JP7569460B2 - Battery remote monitoring system and battery remote monitoring method - Google Patents

Description

The present invention relates to a storage battery remote monitoring system and a storage battery remote monitoring method.

Lithium-ion batteries and other storage batteries are used in moving vehicles such as railway cars. The speed at which the performance of storage batteries such as lithium-ion batteries deteriorates varies depending on load conditions such as temperature, charging rate, and current pattern.
As the deterioration of the storage battery progresses, the capacity maintenance rate of the storage battery decreases, making it more likely to run out of power (a state in which power runs out). Furthermore, as the resistance deterioration of the storage battery progresses, abnormal heat generation becomes more likely, and the decrease in the capacity maintenance rate of the storage battery and the progression of resistance deterioration can cause imbalances in the storage battery voltage.
For this reason, accurately detecting and responding to signs of abnormalities in storage batteries has become a major technical challenge for the mobile systems that support the electrification of social infrastructure.

Patent Document 1 discloses a mobile object anomaly judgment support system that aims to appropriately predict the possibility that a malfunction occurring in a mobile object will lead to an anomaly, thereby reducing false positives and improving operating rates.
The mobile body abnormality judgment support system disclosed in Patent Document 1 is equipped with various measuring devices, a status monitoring device that detects the occurrence of a malfunction using mobile body status data from the measuring devices, and a communication device that transmits malfunction information to a ground system and receives abnormality judgment results from the ground system.

In addition, the ground system in Patent Document 1 includes a communication device that transmits and receives data to and from mobile bodies, a storage unit that accumulates past fault information, an abnormality diagnosis unit that analyzes and determines whether past faults are abnormal or not, an abnormality analysis database that stores the past fault information and the abnormality diagnosis results in association with each other, and an abnormality occurrence prediction unit that compares the fault information at the time of occurrence with the abnormality analysis database and outputs the abnormality determination result.

JP 2011-201336 A

In Patent Document 1, when an abnormality occurs in a moving object, the presence or absence of the abnormality and the state of the abnormality prediction unit are notified to the worker, which is effective in diagnosing abnormalities in moving objects. However, Patent Document 1:
It is not anticipated that batteries installed in moving objects will be monitored remotely.
Because the data detected from storage batteries is diverse and shows different values depending on factors such as the conditions under which the storage battery is used, simply setting a uniform threshold value makes it difficult to accurately identify important anomalies that require action.

Therefore, the present invention aims to provide a remote battery monitoring technology that enables remote monitoring of a mobile system equipped with a storage battery, determines the importance of warnings for a mobile system including a storage battery, and establishes priorities for dealing with abnormalities.

In order to solve the above problems, one representative remote battery monitoring system of the present invention is as follows:
A battery remote monitoring system including a mobile object equipped with a battery and an external server,
The moving object includes a data acquisition device for acquiring moving object status data and storage battery status data;
a communication device that transmits the moving object status data and the storage battery status data acquired by the data acquisition device to the external server;
The external server is
generating a warning based on a warning threshold determined for each warning content from at least one of the mobile object status data and the storage battery status data received from the mobile object;
The warning is weighted to calculate a priority index for maintenance corresponding to the detected warning.

According to the present invention, it is possible to provide a remote battery monitoring technology that enables remote monitoring of a mobile system equipped with a storage battery, determines the importance of an abnormality in the mobile system, and establishes priorities for dealing with the abnormality.
Problems, configurations and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention.

FIG. 1 is a system configuration diagram of a mobile storage battery remote monitoring system according to the present disclosure. FIG. 2 is a system configuration diagram of the warning weighting calculation unit. FIG. 3 is a flow chart of the warning weighting calculation unit. FIG. 4 is a diagram illustrating the warning threshold. FIG. 5 is a diagram showing an example of setting the warning threshold value and the weighting coefficient. FIG. 6 is a diagram showing an example of calculation of the warning importance and the accumulated importance. FIG. 7 is a diagram showing an example of visualization of maintenance priorities. FIG. 8 is a diagram showing data for estimating a cause of a warning. FIG. 9 is a diagram showing a factor analysis table. FIG. 10 is a diagram showing the procedure for factor estimation. FIG. 11 is a diagram showing data for estimating a cause of a warning. FIG. 12 is a diagram showing a factor analysis table. FIG. 13 is a diagram showing a procedure for factor estimation. FIG. 14 shows an example of a maintenance proposal screen displayed on a monitor. FIG. 15 is a diagram showing an example of a notification by email.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiment. In addition, in the description of the drawings, the same parts are indicated by the same reference numerals.
When there are multiple components having the same or similar functions, they may be described by using the same reference numerals with different subscripts, or when there is no need to distinguish between these multiple components, the subscripts may be omitted.

