JP7672282B2 - Battery management system, battery management method, and battery management program - Google Patents

Description

One aspect of the present disclosure relates to a battery management system, a battery management method, and a battery management program.

Patent Document 1 describes a status monitoring system for lead-acid batteries. This system includes a device for measuring the internal resistance of the lead-acid battery, a device for calculating the average value of the internal resistance for each fixed period and for calculating the rate of change between the average values by comparing this average value of the internal resistance for each fixed period with the average value for the fixed period immediately preceding it, and a device for issuing an alarm or displaying the time to replace the lead-acid battery if the rate of change exceeds a predetermined value.

Patent No. 4353653

An effective indicator for predicting the lifespan of storage batteries is needed.

A battery management system according to one aspect of the present disclosure includes an acquisition unit that acquires reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period after the reference period, a characteristic calculation unit that calculates a characteristic value corresponding to the charge state of the storage battery during the reference period as a reference characteristic value based on the reference data and calculates a characteristic value corresponding to the charge state of the storage battery during the target period as a target characteristic value based on the target data, and a ratio calculation unit that calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

A battery management method according to one aspect of the present disclosure is executed by a battery management system including at least one processor. This battery management method includes the steps of acquiring reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period after the reference period, calculating a characteristic value corresponding to the charge state of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the charge state of the storage battery during the target period as a target characteristic value based on the target data, and calculating a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

A battery management program according to one aspect of the present disclosure causes a computer to execute the steps of acquiring reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period following the reference period, calculating a characteristic value corresponding to the charge state of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the charge state of the storage battery during the target period as a target characteristic value based on the target data, and calculating a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

In this aspect, the degree of change in the characteristic value corresponding to the battery's state of charge as the base period progresses to the target period is obtained as a reference value. This reference value makes it possible to predict how the characteristics of the battery will change further into the future. Therefore, the reference value can be said to be an effective indicator for predicting the lifespan of the battery.

According to one aspect of the present disclosure, it is possible to obtain an index that is effective for predicting the lifespan of a storage battery.

FIG. 2 is a diagram illustrating an example of a functional configuration of a battery management system according to an embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of a computer that configures a battery management system according to an embodiment. 5 is a flowchart illustrating an example of a process performed by a battery management system according to the embodiment. FIG. 13 is a diagram showing an example of a graph relating to a reference characteristic value and a target characteristic value.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the description of the drawings, identical or equivalent elements are given the same reference numerals, and duplicate descriptions will be omitted.

[System Configuration]
The battery management system 1 according to the embodiment is a computer system that calculates a reference value for predicting the life of a storage battery (secondary battery) mounted on an electric vehicle. The reference value can be used as an effective index for predicting the life of the storage battery. Examples of types of storage batteries include, but are not limited to, lead-acid batteries and lithium-ion batteries. The storage battery may be a battery pack composed of a plurality of single cells of the same type. An electric vehicle refers to a vehicle that runs using all or part of the electric energy stored in a storage battery as its motive power. The electric vehicle may be a vehicle for carrying people or a vehicle for moving luggage. The electric vehicle may be a cargo handling vehicle for moving luggage, for example, a forklift. In one example, the battery management system 1 may calculate a reference value for predicting the life of a lead-acid battery mounted on a cargo handling vehicle.

Figure 1 is a diagram showing an example of the functional configuration of the battery management system 1. In one example, the battery management system 1 includes a server 10. The server 10 can access a database 20 that stores storage battery data indicating the state of storage batteries mounted on an electric vehicle 2 via a communication network. The database 20 stores storage battery data for at least one electric vehicle 2. The database 20 may be a component of the battery management system 1, or may be provided in a computer system separate from the battery management system 1. The communication network used for the battery management system 1 is, for example, composed of at least one of the Internet and an intranet.

Each electric vehicle 2 provides storage battery data to the database 20. The electric vehicle 2 includes a battery management unit (BMU) 3 that monitors or controls the storage battery. The BMU 3 repeatedly measures the state of the storage battery at a given time interval and generates storage battery data indicating the state. The BMU 3 then transmits the storage battery data to the database 20 via a communication network at a given timing. The storage battery data is time-series data indicating the state of the storage battery. For example, each record of the storage battery data includes a measurement date and time and at least one physical quantity indicating the state of the storage battery. Examples of the physical quantity include, but are not limited to, a measured voltage, a measured current, and a measured temperature. The storage battery data indicates a physical quantity measured, for example, every 100 milliseconds. In the database 20, the storage battery data is associated with at least one of a storage battery ID and an electric vehicle ID. The storage battery ID is an identifier that uniquely identifies the storage battery. The electric vehicle ID is an identifier that uniquely identifies the electric vehicle 2.

