy required for a moving body driven by a secondary battery to move along a designated planned moving route based on an energy database storing a required energy amount corresponding to a moving date and time or an outside air temperature during the moving and the moving route;
a charging condition determination process for recording the deterioration state of the secondary battery, or referring to a battery deterioration database in which the past usage states of the secondary battery are recorded together with data showing a correlation between the usage state of the secondary battery and the deterioration state, calculating a usage SOC region or usage voltage region of the secondary battery in which the predicted amount of power can be secured according to the deterioration state, and determining a driving start condition, which is the voltage or SOC of the secondary battery in a fully charged state, based on the calculated usage SOC region or usage voltage region. Computer,
an electric power calculation means for calculating a predicted amount of electric power required for a moving body driven by a secondary battery to move along a designated planned moving route, based on an electric power database storing a required electric power amount corresponding to a moving date and time or an outside temperature during the moving and a moving route;
a charging condition determination means for recording a degradation state of the secondary battery, or for referring to a battery degradation database in which a past usage state of the secondary battery is recorded together with data showing a correlation between a usage state of the secondary battery and the degradation state, calculating a usage SOC region or usage voltage region of the secondary battery in which the predicted amount of power can be secured according to the degradation state, and determining a running start condition, which is the voltage or SOC of the secondary battery in a charging complete state, based on the calculated usage SOC region or usage voltage region;
A program to function as a

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