In this disclosure,
"Failure" means that the equipment is in a state that is different from the normal state. The range of what is considered to be the normal state is generally defined in the specifications of each equipment, but it is not necessarily limited to the range defined in the specifications.
"Abnormal" refers to an undesirable state of the system caused by equipment failure, and a state that requires recovery by shutting down the equipment, etc.
"Abnormal data" refers to various data that result in an "abnormality."
"Abnormal data history" refers to whether or not the same type of abnormal data or warning data has been recorded in the past, and the frequency with which it has occurred.
The "warning threshold" refers to the determination condition at which a "warning" is generated.
"Alert Data" means data sent as an alert.
"Seasonal factors" refer to elements or coefficients that are determined for each period determined by months, dates, etc.
"Mobile object status data" refers to data obtained by measuring devices equipped on a mobile object that indicates the status of the mobile object's equipment (e.g., a cart, brakes, etc.) and the status of sensors equipped on the equipment.
An "operation mode" is a classification of the operation status of a mobile body, for example, for a storage battery of a mobile body, it is classified into rapid charging, normal charging, discharging, etc. In addition, the running state is classified into powering, braking, coasting, regenerating, etc. At least one of these classifications can be included and expressed as an operation mode.

"Battery status data" means data including one or more of the following, acquired on a cell or module basis for a battery: current, voltage, power, charging rate, temperature, capacity maintenance rate (information regarding capacity deterioration within the degree of deterioration of the battery), and resistance increase rate (information regarding resistance deterioration within the degree of deterioration of the battery).
"Weather data" means data including one or more of the following: outside temperature, humidity, cloud cover, rainfall, date, time, amount of solar radiation, date, and season.
"Operation status" refers to data including one or more of the following: operation mode, time of battery system introduction, battery operation period, and vehicle information (occupancy rate, vehicle weight, vehicle position information, diagram, number of passengers, etc.).
In particular, "battery operation status" means data including one or more of the following: the time of introduction of the battery system, the period of operation of the battery, and vehicle information (occupancy rate, vehicle weight, vehicle position information, diagram, number of passengers, etc.).
"Remotely monitored data" means data including one or more of the following: current, voltage, power, charging rate, temperature, air temperature, capacity maintenance rate (information regarding capacity deterioration within the degree of deterioration of the storage battery), resistance increase rate (information regarding resistance deterioration within the degree of deterioration of the storage battery), and speed (rotor frequency).
The "maintenance priority index" is an index that indicates the necessity and urgency of part replacement, repair, and maintenance, and can be expressed as a rank or numerical value.
The term "external server" refers to a data server installed outside the vehicle in which the storage battery to be monitored is installed. Such an external server may be installed in a ground system that monitors the vehicle, or may be located on a cloud.

<System Configuration>
First, the system configuration of the remote battery monitoring system of the present disclosure will be described with reference to Fig. 1. Fig. 1 is a system configuration diagram in which the remote battery monitoring system of the present invention is applied to a railway vehicle as an example of a moving body.
In FIG. 1, a remote battery monitoring system 100 includes a vehicle 101 and an external server 102 .
The vehicle 101 is equipped with a measuring device 103 that measures the states of various devices in the vehicle 101, a status monitoring device 104 that monitors the states of the various devices measured by the measuring device 103, a status data recording unit 105 that records status data monitored by the status monitoring device 104, and a cab monitor 106. The vehicle 101 also includes a storage battery 120, a battery control device 110 that controls the storage battery 120, a storage battery data recording unit 109 that records the storage battery status from the storage battery control device 110, and a data acquisition device 108 that acquires the status data monitored by the status monitoring device 104 and the storage battery status data of the storage battery data recording unit 109.
Furthermore, the vehicle 101 includes a communication device 107 for transmitting and receiving data to and from the external server 102. The communication device 107 is connected to a data acquisition device 108.
The data X acquired by the data acquisition device 108 from the status monitoring device 104 includes data on the driving mode, vehicle information, and the like.

The external server 102 includes a battery data storage unit 112, a status data storage unit 111 that stores mobile unit status data 203 of various devices equipped in the vehicle 101, an abnormality data storage unit 113 that stores past warnings such as past warning data and abnormality data, and a weather data acquisition unit 114 that acquires weather data for the travel route.
The external server 102 also has an alert weighting calculation unit 115 that detects abnormal data from the battery status data 204 and the mobile object status data 203 based on thresholds, generates an alert, performs a weighting calculation on the generated alert, sorts the alerts in order of importance, and calculates the maintenance priority.
Furthermore, the external server 102 is provided with a monitor 116 that displays the results of the warning weighting calculation section 115 .

