Calculation system, battery degradation prediction method, and battery degradation prediction program

Abstract

A deterioration regression curve generating unit (113) performs curve regression of a plurality of SOHs specified in a time series for each battery (E1, 41), to generate a deterioration regression curve for each battery (E1, 41). A coefficient regression function generating unit (114) uses an average traveled distance or an average discharge amount per unit period for a plurality of electric-powered moving bodies (3) as an independent variable, and uses a deterioration coefficient of the deterioration regression curves of the plurality of batteries (E1, 41) as a dependent variable, to generate a regression function of the deterioration coefficient. A deterioration predicting unit (116) identifies an average traveled distance or an average discharge amount per unit period corresponding to an accepted change in traveling conditions, identifies the deterioration coefficient after the change in traveling conditions by applying the average traveled distance or the average discharge amount per unit period to the deterioration coefficient regression function, and uses the deterioration coefficient to change the deterioration regression curve of the batteries (E1, 41) installed in the electric-powered moving body (3).

Description

Technical Field

[0001] The present disclosure relates to an arithmetic system for predicting degradation of a battery mounted on an electric moving body, a method for predicting battery degradation, and a battery degradation prediction program.

Background Art

[0002] In recent years, hybrid vehicles (HV), plug-in hybrid vehicles (PHV), and electric vehicles (EV) have become widespread. These electric vehicles are equipped with secondary batteries such as lithium-ion batteries as key devices. Operators such as delivery operators (home delivery operators), bus operators, taxi operators, rental car operators, and car-sharing operators perform decisions on the replacement timing of electric vehicles and reviews of operation management based on predictions of degradation of secondary batteries mounted on a plurality of electric vehicles they manage.

[0003] Although it is assumed for stationary batteries, the following method has been proposed as a method for predicting battery degradation. In this method, usage conditions such as the state of charge (SOC), temperature, and current rate of the battery are classified, the time used in each classification is recorded when using a plurality of batteries, and the degradation coefficient for each classification is obtained to perform degradation prediction (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

[0005] Unlike stationary batteries, the battery mounted on an electric vehicle may have a large current change in a short time, so it is difficult to obtain a degradation coefficient for each classification. In addition, the method of obtaining a degradation coefficient for each classification complicates the arithmetic processing.

[0006] The present disclosure has been made in view of such circumstances, and an object thereof is to provide a technique for easily predicting the deterioration of a battery mounted on an electric moving body.

[0007] In order to solve the above problems, an arithmetic system according to an aspect of the present disclosure includes a data acquisition unit that acquires running data including battery data of batteries respectively mounted on a plurality of electric moving bodies, and battery data included in the acquired running data. Based on this, an SOH specifying unit that specifies the SOH of the battery mounted on each electric moving body, a deterioration regression curve generation unit that performs curve regression on a plurality of SOHs specified in time series for each battery to generate a deterioration regression curve for each battery, and the above-mentioned A coefficient regression function generation unit that generates a regression function of the deterioration coefficient, with the average running distance or average discharge amount per unit period of the plurality of electric moving bodies as an independent variable and the deterioration coefficient of the deterioration regression curves of the plurality of batteries as a dependent variable, and the set SOH to be the battery life, and a deterioration prediction unit that predicts the remaining life of a specific battery based on the deterioration regression curve of the battery, and a reception unit that receives a change in the running condition of the electric moving body on which the battery is mounted, which is input by the user. The deterioration prediction unit specifies the average running distance or average discharge amount per unit period according to the received change in the running condition, applies the average running distance or average discharge amount per unit period to the regression function of the deterioration coefficient to specify the deterioration coefficient after the change in the running condition, and uses the deterioration coefficient to change the deterioration regression curve of the battery mounted on the electric moving body.

[0008] Note that any combination of the above components, and those obtained by converting the expression of the present disclosure among a method, an apparatus, a system, a computer program, etc. are also effective as an aspect of the present disclosure.

[0009] According to the present disclosure, it is possible to easily predict the deterioration of a battery mounted on an electric moving body.