The server 10 is a computer that calculates a reference value based on storage battery data. The server 10 has the following functional modules: an acquisition unit 11, a calculation unit 12, and an output unit 13. The acquisition unit 11 is a functional module that acquires storage battery data from the database 20. The calculation unit 12 is a functional module that calculates a reference value based on the storage battery data. The output unit 13 is a functional module that outputs the reference value.

Figure 2 is a diagram showing an example of a general hardware configuration of a computer 100 constituting the server 10. For example, the computer 100 includes a processor (e.g., a CPU) 101 that executes an operating system, application programs, etc., a main memory unit 102 consisting of ROM and RAM, an auxiliary memory unit 103 consisting of a storage device such as a hard disk or flash memory, a communication control unit 104 consisting of a network card or wireless communication module, an input device 105 such as a keyboard or mouse, and an output device 106 such as a monitor.

Each functional module of the server 10 is realized by loading a predetermined program onto the processor 101 or the main memory unit 102 and having the processor 101 execute the program. In accordance with the program, the processor 101 operates the communication control unit 104, the input device 105, or the output device 106, and reads and writes data in the main memory unit 102 or the auxiliary memory unit 103. Data or databases required for processing are stored in the main memory unit 102 or the auxiliary memory unit 103.

The server 10 is composed of at least one computer. When multiple computers are used, a single server 10 is logically constructed by connecting these computers via a communication network such as the Internet or an intranet.

[Reference value calculation theory]
In one example, the calculation unit 12 obtains characteristic values corresponding to the state of charge (SOC) of the storage battery for each of a reference period and a target period following the reference period. Thus, the calculation unit 12 functions as a characteristic calculation unit. In the present disclosure, the characteristic values in the reference period are also referred to as "reference characteristic values," and the characteristic values in the target period are also referred to as "target characteristic values." These characteristic values are not the SOC itself, but values obtained based on the SOC.

In one example, the reference period and the target period are each the time span from when charging of the storage battery is completed to when the next charging begins. In this case, the SOC at the start of both the reference period and the target period is 100%.

In one example, the calculation unit 12 may calculate a parameter obtained from the relationship between the SOC and the open circuit voltage (OCV) as the characteristic value. In the present disclosure, this parameter is also referred to as an "OCV-SOC parameter." Alternatively, the calculation unit 12 may calculate a parameter obtained from the relationship between the SOC and the direct current resistance (DCR) as the characteristic value. In the present disclosure, this parameter is also referred to as a "DCR-SOC parameter." In these examples, the calculation unit 12 executes a calculation based on an equivalent circuit of a storage battery. The equivalent circuit includes a power source whose voltage changes in proportion to the SOC and an internal resistance whose resistance value changes in proportion to the SOC. The calculation based on the equivalent circuit includes a linear equation (1) showing the OCV-SOC characteristic, which is the relationship between the SOC and the OCV, and a linear equation (2) showing the DCR-SOC characteristic, which is the relationship between the SOC and the DCR. In these two equations, a indicates the intercept, and b indicates the slope. It can be said that _{a OCV} , b _OCV , a _DCR , and b _DCR are all linear approximation constants.
OCV=a _OCV +b _OCV・SOC…(1)
DCR=a _DCR +b _DCR・SOC…(2)

The calculation unit 12 may obtain b _OCV in formula (1) as a characteristic value. The constant b _OCV is an example of an OCV-SOC parameter. The calculation unit 12 may obtain DCR when the SOC is 50% as obtained from formula (2) as a characteristic value. In the present disclosure, the DCR when the SOC is 50% is also referred to as DCR _50. DCR ₅₀ is an example of a DCR-SOC parameter.

The calculation unit 12 calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value. Therefore, the calculation unit 12 also functions as a ratio calculation unit. The reference value indicates how the characteristics of the storage battery have changed over time from the reference period to the target period. The reference value can also be said to represent the deterioration state (State of Health: SOH) of the storage battery. It is expected that the lifespan of the storage battery can be predicted by using this reference value.