<Processing and data flow>
Next, the process and data flow for generating a warning and performing weighting calculations in the battery remote monitoring system disclosed herein will be described.

1 is a device that acquires the status of devices equipped in the vehicle 101 and measurement data of sensors equipped in the devices. The measurement device 103 transmits the sensor measurement data to a status monitoring device 104. The status monitoring device 104 receives the measurement data from the measurement device 103, judges the status based on the received data, and records the received measurement data and the result of the status judgment in a status data recording unit 105.
Here, the measurement data (mobile body state data) of the vehicle includes various physical quantities that can be acquired by sensors provided on the vehicle 101, such as air temperature, humidity, wind speed, rotor frequency, and temperatures of various components.
In addition, the status data recording unit 105 can also store the operating mode, vehicle information (occupancy rate, vehicle weight, vehicle position information, diagram, number of occupants, etc.), etc., and the status monitoring device 104 can access the operating mode and vehicle information.
In addition, the status determination results of the status monitoring device 104 can be displayed on the cab monitor 106.

Next, the battery control device 110 controls the battery on a cell-by-cell or module-by-module basis based on the battery status data acquired from the battery 120. The battery status data 204 includes one or more of the following data acquired on a cell-by-cell or module-by-module basis: current, voltage, power, charging rate, temperature, capacity maintenance rate (information on capacity deterioration within the battery deterioration degree), and resistance increase rate (information on resistance deterioration within the battery deterioration degree).

The data acquisition device 108 can acquire information (X) necessary for extracting mobile object status data, operation mode, vehicle information, etc. from the status monitoring device 104, and can acquire storage battery status data from the storage battery control device 110. The various data (Y) acquired by the data acquisition device 108 is transmitted to the external server 102 via the communication device 107.
In addition, the various data to be transmitted to the external server may be linked with time data when each data was acquired and transmitted to the external server 102 .
The method of transmission to the external server 102 can be any communication method, such as a method using close-proximity wireless or a method using an LTE line.

<<Warning weight calculation section>>
Next, the warning weight calculation unit 115 will be described in detail with reference to Fig. 2 and Fig. 3. Fig. 2 is a diagram showing the processing and data flow in the warning weight calculation unit 115 described in Fig. 1. FIG. 3 is a flow chart of the process.

(Step 301)
In step 301 in FIG. 3, the warning generation unit 205 in FIG. 2 acquires the mobile unit state data 203, the storage battery state data 204, and the meteorological data 201.

(Step 302)
Next, the warning generation unit 205 extracts the vehicle operation mode 212 and the storage battery operation status 213 based on the moving object status data 203 and the storage battery status data 204 .

(Step 303)
Next, the warning generation unit 205 sets a warning threshold 206 determined for each warning content with respect to the operation mode 212, the operation status 213 of the storage battery, and the weather data 201.
The warning threshold 206 can be set in multiple stages for one indicator, the details of which will be described later.
Once the warning threshold 206 has been set, it is not necessary to reset it unless a correction is required. In this case, step 303 can be skipped and the process can proceed from step 302 to step 304.

(Step 304)
Next, the warning generation unit 205 judges whether the storage battery state data 204 exceeds the range of the warning threshold 206 based on the set warning threshold 206. If the storage battery state data 204 does not exceed the range of the warning threshold 206, the process proceeds to step 310. If the storage battery state data 204 exceeds the range of the warning threshold 206, a warning is generated and the process proceeds to step 305.

(Step 305)
The warning generation unit 205 records the time at which it is determined that the storage battery state data 204 exceeds the range of the warning threshold 206 as the warning generation time (t1).
In addition, the warning generation unit 205 transmits to the warning notification device 209 the warning generation time (t1), the content (warning content) of the warning threshold 206 beyond which the storage battery state data 204 deviates, and the operation mode 212 at the warning generation time.
Furthermore, the warning generation unit 205 transmits the warning threshold value 206 to the weighted integrated value calculation unit 207 .
The timing of transmission of the warning threshold value 206 to the weighted integrated value calculation unit 207 is not limited to step 305, and may be any timing after the warning threshold value 206 is set.

(Step 306)
Next, the weighted integrated value calculation unit 207 reads out past warning data from the abnormality data storage unit 113 .

(Step 307)
Next, the weighted integrated value calculation unit 207 performs a weighted calculation of the importance of the warning based on the battery state data 204, the operation mode 212, the battery operation status 213, and the weather data 201, and calculates the warning importance, which is the integrated value of the weighting coefficients.
A specific method for calculating the warning importance will be described later.

(Step 308)
Next, the weighted integrated value calculation unit 207 records the warning content and the integrated value of the weighting coefficient (warning importance) in the abnormality data storage unit 113 .