Brief Description of Drawings

[0010]

Figure 1

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Figure 8A

Figure 8B

Figure 9

Embodiments for Carrying Out the Invention

[0011] FIG. 1 is a diagram for explaining an arithmetic system 1 used by an operator according to an embodiment. The operator owns a plurality of electric vehicles 3 and conducts business by utilizing the plurality of electric vehicles 3. For example, the operator conducts a delivery business (home delivery business), a taxi business, a car rental business, or a car sharing business by utilizing the plurality of electric vehicles 3. In the present embodiment, a pure EV without an engine is assumed as the electric vehicle 3.

[0012] The computing system 1 is a system for managing the operations of an operator. The computing system 1 is composed of one or more information processing devices (e.g., servers, PCs). Some or all of the information processing devices constituting the computing system 1 may be located in a data center. For example, it may be composed of a combination of a server (owned server, cloud server, or rental server) in the data center and a client PC within the operator.

[0013] When multiple electric vehicles 3 are on standby, they are parked in the parking lot or garage of the operator's business office. The multiple electric vehicles 3 have a wireless communication function and can communicate wirelessly with the computing system 1. The multiple electric vehicles 3 transmit driving data including the operation data of the secondary battery they are equipped with to the computing system 1. When the electric vehicle 3 is in motion, it may wirelessly transmit the driving data to the server constituting the computing system 1 via the network. For example, the driving data may be transmitted each time at a frequency of once every 10 seconds. Also, at a predetermined timing once a day (e.g., at the end of business hours), the driving data for one day may be transmitted in a batch.

[0014] Also, when the computing system 1 is composed of an owned server or PC installed at the business office, after the electric vehicle 3 returns to the business office after business hours, it may transmit the driving data for one day to the owned server or PC. In that case, it may be transmitted wirelessly to the owned server or PC, or it may be connected to the owned server or PC by wire and transmitted via wire. Also, the data may be transmitted to the owned server or PC via a recording medium on which the driving data is recorded. Also, when the computing system 1 is composed of a combination of a cloud server and a client PC within the operator, the electric vehicle 3 may transmit the driving data to the cloud server via the client PC within the operator.

[0015] FIG. 2 is a diagram for explaining the detailed configuration of the battery system 40 mounted on the electric vehicle 3. The battery system 40 is connected to the motor 34 via the first relay RY1 and the inverter 35. During power running, the inverter 35 converts the DC power supplied from the battery system 40 into AC power and supplies it to the motor 34. During regeneration, the inverter 35 converts the AC power supplied from the motor 34 into DC power and supplies it to the battery system 40. The motor 34 is a three-phase AC motor and rotates according to the AC power supplied from the inverter 35 during power running. During regeneration, the rotational energy due to deceleration is converted into AC power and supplied to the inverter 35.

[0016] The first relay RY1 is a contactor inserted between the wirings connecting the battery system 40 and the inverter 35. During running, the vehicle control unit 30 controls the first relay RY1 to be in the on state (closed state) to electrically connect the battery system 40 and the power system of the electric vehicle 3. During non-running, the vehicle control unit 30 generally controls the first relay RY1 to be in the off state (open state) to electrically disconnect the battery system 40 and the power system of the electric vehicle 3. Note that other types of switches such as semiconductor switches may be used instead of the relay.

[0017] The battery system 40 can be charged from the commercial power system 9 by connecting it to the charger 4 installed outside the electric vehicle 3 with the charging cable 38. The charger 4 is connected to the commercial power system 9 and charges the battery system 40 in the electric vehicle 3 via the charging cable 38. In the electric vehicle 3, a second relay RY2 is inserted between the wirings connecting the battery system 40 and the charger 4. Note that other types of switches such as semiconductor switches may be used instead of the relay. The management unit 42 of the battery system 40 controls the second relay RY2 to be in the on state (closed state) before the start of charging and in the off state (open state) after the end of charging.

[0018] Generally, in the case of normal charging, it is charged with AC, and in the case of rapid charging, it is charged with DC. When charged with AC, the AC power is converted into DC power by an AC / DC converter (not shown) inserted between the second relay RY2 and the battery system 40.

[0019] The battery system 40 includes a battery module 41 and a management unit 42. The battery module 41 includes a plurality of cells E1 - En connected in series. Note that the battery module 41 may be configured by connecting a plurality of battery modules in series / series-parallel. As the cells, lithium-ion battery cells, nickel-metal hydride battery cells, lead battery cells, etc. can be used. Hereinafter, in this specification, an example of using lithium-ion battery cells (nominal voltage: 3.6 - 3.7V) is assumed. The number of series-connected cells E1 - En is determined according to the driving voltage of the motor 34.