[System Operation]
An example of processing by the battery management system 1 (server 10) will be described with reference to Fig. 3, and an example of a battery management method according to the present embodiment will be described. Fig. 3 is a flowchart showing an example of the processing as a processing flow S1. Processing flow S1 shows processing for calculating a reference value for one storage battery (electric vehicle 2). The server 10 may execute processing flow S1 for each of a plurality of storage batteries (electric vehicles 2).

In step S11, the acquisition unit 11 acquires data identification information. The data identification information is information used to read out the storage battery data from the database 20. In one example, the data identification information includes at least one of the storage battery ID and the electric vehicle ID, a reference period, and a target period. For example, the reference period may correspond to a period when the storage battery is new, and the target period may correspond to a period in the past including the present time. The acquisition unit 11 may accept data identification information input by a user of the battery management system 1, or may automatically set the data identification information based on a given rule.

In step S12, the acquisition unit 11 acquires storage battery data corresponding to the reference period as reference data. The acquisition unit 11 reads out a record group of storage battery data corresponding to at least one of the storage battery ID and the electric vehicle ID and the reference period from the database 20.

In step S13, the calculation unit 12 calculates a reference characteristic value based on the reference data. In one example, the calculation unit 12 calculates a moving average of the measured voltage and the measured current for each of a plurality of sections set along the time axis. For example, when the time interval between records is 100 milliseconds, the calculation unit 12 sets the section to 10 seconds and calculates the average value of 100 physical quantities within the section every 10 seconds. Furthermore, the calculation unit 12 calculates the SOC for each section. Next, the calculation unit 12 selects a section group in which the moving average of the measured current is equal to or greater than a given threshold. This threshold may be a value for distinguishing whether the electric vehicle 2 is in an idling state or not. Then, the calculation unit 12 calculates the I-V characteristic in the reference period by a statistical method based on the data of the selected section group, and obtains a reference characteristic value based on the I-V characteristic. In this disclosure, the I-V characteristic refers to the relationship between the measured current, the measured voltage, and the SOC. In this disclosure, the "data of the selected section group" is also referred to as "partial data".

Using the measured voltage at a small current will result in a large error in the OCV calculation, and therefore in the calculation of the characteristic value. In addition, depending on the current sensor, the offset error due to temperature and the hysteresis error due to residual magnetism will be large at a small current, which will increase the error in the SOC calculation. By excluding the section corresponding to the idling state where the current is small, these errors can be reduced or avoided, and the characteristic value can be calculated with high accuracy. The idling state refers to the state in which the electric vehicle 2 is operating with no load.

The threshold for determining whether the electric vehicle 2 is idling may be a threshold caused by an offset error of the current sensor, and may be set to, for example, 1 (A). In this case, the error in the SOC can be reduced. Alternatively, the threshold for determining whether the electric vehicle 2 is idling may be a threshold caused by the battery characteristics, and may be set to, for example, 0.05 (CA). In this case, the I-V characteristics can be determined with greater accuracy.

The calculation unit 12 calculates the SOC(k) for each interval k for which the moving average is obtained, using equation (3).
SOC(k)=[W _bat -Σ{I(k)/α}]/W _bat ...(3)
Here, W _bat indicates the rated capacity of the battery, I(k) indicates the measured current in section k, α is a coefficient for converting current (A) to capacity (Ah). If the length of the section is 10 seconds, α=360, and Σ{I(k)/α} indicates the consumed capacity of the battery up to section k.

As a result, the calculation unit 12 obtains the measured current I(k), measured voltage MV(k), and SOC(k) for each of the n intervals k (k = 1 to n). That is, the calculation unit 12 obtains time series data for the moving average of the current, the moving average of the measured voltage, and the corresponding SOC.

Next, the calculation unit 12 calculates the linear approximation constants a _OCV , b _OCV , a DCR , and b _DCR in the formulas (1) and (2) based on the n sets of the measured current, the measured voltage, and the _SOC by a statistical method. As an example, the calculation unit 12 may use the Marquardt method, which is a nonlinear least square method, as the statistical method. The calculation unit 12 uses this Marquardt method to calculate the linear approximation constants a _OCV , b _OCV , a _DCR , and b _DCR that minimize the mean square error between the measured voltage MV and the theoretical voltage CV. In one example, the theoretical voltage CV(k) in the section k is obtained by the formula (4). The formula (4) can be said to represent the IV characteristics of the storage battery based on the equivalent circuit of the storage battery, and can also be said to be a calculation formula for the theoretical voltage.
CV(k)=OCV(k)-I(k)・DCR(k)={a _OCV +b _OCV・SOC(k)}−I(k)・{a _DCR +b _DCR・SOC(k)}…(4)

Alternatively, the calculation unit 12 may use multivariate analysis as the statistical method. In one example, the calculation unit 12 may calculate the first-order approximation constants _aOCV , _bOCV , _aDCR , and _bDCR based on the formula (4).