(Step 309)
Next, the weighted integrated value calculation unit 207 transmits the warning content and the integrated value of the weighting coefficient (warning importance) to the abnormality countermeasure priority order determination unit 208 .
In addition, if the abnormality data accumulation unit 113 can acquire the warning content and its weighted accumulated value from the abnormality data accumulation unit 113, the weighted accumulated value calculation unit 207 does not necessarily need to transmit the warning content and its weighted accumulated value to the abnormality countermeasure priority determination unit 208.

(Step 310)
The abnormality countermeasure priority determination unit 208 calculates a cumulative importance based on the number of similar warnings in the past and their importance. Then, based on the cumulative importance, it determines the priority of abnormality countermeasures and transmits the determination result to the warning notification device 209. That is, in this example, the cumulative importance becomes a maintenance priority index.
The warning notification device 209 selects a display format based on the abnormality countermeasure priority index, and transmits a display image to the warning display unit 211 .
The contents displayed on the warning display unit 211 can be displayed in various units such as by storage battery, by vehicle, or by the route on which the vehicle is traveling, and the display method is not limited to these.

In the following, specific methods for calculating the warning threshold value, the weighting coefficient, the warning importance level, and the cumulative importance level, which have been described in steps 307 to 310, will be described with reference to FIGS.

<Warning threshold settings>
First, the setting of the warning threshold 206 will be described with reference to Fig. 4. Fig. 4(a) shows an example of a warning threshold for the battery temperature when the operation mode is the quick charge mode, the seasonal factor is spring (March to June), and the operation period as the operation history of the battery as the operation status is two years. In other words, it shows an index for a warning of an overtemperature of the battery.
The horizontal axis represents time, and the vertical axis represents the temperature of the battery. The warning threshold 206 is set according to the vehicle operation mode, the season, and the operation period of the battery.
The warning threshold 206 can be set in multiple stages for one index, for example, in three stages, and a weight can be set for each stage.

In the example of FIG. 4, the over-temperature warning threshold 206 is set to three levels: a light warning threshold (Th:L), a medium warning threshold (Th:M), and a heavy warning threshold (Th:H).
For example, the severe warning threshold (Th:H) can be set at a level just before an abnormality is detected, and the medium warning threshold (Th:M) can be set as a level at which drastic countermeasures need to be prepared or at which early countermeasures such as removing the battery or preparing for maintenance should be taken.
In addition, the minor warning threshold (Th:L) can be set as a warning level for a stage where minor countermeasures are to be prepared or where early countermeasures are not required.
Specific threshold values and weights can be determined from past experience and battery specifications.

4A, the temperature data of the storage battery is set to three levels, namely, a light warning threshold (Th:L), a medium warning threshold (Th:M), and a heavy warning threshold (Th:H), with the heavy warning threshold (Th:H) being the highest threshold temperature and set as a threshold indicating a more serious fault state, and the medium warning threshold (Th:M) being set at an intermediate temperature between the heavy warning threshold (Th:H) and the light warning threshold (Th:L).
In this way, by setting the warning threshold in multiple stages, it is possible to make more precise warning decisions.
In FIG. 4A, the time when the temperature of the storage battery exceeds the light warning threshold (Th: L) is set as the warning generation time (t1).

Fig. 4(b) is a schematic diagram showing seasonal variations in the light warning threshold (Th:L) in Fig. 4(a). Fig. 4(b) shows an example of the upper hemisphere where the outside temperature drops in winter, and shows that the warning threshold in winter (December to February in Japan) is set to a lower temperature than the warning threshold in spring (March to June in Japan) and autumn (October to November in Japan), and the warning threshold in summer (July to September in Japan) is set to a higher temperature than the warning threshold in spring and autumn.

Generally, the average temperature is high in summer, so the temperature of the battery itself is also high, but in winter, the average temperature is low, so the temperature of the battery itself is lower than in other seasons.
In summer, even if the battery temperature becomes high, it is due to high air temperature, and the possibility of a battery abnormality is low, but in winter, the air temperature is low, so there is a high possibility that the battery has reached an overtemperature due to abnormal heat generation. Therefore, for overtemperature in winter, it is desirable to set stricter threshold values or a larger coefficient in order to give the battery a higher maintenance priority, given that there is a high possibility of heat generation due to a battery abnormality.
In the above example, the warning threshold is set for each season, but the warning threshold can be set for the difference between the temperature data of the storage battery and the outside air temperature regardless of the season. This eliminates the need to manage thresholds and warnings for each season.
The outside air temperature may be not only meteorological data, but also the vehicle ambient temperature obtained by a temperature sensor installed on the battery system box that houses the battery system, on the outer side of other equipment adjacent to the battery system box, or near the location where the battery system box is installed on the vehicle body.