[0020] A shunt resistor Rs is connected in series with the plurality of cells E1 - En. The shunt resistor Rs functions as a current detection element. Note that a Hall element may be used instead of the shunt resistor Rs. Also, a plurality of temperature sensors T1, T2 for detecting the temperatures of the plurality of cells E1 - En are installed in the battery module 41. One temperature sensor may be installed in the battery module, or one temperature sensor may be installed for each of the plurality of cells. For example, a thermistor can be used as the temperature sensors T1, T2.

[0021] The management unit 42 includes a voltage measurement unit 43, a temperature measurement unit 44, a current measurement unit 45, and a battery control unit 46. The nodes of the plurality of cells E1 - En connected in series and the voltage measurement unit 43 are connected by a plurality of voltage lines. The voltage measurement unit 43 measures the voltage of each cell E1 - En by measuring the voltage between each adjacent pair of voltage lines. The voltage measurement unit 43 transmits the measured voltage of each cell E1 - En to the battery control unit 46.

[0022] Since the voltage measurement unit 43 has a high voltage with respect to the battery control unit 46, the voltage measurement unit 43 and the battery control unit 46 are insulated from each other and connected by a communication line. The voltage measurement unit 43 can be composed of an ASIC (Application Specific Integrated Circuit) or a general-purpose analog front-end IC. The voltage measurement unit 43 includes a multiplexer and an A / D converter. The multiplexer outputs the voltages between two adjacent voltage lines to the A / D converter in order from the top. The A / D converter converts the analog voltage input from the multiplexer into a digital value.

[0023] The temperature measurement unit 44 includes a voltage-dividing resistor and an A / D converter. The A / D converter sequentially converts a plurality of analog voltages divided by a plurality of temperature sensors T1, T2 and a plurality of voltage-dividing resistors into digital values and outputs them to the battery control unit 46. The battery control unit 46 estimates the temperatures of the plurality of cells E1-En based on the digital values. For example, the battery control unit 46 estimates the temperatures of the cells E1-En based on the values measured by the temperature sensors closest to the cells E1-En.

[0024] The current measurement unit 45 includes a differential amplifier and an A / D converter. The differential amplifier amplifies the voltage across the shunt resistor Rs and outputs it to the A / D converter. The A / D converter converts the voltage input from the differential amplifier into a digital value and outputs it to the battery control unit 46. The battery control unit 46 estimates the current flowing through the plurality of cells E1-En based on the digital value.

[0025] If an A / D converter is mounted in the battery control unit 46 and an analog input port is provided in the battery control unit 46, the temperature measurement unit 44 and the current measurement unit 45 may output the analog voltage to the battery control unit 46 and convert it into a digital value by the A / D converter in the battery control unit 46.

[0026] The battery control unit 46 manages the states of the plurality of cells E1-En based on the voltages, temperatures, and currents of the plurality of cells E1-En measured by the voltage measurement unit 43, the temperature measurement unit 44, and the current measurement unit 45. The battery control unit 46 and the vehicle control unit 30 are connected by an in-vehicle network. As the in-vehicle network, for example, CAN (Controller Area Network) or LIN (Local Interconnect Network) can be used.

[0027] The battery control unit 46 can be composed of a microcomputer and a non-volatile memory (for example, EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory). An SOC-OCV (Open Circuit Voltage) map 46a is held in the microcomputer or the non-volatile memory. The SOC-OCV map 46a describes the characteristic data of the SOC-OCV curves of the plurality of cells E1-En. The SOC-OCV curves of the plurality of cells E1-En are pre-created by the battery manufacturer and registered in the microcomputer or the non-volatile memory at the time of shipment. The battery manufacturer conducts various tests to derive the SOC-OCV curves of the cells E1-En.

[0028] The battery control unit 46 estimates the SOC, FCC (Full Charge Capacity), and SOH of each of the plurality of cells E1-En. The battery control unit 46 estimates the SOC by the OCV method, the current integration method, or a combination of both. The OCV method is a method of estimating the SOC based on the OCV of each cell E1-En measured by the voltage measurement unit 43 and the characteristic data of the SOC-OCV curve described in the SOC-OCV map 46a. The current integration method is a method of estimating the SOC based on the OCV at the start of charge and discharge of each cell E1-En and the integrated value of the current measured by the current measurement unit 45. In the current integration method, as the charge and discharge time becomes longer, the measurement error of the current measurement unit 45 accumulates. Therefore, it is preferable to correct the SOC estimated by the current integration method using the SOC estimated by the OCV method.