That is, the calculation unit 12 uses statistical methods such as the Marquardt method, multivariate analysis, etc. to calculate the IV characteristic so that the mean square error between the measured voltage MV and the theoretical voltage CV is minimized, and calculates the first-order approximation constants a _OCV , b _OCV , a _DCR , and b _DCR obtained from this IV characteristic.

In one example, the calculation unit 12 obtains at least one of the b _OCV and the DCR ₅₀ as the reference characteristic value.

In step S14, the acquisition unit 11 acquires storage battery data corresponding to the target period as target data. The acquisition unit 11 reads out a group of records of storage battery data corresponding to at least one of the storage battery ID and the electric vehicle ID and the target period from the database 20.

In step S15, the calculation unit 12 calculates the target characteristic value based on the target data. In one example, the calculation unit 12 calculates the target characteristic value in the same manner as the reference characteristic value. That is, the calculation unit 12 calculates the moving average of the measured voltage and the measured current for each predetermined section. Furthermore, the calculation unit 12 calculates the SOC for each section. Next, the calculation unit 12 selects a section group in which the moving average of the measured current is equal to or greater than a given threshold. Then, the calculation unit 12 calculates the I-V characteristic in the target period using a statistical method based on the data of the selected section group, i.e., the partial data, and obtains the target characteristic value based on the I-V characteristic. The section for calculating the moving average and the threshold for selecting the section are both the same as those used to calculate the reference characteristic value. In one example, the calculation unit 12 uses the Marquardt method or multivariate analysis to calculate the I-V characteristic so that the mean square error between the measured voltage MV and the theoretical voltage CV is minimized, and calculates the linear approximation constants a _OCV , b _OCV , a _DCR , and b _DCR obtained from the I-V characteristic.

In step S16, the calculation unit 12 calculates a reference value based on the reference characteristic value and the target characteristic value. The calculation unit 12 calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value. The calculation unit 12 calculates at least one reference value.

The calculation unit 12 may calculate a ratio related to the OCV-SOC parameter as the reference value. In one example, the calculation unit 12 calculates a ratio indicating the relationship between the b _OCV in a reference period and the b _OCV in a target period as the reference value. The calculation unit 12 may obtain a ratio of the reciprocal of the b _OCV in the target period to the reciprocal of the b _OCV in the reference period as the reference value. In the present disclosure, this reference value is also referred to as "SOH-Q". The reciprocal of the b _OCV is also referred to as b _OCV ^-1 .

The calculation unit 12 may calculate a ratio related to the DCR-SOC parameter as a reference value. In one example, the calculation unit 12 may obtain a ratio of the DCR ₅₀ in the target period to the DCR ₅₀ in the base period as a reference value. In the present disclosure, this reference value is also referred to as "SOH-R."

In step S17, the output unit 13 outputs the reference value. This reference value can be used to predict the life of the storage battery. The output unit 13 may output the at least one reference value to another functional module in the battery management system 1 for subsequent processing in the battery management system 1. Alternatively, the output unit 13 may store the at least one reference value in a predetermined storage device such as a memory or a database. Alternatively, the output unit 13 may display the at least one reference value on a display device. Alternatively, the output unit 13 may transmit the at least one reference value to another computer system.

We will explain examples of reference values, SOH-Q and SOH-R, with reference to Figure 4. Figure 4 shows an example graph of the reference characteristic value and the target characteristic value.

Example (a) shows the OCV-SOC characteristic shown by the linear equation (1) above. The horizontal axis shows SOC (%), and the vertical axis shows OCV (V). Both graphs 201 and 202 show the OCV-SOC characteristic shown by the linear equation (1) above. Graph 201 shows the OCV-SOC characteristic in a reference period, and graph 202 shows the OCV-SOC characteristic in a target period. In this example, the reference period corresponds to a period when the storage battery is new, and the target period corresponds to a period when the storage battery has deteriorated. As can be seen from graphs 201 and 202, as the storage battery deteriorates, the characteristic value b _OCV indicating the slope of the graph increases, and the reciprocal b _OCV ^-1 decreases. Therefore, the reference value SOH-Q gradually decreases from 100% (or 1.0) as the storage battery deteriorates. Since a decrease in the reciprocal b _OCV ⁻¹ means a decrease in the capacity of the storage battery, a decrease in the reference value SOH-Q indicates a decrease in the capacity of the storage battery.