<Setting thresholds and weighting coefficients>
The weighting coefficient differs between serious, medium and minor warnings, and the weighting coefficient can be made variable for each warning type depending on whether the warning is the first or second or subsequent occurrence.

For warnings other than temperature, the weighting coefficient can be varied to account for seasonal factors, and the weighting can be varied based on the number of days of operation as an operating history. Weighting can also be varied based on the degree of deterioration of the storage battery.
For example, if a warning occurs within one year as an abnormal data history, it is highly likely that the battery is abnormal and is unlikely to be due to deterioration over time. For this reason, if the operation period is within one year, it is possible to increase the weight to make it easier to detect in order to quickly identify the cause. On the other hand, if a warning occurs after the sixth year of operation, it is highly likely that the battery has deteriorated over time and it is unlikely that the abnormality is due to an initial defect that requires urgent action, so it is desirable to keep the weighting coefficient constant.
In addition, the weighting coefficient for seasonal factors in the temperature data of the storage battery may be set to a warning threshold for the difference between the temperature data of the storage battery and the outside air temperature regardless of the season, which eliminates the need to manage thresholds and warnings for each season.
In addition, the weighting coefficients and warning thresholds for the battery data (temperature, voltage, etc.) may be made variable according to the degree of battery deterioration, making it possible to identify abnormal batteries while taking into account the variability in the degree of battery deterioration.

Next, an example of setting the warning threshold value and the weighting coefficient will be described with reference to the example of FIG. 5 based on the above viewpoint.
FIG. 5 shows an example in which warning thresholds 504 are set for each month for the voltage 501, the state of charge (SOC) 502, and the temperature 503 of each cell of the storage battery at three levels: a heavy warning threshold (Th:H), a medium warning threshold (Th:M), and a light warning threshold (Th:L).
As for the weighting coefficient 505, the weighting coefficient is shown for each of the three stages of the warning threshold 504, separately for the first warning and the second or subsequent warnings.
Furthermore, the seasonal coefficient 506 indicates a factor by which the coefficient is multiplied, and the operation period 507 of the storage battery also indicates a factor by which the coefficient is multiplied.
It is possible to take measures such as setting an upper limit on the importance, which is the integrated value of the weighting coefficients, so that the calculated value does not become an outstanding value.

<Warning severity and cumulative severity settings>
Next, a specific example of calculating the warning importance level and an example of the accumulated importance level using thresholds and weighting will be described with reference to FIG.
6 is an example of a table showing the state and importance for each time 601 when a warning was detected. The vertical axis shows events 621, 622 for which a warning was detected for each time 601 when a warning was detected. The horizontal axis shows the battery voltage 602, battery temperature 603, number of overvoltage warnings 604, number of overtemperature warnings 605, total number of warnings 606, number of days of operation 607, warning content 608, warning importance 609, and cumulative importance 610.

The warning importance 609 is calculated according to the following formula 1 in accordance with the example of setting the warning threshold and weighting coefficient shown in FIG. 5. Warning importance=(warning weighting coefficient)×(seasonal weighting coefficient)×(number of operation days weighting coefficient) (1)
Moreover, the cumulative importance level is displayed by adding up the importance levels of past warnings of the same content.
Note that a weighting coefficient according to the degree of battery deterioration may be added to calculate the importance of the warning. This allows measures to be taken to prioritize the degree of battery deterioration over the number of days of operation. This makes it possible to take measures against abnormalities caused by rapid deterioration that is difficult to determine based on the number of days of operation alone. In some cases, it may be possible to extract warnings and abnormalities that take the degree of battery deterioration into account, regardless of the number of days of operation.

(Case 621)
In the case of 621 in Figure 6, the temperature of the storage battery reached 56°C in July, exceeding the medium warning threshold for over-temperature. Since this event is not the first to occur, the weighting coefficient is 10. The seasonal weighting coefficient for July is 0.5. As for the weighting coefficient for the number of days of operation, since the number of days of operation was 300 days, it is within one year, so the weighting coefficient is 2. As a result, the warning importance level for event 621 is calculated as 10 x 0.5 x 2 = 10.
It should be noted that for 621, an over-temperature warning has been issued in the past, and if the importance of the past warnings was 20, then the cumulative warning importance would be calculated as 10+20=30.

(Case 622)
6 indicates that an over-temperature warning occurred again one minute after the event 620. In this case, the battery temperature is 57° C., exceeding the severe warning threshold, and the weighting coefficient is 100. The warning importance 609 calculated according to Equation 1 is calculated as 100×0.5×2=100.
In this case, the cumulative importance 610 is calculated as 130 by adding the cumulative importance of 30 from the previous time.