[0029] The battery control unit 46 can estimate the FCC of the cell based on the characteristic data of the SOC-OCV curve described in the SOC-OCV map 46a and the OCVs of two points of the cell measured by the voltage measurement unit 43.

[0030] FIG. 3 is a diagram for explaining the method of estimating the FCC. The battery control unit 46 acquires the OCVs of two points of the cell. The battery control unit 46 refers to the SOC-OCV curve, specifies the SOCs of two points corresponding to the two voltages respectively, and calculates the difference ΔSOC between the SOCs of the two points. In the example shown in FIG. 3, the SOCs of the two points are 20% and 75%, and ΔSOC is 55%.

[0031] The battery control unit 46 calculates the integrated current amount (= charge and discharge capacity) Q during the period between the two times when the OCVs of the two points are acquired based on the change in the current measured by the current measurement unit 45. The battery control unit 46 can calculate the following (Equation 1) to estimate the FCC.

[0032] FCC = Q / ΔSOC ··· (Equation 1) SOH is defined as the ratio of the current FCC to the initial FCC, and the lower the value (the closer to 0%), the more the deterioration has progressed. The battery control unit 46 can calculate the following (Equation 2) to estimate the SOH.

[0033] SOH = current FCC / initial FCC ··· (Equation 2) Also, the SOH may be obtained by measuring the capacity by full charge and discharge, or may be obtained by adding up the storage deterioration and the cycle deterioration. The storage deterioration can be estimated based on the SOC, temperature, and storage deterioration rate. The cycle deterioration can be estimated based on the SOC range used, temperature, current rate, and cycle deterioration rate. The storage deterioration rate and the cycle deterioration rate can be derived in advance by experiments or simulations. The SOC, temperature, SOC range, and current rate can be obtained by measurement.

[0034] Also, SOH can be estimated based on the correlation with the internal resistance of the cell. The internal resistance can be estimated by dividing the voltage drop generated when a predetermined current flows through the cell for a predetermined time by the current value. The internal resistance has a relationship of decreasing as the temperature increases, and increasing as the SOH decreases.

[0035] The battery control unit 46 notifies the vehicle control unit 30 of the voltages, currents, temperatures, SOCs, FCCs, and SOHs of the plurality of cells E1 - En via the in-vehicle network. The vehicle control unit 30 generates driving data including battery data and vehicle data. The battery data includes the voltages, currents, and temperatures of the plurality of cells E1 - En. Note that for some battery systems 40, the battery data can include the SOC in addition to the voltage, current, and temperature. Furthermore, for some models, at least one of FCC and SOH can be included in addition to the voltage, current, temperature, and SOC. The vehicle data can include the average speed, driving distance, driving route, and the like.

[0036] The wireless communication unit 36 performs signal processing for wirelessly connecting to the network via the antenna 36a. In the present embodiment, the wireless communication unit 36 wirelessly transmits the driving data acquired from the vehicle control unit 30 to the computing system 1. As a wireless communication network to which the electric vehicle 3 can be wirelessly connected, for example, a mobile phone network (cellular network), wireless LAN, ETC (Electronic Toll Collection System), DSRC (Dedicated Short Range Communications), V2I (Vehicle-to-Infrastructure), V2V (Vehicle-to-Vehicle) can be used.

[0037] FIG. 4 is a diagram showing a configuration example of the arithmetic system 1 according to the embodiment. The arithmetic system 1 includes a processing unit 11, a storage unit 12, a display unit 13, and an operation unit 14. The processing unit 11 includes a data acquisition unit 111, an SOH specifying unit 112, a degradation regression curve generation unit 113, a coefficient regression line generation unit 114, an average travel distance specifying unit 115, a degradation prediction unit 116, an operation reception unit 117, and a display control unit 118. The functions of the processing unit 11 can be realized by the cooperation of hardware resources and software resources, or by hardware resources only. As hardware resources, a CPU, a GPU (Graphics Processing Unit), a ROM, a RAM, an ASIC, an FPGA (Field Programmable Gate Array), and other LSIs can be used. As software resources, programs such as an operating system and an application can be used.