Example (b) shows the DCR-SOC characteristic shown by the linear equation (2) above. The horizontal axis shows SOC (%), and the vertical axis shows DCR (mΩ). Both graphs 211 and 212 show the DCR-SOC characteristic shown by the linear equation (2) above. Graph 211 shows the DCR-SOC characteristic in a reference period, and graph 212 shows the DCR-SOC characteristic in a target period. In this example, too, the reference period corresponds to a time when the storage battery is new, and the target period corresponds to a time when the storage battery has deteriorated. As can be seen from graphs 211 and 212, the characteristic value DCR ₅₀ increases as the storage battery deteriorates. Therefore, the reference value SOH-R gradually increases from 100% (or 1.0) as the storage battery deteriorates.

[program]
A battery management program for causing a computer or computer system to function as the battery management system 1 or server 10 includes program code for causing the computer or computer system to function as an acquisition unit 11, a calculation unit 12, and an output unit 13. This battery management program may be provided after being non-temporarily recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the battery management program may be provided via a communication network as a data signal superimposed on a carrier wave. The provided battery management program is stored in, for example, the auxiliary storage unit 103. The processor 101 reads out the battery management program from the auxiliary storage unit 103 and executes it to realize each of the above-mentioned functional modules.

[effect]
As described above, a battery management system according to one aspect of the present disclosure includes an acquisition unit that acquires reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period after the reference period, a characteristic calculation unit that calculates a characteristic value corresponding to the state of charge of the storage battery during the reference period as a reference characteristic value based on the reference data and calculates a characteristic value corresponding to the state of charge of the storage battery during the target period as a target characteristic value based on the target data, and a ratio calculation unit that calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

A battery management method according to one aspect of the present disclosure is executed by a battery management system including at least one processor. This battery management method includes the steps of acquiring reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period after the reference period, calculating a characteristic value corresponding to the charge state of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the charge state of the storage battery during the target period as a target characteristic value based on the target data, and calculating a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

A battery management program according to one aspect of the present disclosure causes a computer to execute the steps of acquiring reference data indicating the state of a storage battery mounted on an electric vehicle during a reference period and target data indicating the state of the storage battery during a target period following the reference period, calculating a characteristic value corresponding to the charge state of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the charge state of the storage battery during the target period as a target characteristic value based on the target data, and calculating a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value for predicting the life of the storage battery.

In this aspect, the degree of change in the characteristic value corresponding to the battery's state of charge as the base period progresses to the target period is obtained as a reference value. This reference value makes it possible to predict how the characteristics of the battery will change further into the future. Therefore, the reference value can be said to be an effective indicator for predicting the lifespan of the battery.

In a battery management system according to another aspect, the state of the storage battery indicated by the reference data and the target data may include at least the measured voltage and measured current of the storage battery. The characteristic calculation unit may use a statistical method to calculate an I-V characteristic, which is the relationship between the measured current, the measured voltage, and the state of charge during a reference period, based on the reference data, and obtain a reference characteristic value based on the I-V characteristic, and may use a statistical method to calculate an I-V characteristic during a target period based on the target data, and obtain a target characteristic value based on the I-V characteristic. By calculating the reference characteristic value and the target characteristic value using a statistical method, these characteristic values can be calculated with high accuracy from the measured values of the storage battery. As a result, improved accuracy can be expected for both the reference value and the prediction of the life of the storage battery.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the I-V characteristic using a statistical method so that the mean square error between the theoretical voltage of the storage battery obtained by the I-V characteristic based on the equivalent circuit of the storage battery and the measured voltage is minimized. This method allows the reference characteristic value and the target characteristic value to be calculated with high accuracy.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the I-V characteristics using a statistical method such as the Marquardt method or multivariate analysis. By using such methods, the reference characteristic value and the target characteristic value can be calculated quickly.

In a battery management system according to another aspect, the characteristic calculation unit may calculate a moving average of the measured voltage and a moving average of the measured current based on reference data, calculate the I-V characteristics for the reference period based on these moving averages, calculate a moving average of the measured voltage and a moving average of the measured current based on the target data, and calculate the I-V characteristics for the target period based on these moving averages. By introducing the moving average in this way, the amount of data required to calculate the characteristic value can be reduced while the characteristic value can be calculated with high accuracy.