In this way, by using warning thresholds that are divided into stages according to the season, and by making the thresholds variable based on the vehicle operation mode, season, and battery operation period, and by making it possible to set coefficients for weighting calculations, it is possible to detect any signs of abnormalities that vary with the season without overlooking them.
A threshold value according to the degree of deterioration of the storage battery may be added, which makes it possible to detect signs of abnormality that take the degree of deterioration of the storage battery into account without overlooking them.

<Maintenance priority>
Next, an example of visualizing the priority order of maintenance using weighting coefficients will be described with reference to Fig. 7. Fig. 7(a) shows an example of displaying the number of occurrences of warnings 701 and the importance of warnings 702 in a graph format using diagonal lines and white columns, respectively.
In the example of Fig. 7(a), the weighted warning importance 702 is displayed for each train configuration. Fig. 7(a) shows that the warning importance is the highest for train configuration 2, and that maintenance should be performed with the highest priority. Although the warning importance is the same for train configurations 1 and 4, the example shows that maintenance should be performed with priority for train configuration 4, which has had the most warnings.
In addition, the battery deterioration level may be added to the indices that determine the priority order, and the batteries may be visualized in order of decreasing deterioration level (lowest capacity maintenance rate, highest resistance increase rate). For example, if other indices are the same, warnings may be displayed in order of decreasing battery deterioration level. Also, for example, if the battery deterioration levels are the same, warnings may be displayed in order of decreasing other indices. This allows maintenance to be performed with priority given to the battery deterioration level. In some cases, warnings and anomaly extraction may be possible that take into account only the battery deterioration level.

For vehicles equipped with storage batteries, even if they have been used in the same manner or for the same length of time, the occurrence of failures or abnormalities may differ for each battery cell, vehicle, or train set. For this reason, in order to achieve efficient maintenance, it is important to form a population of vehicles that are thought to be used in the same manner, such as by mileage, when the storage battery was introduced, and schedule, or to form a population by using AI (artificial intelligence) to extract vehicles with similar storage battery current patterns, and to be able to detect the importance of abnormalities for each storage battery, train set, or line for that population.
A variety of forms can be used to express such priorities, and it is also possible to prioritize early abnormality countermeasures on a battery-by-battery, vehicle-by-train, or line-by-line basis.

7B shows an example in which the warning importance and cumulative importance 610 described in FIG. 6 are rearranged in descending order of cumulative importance 610. In this example, if the cumulative importance 610 is the same, the warnings are rearranged in descending order of the total number of warnings 606.
In addition, the degree of battery deterioration may be added to the index for determining the priority order, and the batteries may be sorted in order of decreasing degree of battery deterioration (lowest capacity maintenance rate, highest resistance increase rate), or when the warning importance or cumulative importance is the same, the degree of battery deterioration may be sorted in order to determine the priority order.
In this way, the system disclosed herein makes it possible to visualize maintenance priorities from various perspectives, thereby making it possible to formulate the most rational maintenance plan based on the time and budget required for maintenance.

<Causes Estimated>
When a warning is generated, it may be possible to estimate the cause of the warning from the battery status data and meteorological data. For example, in the warning generation unit 205 shown in FIG.

(Case 801)
In this case, when an over-temperature warning occurs, the system uses weather data to determine whether the temperature at the time the warning occurred deviates from the average, whether a fault has been found in the vehicle operating mode and the storage battery parameters based on the operating mode, and whether a fault has been detected in the degree of resistance degradation of the battery, which is a cause of an over-temperature warning. The following two cases are examples of how the warning cause is inferred based on a combination of these determination results.

First, in case 801, the driving mode is the "quick charge mode" and the season is "spring (3,
Assume that the over-temperature warning occurs as shown in FIG. 8( a) when the battery is in operation for “two years” and the battery is in operation for “two years” (either April or May).
Fig. 8A is a graph showing the transition of the temperature of the storage battery 10. Fig. 8A shows that at time t1, the temperature of the storage battery 10 exceeds the threshold value Th:H, and a severe over-temperature warning is issued.
In this case, the warning generation unit 205 in Figure 2 refers to the outside air temperature conditions in Figure 8(b), the battery current value in Figure 8(c), and the resistance deterioration level (SOHR) in Figure 8(d) based on the mobile unit status data 203, the storage battery status data 204, the weather data 201, the operation mode 212, and the storage battery operation status 213, etc.