[0038] The storage unit 12 includes a travel data holding unit 121, a driver data holding unit 122, an SOC-OCV characteristic holding unit 123, and a time-series SOH value holding unit 124. The storage unit 12 includes a non-volatile recording medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and records various programs and data.

[0039] The travel data holding unit 121 holds travel data collected from a plurality of electric vehicles 3 owned by an operator. The driver data holding unit 122 holds data of a plurality of drivers belonging to the operator. For example, for each driver, the cumulative travel distance for each electric vehicle 3 driven is managed.

[0040] The SOC-OCV characteristic holding unit 123 holds the SOC-OCV characteristics of a plurality of battery modules 41 mounted on a plurality of electric vehicles 3 owned by an operator. As the SOC-OCV characteristics of the battery module 41, those obtained from each electric vehicle 3 may be used, or those estimated based on the travel data collected from each electric vehicle 3 may be used.

[0041] In the latter case, the SOC-OCV characteristic estimation unit (not shown) of the processing unit 11 extracts a set of SOC and voltage (≈OCV) during a period in which the battery module 41 can be regarded as in a rest state from a set of SOC and voltage at a plurality of times included in the acquired battery data, and approximates the SOC-OCV characteristic based on the extracted multiple sets of SOC and OCV. Note that the SOC-OCV characteristic estimation unit may generate a common SOC-OCV characteristic of the battery module 41 of this type based on the set data of SOC and OCV acquired from a plurality of electric vehicles 3 equipped with the same type of battery module 41. Note that the SOC-OCV characteristic may be held in cell units.

[0042] The time-series SOH value holding unit 124 holds time-series data of SOH for each battery module 41. The time-series data of SOH is recorded, for example, once a day, once every few days, or once a week.

[0043] The display unit 13 includes a display such as a liquid crystal display or an organic EL display, and displays an image generated by the processing unit 11. The operation unit 14 is a user interface such as a keyboard, a mouse, or a touch panel, and receives an operation of the user of the arithmetic system 1.

[0044] The data acquisition unit 111 acquires running data including battery data of the battery modules 41 respectively mounted on the plurality of electric vehicles 3, and stores the acquired running data in the running data holding unit 121. The SOH specifying unit 112 specifies the SOH of the battery module 41 mounted on each electric vehicle 3 based on the battery data included in the running data acquired by the data acquisition unit 111. The SOH specifying unit 112 stores the specified SOH in the time-series SOH value holding unit 124.

[0045] When the acquired battery data includes SOH, the SOH specifying unit 112 can use the acquired SOH as it is. When the acquired battery data does not include SOH but includes voltage, current, temperature, and SOC, SOH can be calculated based on the above (Equation 1) and (Equation 2). That is, the SOH specifying unit 112 calculates the integrated current amount Q during the period between the two time points when two OCVs are acquired based on the transition of the current included in the battery data, and estimates the FCC by applying the calculated integrated current amount Q to the above (Equation 1). The SOH specifying unit 112 calculates the SOH by applying the calculated FCC to the above (Equation 2).

[0046] When neither SOC nor SOH is included in the acquired battery data, the SOH specifying unit 112 estimates the SOC by applying the voltage (≈OCV) during the period when the battery module 41 can be regarded as in a rest state to the SOC-OCV characteristic. Alternatively, the SOH specifying unit 112 estimates the SOC by integrating the current values for a certain period. The SOH specifying unit 112 calculates the SOH in the same manner as when the battery data includes SOC using the estimated SOC.

[0047] The degradation regression curve generation unit 113 performs curve regression on a plurality of SOHs specified in time series for each battery module 41 to generate a degradation regression curve for each battery module 41. For curve regression, for example, the least squares method can be used.

[0048] FIG. 5 is a diagram showing the degradation curve of a secondary battery in a graph. It is known that the degradation of a secondary battery progresses in proportion to the square root of time (0.5 power rule) as shown in the following (Equation 3).

[0049] SOH = w0 + w1√t ···(Equation 3) w0 is the initial value, and w1 is the degradation coefficient.