In a battery management system according to another aspect, the characteristic calculation unit may use a threshold value for distinguishing whether the electric vehicle is idling to select partial data for each of the reference data and the target data in which the moving average of the measured current is equal to or greater than the threshold value, calculate a reference characteristic value based on the selected partial data of the reference data, and calculate a target characteristic value based on the selected partial data of the target data. Using the voltage at a small current increases the error in the calculation of the characteristic value. In addition, with some current sensors, the offset error due to temperature and the hysteresis error due to residual magnetism become large at a small current, which increases the error in the calculation of the state of charge. By excluding records of small current, these errors can be reduced or avoided, and the characteristic value can be calculated with high accuracy.

In a battery management system according to another aspect, the characteristic calculation unit may calculate an OCV-SOC parameter obtained from the relationship between the state of charge and the open circuit voltage of the storage battery as a characteristic value corresponding to the state of charge of the storage battery. The inventors have found that in order to predict the life of a storage battery in an electric vehicle that operates at a low discharge rate, it is effective to focus on the relationship between SOC and OCV. By using the OCV-SOC parameter, a reference value for the prediction can be obtained.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the slope of a linear expression that indicates the relationship between the state of charge and the open circuit voltage as the OCV-SOC parameter. This slope clearly indicates the deterioration of the storage battery. Therefore, by using the slope as the OCV-SOC parameter, i.e., the characteristic value, it is possible to obtain a reference value for predicting the life of the storage battery of an electric vehicle that operates at a low discharge rate.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the inverse of the slope in the reference period as the reference characteristic value, and the inverse of the slope in the target period as the target characteristic value. The ratio calculation unit may calculate the ratio of the target characteristic value to the reference characteristic value as the reference value. By this method, a reference value for predicting the life of the storage battery of an electric vehicle operating at a low discharge rate can be obtained.

In a battery management system according to another aspect, the characteristic calculation unit may calculate a DCR-SOC parameter obtained from the relationship between the state of charge and the DC resistance of the storage battery as a characteristic value corresponding to the state of charge of the storage battery. The inventors have found that in order to predict the life of a storage battery in an electric vehicle that operates at a high discharge rate, it is effective to focus on the relationship between SOC and DCR. By using this DCR-SOC parameter, a reference value for the prediction can be obtained.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the DC resistance when the state of charge is 50% as the DCR-SOC parameter. The lower the state of charge, the greater the change in DC resistance depending on the degree of deterioration of the storage battery. On the other hand, if the state of charge becomes too low, this may cause problems in the actual operation of the electric vehicle. Therefore, by focusing on the DC resistance when the state of charge is 50%, it is possible to obtain a reference value for predicting the life of the storage battery of an electric vehicle operating at a high discharge rate without affecting the actual operation of the electric vehicle.

In a battery management system according to another aspect, the characteristic calculation unit may calculate the DC resistance when the state of charge is 50% during a reference period as a reference characteristic value, and may calculate the DC resistance when the state of charge is 50% during a target period as a target characteristic value. The ratio calculation unit may calculate the ratio of the target characteristic value to the reference characteristic value as a reference value. This method makes it possible to obtain a reference value for predicting the life of a storage battery of an electric vehicle that operates at a high discharge rate.

In a battery management system according to another aspect, the electric vehicle may be a cargo handling vehicle. In this case, an effective indicator for predicting the life of the storage battery installed in the cargo handling vehicle can be obtained.

In the battery management system according to another aspect, the storage battery may be a lead-acid battery. In this case, an effective indicator for predicting the life of the lead-acid battery installed in the electric vehicle can be obtained.

[Modification]
The present invention has been described in detail above based on the embodiments. However, the present invention is not limited to the above embodiments. The present invention can be modified in various ways without departing from the spirit and scope of the present invention.

The battery management system 1 may include a prediction unit that predicts the life of the storage battery (battery life) based on a reference value. For example, the prediction unit may predict the battery life from the reference value based on a correspondence table or a formula that indicates the relationship between the reference value and the usage period of the storage battery. When SOH-Q is used as the reference value, the prediction unit may determine the battery life as the point at which the SOH-Q reaches a given threshold value between 50 and 80%. When SOH-R is used as the reference value, the prediction unit may determine the battery life as the point at which the SOH-R reaches a given threshold value between 200 and 300%. The prediction unit may predict the battery life based on both SOH-Q and SOH-R.