In FIG. 8(b), since the outside temperature does not reach the average temperature (Ave. Temperature), it is determined that the cause of the over-temperature is not the outside temperature. Next, the current value in FIG. 8(c) is checked, and since the operation status is during quick charging at the time when the warning was generated, the current value is excessive, and the storage battery resistance deterioration degree (SOHR) is deteriorated to a certain degree compared to when it was new, it can be inferred that the warning was generated due to the combination of "input and output of large current due to quick charging mode" and "increase in storage battery resistance deterioration degree (SOHR)". Then, (1) reducing the current value during quick charging and (2) replacing the storage battery are selected as measures for early abnormality countermeasures. After that, the measures based on the cause estimation result are notified.

Such factor estimation is performed according to the factor analysis table shown in Fig. 9. The factor analysis table shown in Fig. 9 first performs judgments in the order of outside temperature judgment 901, current value judgment 902, and resistance deterioration degree judgment 903, and can present factor estimation 904 and measures 905. Specifically, the warning generation unit 205 performs factor estimation by an automatic judgment program that realizes the procedure of factor estimation shown in Fig. 10.

(Case 1101)
Next, the cause estimation when a warning about a power shortage occurs will be described with reference to FIGS.
First, in case 1101, it is assumed that the operating mode is “quick charge mode”, the season is “spring (March, April, or May)”, and the battery operation period is “two years”, and a low-power warning occurs as shown in FIG. 11(a).

Fig. 11(a) is a graph showing the transition of the battery's charging rate. Fig. 11(a) shows that at time t1, the battery's charging rate (SOC) exceeds a threshold Th, and a charging rate warning is generated. In this case, when a low-power warning is generated, it is determined whether the temperature at the time the warning is generated deviates from the average based on weather data, whether a fault is found in the operating mode of the railway vehicle and the battery parameters based on the operating mode, and whether a fault is found in the capacity maintenance rate of the battery, which is a cause of the low-power warning, and the cause of the warning is estimated based on a combination of the determination results.

In this case, as shown in Fig. 11(b) and Fig. 11(c), there is no influence of the outside temperature and no abnormal charging/discharging. On the other hand, as shown in Fig. 11(d), the state of health (SOH) has decreased by a certain amount from the SOH when the battery was new, so it is presumed that the warning is due to the deterioration of the battery capacity.
Based on the results of these warning cause estimations, the proposed measures for early detection of abnormalities are (1) to raise the upper limit charging rate in order to resolve the insufficient charging caused by capacity degradation, or (2) to replace the storage battery.

Such factor estimation is performed according to the factor analysis table shown in Fig. 12. The factor analysis table shown in Fig. 12 first performs an outside temperature determination 1201, a discharge power determination 1202, and a capacity deterioration degree determination 1203 in that order, and can present a factor estimation 1204 and a countermeasure content 1205.
Specifically, the warning generation unit 205 performs cause estimation by an automatic determination program that realizes the cause estimation procedure shown in FIG.

<Display maintenance suggestions>
Fig. 14 is an example of a maintenance proposal screen output to the monitor 116 of the external server 102 shown in Fig. 1. In this example screen, the importance of warnings and maintenance timing proposals can be displayed for each storage battery, each train set, or each line.
In this example screen, the importance of maintenance is shown in graphical form, allowing maintenance staff or operations managers to easily determine maintenance priorities.

In addition, information such as the warning detected by the external server 102, the warning severity indicating the severity of the warning, the cumulative severity, and the measures for early abnormality countermeasures obtained from the results of the warning cause estimation may be transmitted from the external server 102 to the vehicle 101 while ensuring information security, or may be stored in a form that allows the vehicle 101 to confirm the information.
In this way, by making it possible for the vehicle 101 to immediately check warnings and countermeasures detected by the external server 102, it becomes possible to quickly implement desired control over the storage battery of the vehicle 101.

Furthermore, for maintenance that involves the replacement of components such as storage batteries, by combining information on the timing of when it will be necessary to procure these components such as batteries, it is possible to present a maintenance plan that calculates backwards from when maintenance should be performed and shows on-screen when procurement will begin.
Specifically, the system outputs the result of subtracting the time required for procuring and designing the batteries from the timing when the above maintenance should be performed, making it possible to optimize the timing of battery procurement, preventing deterioration of the storage batteries through early procurement and reducing downtime caused by delayed procurement.
This will make it possible to minimise the impact of delays in procurement for items that require early maintenance.
For maintenance involving replacement of components such as batteries, the battery deterioration level may be combined. This allows a maintenance plan to be presented in descending order of battery deterioration level (lowest capacity maintenance rate, highest resistance increase rate).

Furthermore, the above-mentioned maintenance information can be not only displayed on the monitor of an external server, but can also be directly transmitted to a maintenance worker or an operation manager by email.
15(a) and (b) show examples of notifications by email.
By providing a screen that displays graphs and estimates of the causes of warnings for the highest priority batteries, train units, or lines, as well as the details of the measures to be taken, it is possible to create maintenance schedules for batteries, train units, or lines that require early maintenance.