[0050] The degradation regression curve generation unit 113 obtains the degradation coefficient w1 of the above (Equation 3) by performing a 0.5 - power exponential curve regression with time t as the independent variable and SOH as the dependent variable. w0 is common and is usually set in the range of 1.0 to 1.1. When the actual initial capacity matches the nominal value, w0 = 1.0 is set. When the nominal value is set to the minimum guaranteed amount and is set lower than the actual initial capacity, a value greater than 1.0 is set.

[0051] The coefficient regression line generation unit 114 generates a regression line with the average driving distance per unit period of a plurality of electric vehicles 3 as the independent variable and the degradation coefficient w1 of the degradation regression curve of a plurality of battery modules 41 as the dependent variable. For linear regression, for example, the least - squares method can be used. Hereinafter, the average driving distance per day is assumed as the average driving distance per unit period.

[0052] When the average driving distance per day of each electric vehicle 3 is recorded in the driving data holding unit 121, the average driving distance specifying unit 115 can use the average driving distance recorded in the driving data holding unit 121. When the average driving distance per day of each electric vehicle 3 is not recorded in the driving data holding unit 121, the data acquisition unit 111 acquires the cumulative driving distance from each electric vehicle 3. The average driving distance specifying unit 115 divides the acquired cumulative driving distance of each electric vehicle 3 by the number of days from the start date of use of each electric vehicle 3 to calculate the average driving distance per day of each electric vehicle 3.

[0053] FIG. 6 is a diagram showing an example of a regression line with the average driving distance per day as the independent variable and the degradation coefficient w1 of the degradation curve of the battery module 41 as the dependent variable. In the example shown in FIG. 6, the regression line shown in the following (Equation 4) is generated.

[0054] y = - 0.003x - 0.2981 ···(Equation 4) The degradation prediction unit 116 predicts the remaining life of the battery module 41 based on the degradation regression curve of the battery module 41 generated by the degradation regression curve generation unit 113 and the remaining life and the SOH that should be the remaining life of the battery module 41. The SOH that should be the remaining life of the battery module 41 is preset by the battery manufacturer. For example, it may be set to 70%. Note that the SOH that should be the remaining life of the battery module 41 can be set and changed by the user from the operation unit 14.

[0055] The operation reception unit 117 receives a degradation prediction request for a specific battery module 41 input by the user. The degradation prediction unit 116 generates a degradation regression curve of the designated battery module 41 and predicts the remaining life of the battery module 41. The display control unit 118 causes the display unit 13 to display the predicted remaining life of the battery module 41.

[0056] The display control unit 118 can display the remaining life of the battery module 41 in terms of the remaining available period (e.g., the remaining available days) of the battery module 41. Also, when it is assumed that the electric vehicle 3 is used every day, the display control unit 118 can also display the remaining life of the battery module 41 in terms of the end date of use (XX / XX / XXXX) of the battery module 41. Further, the display control unit 118 can also display the remaining life of the battery module 41 in terms of the remaining travelable distance of the electric vehicle 3 on which the battery module 41 is mounted. The remaining travelable distance of the electric vehicle 3 can be calculated by multiplying the remaining available days of the battery module 41 mounted on the electric vehicle 3 by the average travel distance per day of the electric vehicle 3. Note that since the calculated end date of use, the remaining available days, and the remaining travelable distance are prediction results, the display control unit 118 may display them on the display unit 13 with a margin of about ±10% and present them to the user.

[0057] The above description is for the case where there is no change in the average travel distance per day of the electric vehicle 3. The user can change the driving conditions of the electric vehicle 3 on which the battery module 41 for which the remaining life is to be predicted is mounted from the operation unit 14.

[0058] The operation reception unit 117 receives a change in the driving conditions of the electric vehicle 3 equipped with the specific battery module 41 input by the user. For example, it receives a change in the average daily driving distance of the electric vehicle 3. The deterioration prediction unit 116 applies the received average daily driving distance to the coefficient regression line generated by the coefficient regression line generation unit 114. Thereby, the deterioration coefficient w1 after the change in the average driving distance can be obtained. The deterioration prediction unit 116 changes the deterioration regression curve of the battery module 41 mounted on the electric vehicle 3 using the obtained deterioration coefficient w1.