That is, the battery management system according to another aspect may further include a prediction unit that predicts the life of the storage battery based on the reference value. In this case, the life of the storage battery can be appropriately, for example, accurately, predicted based on the reference value.

The calculation unit 12 may calculate the reference characteristic value and the target characteristic value by a method other than a statistical method. For example, the calculation unit 12 may calculate the characteristic values using a Kalman filter each time measurement data is obtained.

The reference values may be calculated by a computer or device other than the server 10. For example, each BMU 3 may calculate the reference values for the corresponding storage battery. That is, the battery management system may be implemented in the BMU 3.

The BMU 3 may calculate moving averages of the measured voltage and measured current, and transmit storage battery data indicating these moving averages to the database 20. Alternatively, the BMU 3 may transmit to the database 20 only data for sections in which the moving average of the measured current is equal to or greater than a given threshold. As in the above embodiment, the threshold may be a value for distinguishing whether the electric vehicle 2 is idling or not. In these cases, the amount of communication between the BMU 3 and the database 20 can be reduced, and the processing load on the server 10 can be reduced.

The processing procedure of the method executed by at least one processor is not limited to the example in the above embodiment. For example, some of the steps (processing) described above may be omitted, or each step may be executed in a different order. In addition, any two or more of the steps described above may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be executed in addition to each of the steps described above.

In the present disclosure, when comparing the magnitude relationship of two numerical values, either of the two criteria "greater than or equal to" and "greater than" may be used, or either of the two criteria "less than or equal to" and "less than" may be used. The selection of such criteria does not change the technical significance of the process of comparing the magnitude relationship of two numerical values.

In this disclosure, the expression "at least one processor executes a first process, executes a second process, ... executes an nth process" or a corresponding expression indicates a concept including cases where the entity executing the n processes from the first process to the nth process (i.e., the processor) changes midway. In other words, this expression indicates a concept including both cases where all n processes are executed by the same processor and cases where the processor changes among the n processes according to an arbitrary policy.

1... Battery management system, 2... Electric vehicle, 3... BMU, 10... Server, 11... Acquisition unit, 12... Calculation unit, 13... Output unit, 20... Database.

Claims (15)