Figure 8 shows an example of a warning notification when a warning occurs. In this example, a warning is output for each train set, but the notification unit may be a storage battery unit, a train set unit, or a line unit. The screen configuration and the items and contents displayed on the screen can be freely selected.

Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the gist of the present invention.
For example, the types of data indicating the status of the mobile object and the storage battery are not limited to those described above, and various indexes, parameters, etc. can be adopted.
In addition, the type and level of the warning threshold can be changed in various ways, and it goes without saying that the weighting level and the calculation method can also be changed in various ways. For example, the weighting coefficient items can be the battery material, battery capacity, number of charge/discharge cycles, number and time of quick charge, time when the battery has not been used, and the degree of deterioration of the battery.
Furthermore, various changes can be made to the warning content settings, the content of abnormal data history, the content of seasonal factors, how the storage battery operation history is taken into account, the composition of the data factor analysis table, and the judgment methods used.
Furthermore, with regard to the configuration of the various functional units described in this disclosure, each function may be transferred to another functional unit to the extent that no problems are caused.

100 Battery remote monitoring system 101 Vehicle 102 External server 103 Measuring device 104 Status monitoring device 105 Status data recording unit 106 Cab monitor 107 Communication device 108 Data acquisition device 109 Battery data recording unit 110 Battery control device 111 Status data accumulation unit 112 Battery data accumulation unit 113 Abnormality data accumulation unit 114 Weather data acquisition unit 115 Warning weighting calculation unit 116 Monitor 120 Battery 201 Weather data 203 Mobile body status data 204 Battery status data 205 Warning generation unit 207 Weighted integrated value calculation unit 208 Abnormality countermeasure priority determination unit 209 Warning notification device 211 Warning display unit 212 Operation mode 213 Battery operation status t1 Warning occurrence time

Claims (13)

A battery remote monitoring system including a mobile object equipped with a battery and an external server,
The moving object includes a data acquisition device for acquiring moving object status data and storage battery status data;
a communication device that transmits the moving object status data and the storage battery status data acquired by the data acquisition device to the external server;
The external server is
generating a warning based on a plurality of warning thresholds determined for each warning content for a parameter of the storage battery state data from the mobile object state data, the storage battery state data, and weather data including an outside temperature received from the mobile object;
The warning is weighted according to the stage of the warning threshold and the outside temperature;
A battery remote monitoring system that calculates a warning importance level multiplied by the weighting or a cumulative importance level obtained by adding past warning importance levels to the warning importance level as a maintenance priority index. 2. The battery remote monitoring system according to claim 1, wherein the weighting is further made different between a first warning and a second or subsequent warning. (delete) (delete) 3. The battery remote monitoring system according to claim 1, wherein the warning importance and the accumulated importance are calculated for each moving body equipped with the storage battery or for each configuration of the moving body. The battery remote monitoring system of any one of claims 1, 2 or 5, characterized in that the external server estimates the cause of the warning based on the mobile object status data, the battery status data and the weather data, and presents a maintenance plan including measures corresponding to the maintenance priority index. In a battery remote monitoring system that includes a mobile object equipped with a battery and an external server,
The mobile object transmits mobile object status data and storage battery status data of the mobile object to an external server;
the external server generates a warning based on a plurality of warning thresholds determined for each warning content for a parameter of the storage battery state data from the mobile object state data, the storage battery state data, and weather data including an outside temperature received from the mobile object;
The warning is weighted according to the stage of the warning threshold and the outside temperature;
A battery remote monitoring method that calculates, as a priority index for maintenance, a warning importance level multiplied by the weighting or a cumulative importance level obtained by adding past warning importance levels to the warning importance level. 8. The method for remotely monitoring a storage battery according to claim 7, wherein the weighting is further made different between a first warning and a second or subsequent warning. (delete) (delete) A status monitoring device that monitors the status of various devices measured by a measuring device that measures the status of various devices mounted on the moving object;
a status data recording unit that records data on the status monitored by the status monitoring device;
A battery data recording unit that records data of a storage battery mounted on the moving object;
a communication device that transmits the data recorded in the status data recording unit and the storage battery data recording unit to an external server,
A mobile object connectable to the battery remote monitoring system according to any one of claims 1 to 6. 12. The mobile body according to claim 11, wherein outside air temperature data of the mobile body is recorded in either the state data recording unit or the storage battery data recording unit. 13. The mobile body according to claim 11, wherein the external server is capable of acquiring a maintenance plan including the detected warning, warning importance, cumulative importance, and measures to be taken.

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