an acquisition unit that acquires reference data indicating a state of a storage battery mounted on an electric vehicle in a reference period and target data indicating a state of the storage battery in a target period after the reference period;
a characteristic calculation unit that calculates a characteristic value corresponding to the state of charge of the storage battery in the reference period as a reference characteristic value based on the reference data, and calculates a characteristic value corresponding to the state of charge of the storage battery in the target period as a target characteristic value based on the target data;
a ratio calculation unit that calculates a ratio indicating a relationship between the reference characteristic value and the target characteristic value as a reference value for predicting a life of the storage battery;
Equipped with
The state of the storage battery indicated by each of the reference data and the target data includes at least a measured voltage and a measured current of the storage battery;
The characteristic calculation unit,
Based on the reference data, an I-V characteristic, which is a relationship between the measured current, the measured voltage, and the state of charge during the reference period, is calculated by a statistical method so that a mean square error between the theoretical voltage of the storage battery obtained by the I-V characteristic based on an equivalent circuit of the storage battery and the measured voltage is minimized, and the reference characteristic value is obtained based on the I-V characteristic;
Based on the target data, the IV characteristic during the target period is calculated by the statistical method so that the mean square error between the theoretical voltage of the storage battery obtained by the IV characteristic based on the equivalent circuit of the storage battery and the measured voltage is minimized, and the target characteristic value is obtained based on the IV characteristic.
Battery management system. The characteristic calculation unit calculates the IV characteristics using a Marquardt method or multivariate analysis as the statistical method.
The battery management system of claim 1 . The characteristic calculation unit,
calculating a moving average of the measured voltage and a moving average of the measured current based on the reference data, and calculating the I-V characteristic during the reference period based on these moving averages;
Calculating a moving average of the measured voltage and a moving average of the measured current based on the target data, and calculating the IV characteristic during the target period based on these moving averages;
The battery management system according to claim 1 or 2 . The characteristic calculation unit,
using a threshold value for distinguishing whether the electric vehicle is in an idling state or not, selecting partial data for each of the reference data and the target data, in which the moving average of the measured current is equal to or greater than the threshold value;
Calculating the reference characteristic value based on the selected partial data of the reference data;
calculating the target characteristic value based on the selected partial data of the target data;
The battery management system according to any one of claims 1 to 3 . The characteristic calculation unit calculates an OCV-SOC parameter obtained from a relationship between the state of charge and an open circuit voltage of the storage battery as the characteristic value corresponding to the state of charge of the storage battery.
The battery management system according to any one of claims 1 to 4 . The characteristic calculation unit calculates a slope of a linear expression indicating a relationship between the state of charge and the open circuit voltage as the OCV-SOC parameter.
The battery management system according to claim 5 . The characteristic calculation unit,
calculating an inverse of the slope during the reference period as the reference characteristic value;
calculating the inverse of the slope during the target period as the target characteristic value;
the ratio calculation unit calculates a ratio of the target characteristic value to the reference characteristic value as the reference value;
The battery management system according to claim 6 . The characteristic calculation unit calculates a DCR-SOC parameter obtained from a relationship between the state of charge and the DC resistance of the storage battery as the characteristic value corresponding to the state of charge of the storage battery.
The battery management system according to any one of claims 1 to 7 . The characteristic calculation unit calculates the DC resistance when the state of charge is 50% as the DCR-SOC parameter.
The battery management system according to claim 8 . The characteristic calculation unit,
The DC resistance when the state of charge is 50% during the reference period is calculated as the reference characteristic value;
Calculating the DC resistance when the state of charge is 50% during the target period as the target characteristic value;
the ratio calculation unit calculates a ratio of the target characteristic value to the reference characteristic value as the reference value;
The battery management system of claim 9 . The battery management system according to claim 1 , further comprising a prediction unit that predicts a life of the storage battery based on the reference value. The electric vehicle is a cargo handling vehicle.
The battery management system according to any one of claims 1 to 11 . The storage battery is a lead-acid battery.
The battery management system according to any one of claims 1 to 12 . 1. A battery management method executed by a battery management system having at least one processor, comprising:
acquiring reference data indicating a state of a storage battery mounted on an electric vehicle during a reference period and target data indicating a state of the storage battery during a target period after the reference period;
calculating a characteristic value corresponding to the state of charge of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the state of charge of the storage battery during the target period as a target characteristic value based on the target data;
Calculating a ratio indicating a relationship between the reference characteristic value and the target characteristic value as a reference value for predicting a life of the storage battery;
Including,
The state of the storage battery indicated by each of the reference data and the target data includes at least a measured voltage and a measured current of the storage battery;
In the step of calculating the characteristic value as the target characteristic value,
Based on the reference data, an I-V characteristic, which is a relationship between the measured current, the measured voltage, and the state of charge during the reference period, is calculated by a statistical method so that a mean square error between the theoretical voltage of the storage battery obtained by the I-V characteristic based on an equivalent circuit of the storage battery and the measured voltage is minimized, and the reference characteristic value is obtained based on the I-V characteristic;
Based on the target data, the IV characteristic during the target period is calculated by the statistical method so that the mean square error between the theoretical voltage of the storage battery obtained by the IV characteristic based on the equivalent circuit of the storage battery and the measured voltage is minimized, and the target characteristic value is obtained based on the IV characteristic.
Battery management methods. acquiring reference data indicating a state of a storage battery mounted on an electric vehicle during a reference period and target data indicating a state of the storage battery during a target period after the reference period;
calculating a characteristic value corresponding to the state of charge of the storage battery during the reference period as a reference characteristic value based on the reference data, and calculating a characteristic value corresponding to the state of charge of the storage battery during the target period as a target characteristic value based on the target data;
Calculating a ratio indicating a relationship between the reference characteristic value and the target characteristic value as a reference value for predicting a life of the storage battery;
on the computer ,
The state of the storage battery indicated by each of the reference data and the target data includes at least a measured voltage and a measured current of the storage battery;
In the step of calculating the characteristic value as the target characteristic value,
Based on the reference data, an I-V characteristic, which is a relationship between the measured current, the measured voltage, and the state of charge during the reference period, is calculated by a statistical method so that a mean square error between the theoretical voltage of the storage battery obtained by the I-V characteristic based on an equivalent circuit of the storage battery and the measured voltage is minimized, and the reference characteristic value is obtained based on the I-V characteristic;
Based on the target data, the IV characteristic during the target period is calculated by the statistical method so that the mean square error between the theoretical voltage of the storage battery obtained by the IV characteristic based on the equivalent circuit of the storage battery and the measured voltage is minimized, and the target characteristic value is obtained based on the IV characteristic.
Battery management program.

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