[0059] FIG. 7 is a diagram showing a specific example of the process of changing the deterioration regression curve of the specific battery module 41. The deterioration regression curve generation unit 113 generates a deterioration regression curve of the battery module 41 based on the SOH of the battery module 41 at a plurality of time points in the past from the prediction time point. Note that w0 of the deterioration regression curve (SOH = w0 + w1√t) shown in FIG. 7 is set to 1.05. This indicates that the actual initial capacity of the battery module 41 is larger than the nominal value.

[0060] The average driving distance specifying unit 115 acquires the average daily driving distance of each day of the electric vehicle 3 equipped with the battery module 41 from the driving data holding unit 121. The average driving distance specifying unit 115 calculates the average daily driving distance by summing the average daily driving distances of each day and dividing the total distance by the number of days of use. When the average daily driving distance of the electric vehicle 3 cannot be acquired, the average driving distance specifying unit 115 acquires the cumulative driving distance of the electric vehicle 3 and calculates the average daily driving distance by dividing the cumulative driving distance by the number of days of use. In the example shown in FIG. 7, the average daily driving distance of the electric vehicle 3 is 110.7 km / day.

[0061] FIGS. 8A and 8B are diagrams showing an example of the deterioration prediction simulation screen 13s displayed on the display unit 13. The deterioration prediction simulation screen 13s shown in FIGS. 8A and 8B includes a vehicle selection column 13a, an average driving distance column 13b, and a remaining available days column 13c.

[0062] The user selects the electric vehicle 3 for which the deterioration prediction simulation is to be performed from the vehicle selection column 13a. The deterioration regression curve generation unit 113 generates a deterioration regression curve of the battery module 41 mounted on the selected electric vehicle 3. The deterioration prediction unit 116 predicts the remaining available days of the battery module 41 based on the generated deterioration regression curve of the battery module 41. The average driving distance specifying unit 115 specifies the average driving distance of the selected electric vehicle 3.

[0063] As shown in FIG. 8A, the display control unit 118 causes the average driving distance of the selected electric vehicle 3 to be displayed in the average driving distance column 13b. The display control unit 118 causes the predicted remaining available days of the battery module 41 to be displayed in the remaining available days column 13c.

[0064] The user can change the average driving distance of the selected electric vehicle 3. The deterioration prediction simulation screen 13s shown in FIG. 8B shows a state where the average driving distance has been changed to 60.7 km / day. The deterioration prediction unit 116 applies the changed average driving distance to the coefficient regression line to obtain the deterioration coefficient w1 after the change in the average driving distance. When the average driving distance is changed from 110.7 km / day to 60.7 km / day, as shown in FIG. 6 above, the deterioration coefficient w1 moves from point a to point a'. The transition from point a to point a' uses the slope of the coefficient regression line. The change in the deterioration coefficient w1 corresponding to point a' is obtained by multiplying the change in the average driving distance (in the case of 110.7 km / day → 60.7 km / day, it is -50 km / day) by the slope of the regression line.

[0065] The deterioration prediction unit 116 overlaps the deterioration regression curve before the change in the average driving distance and the deterioration regression curve after the change at the position where the SOH at the prediction time matches. After the prediction time, the deterioration prediction unit 116 predicts the deterioration according to the deterioration regression curve after the change. In FIG. 7 above, when the average driving distance is 110.7 km / day, when it is changed to 60.7 km / day, when it is changed to 85.7 km / day, when it is changed to 135.7 km / day, and when it is changed to 160.7 km / day, five deterioration regression curves are drawn. It can be seen that the shorter the average driving distance is changed, the gentler the deterioration regression curve becomes, and the longer the life of the battery module 41 becomes.

[0066] The deterioration prediction unit 116 predicts the remaining available days of the battery module 41 based on the deterioration regression curve after the change in the average driving distance. The display control unit 118 causes the remaining available days of the predicted battery module 41 to be displayed in the remaining available days column 13c.

[0067] The user can utilize the deterioration prediction simulation results for predicting the replacement time of the electric vehicle 3 and the operation management of a plurality of electric vehicles 3. For example, in the case of a bus operator, by changing the operation route of the electric vehicle 3 with a short remaining available days to an operation route with a short driving distance and changing the operation route of the electric vehicle 3 with a long remaining available days to an operation route with a long driving distance, the replacement times of a plurality of electric vehicles 3 can be made uniform.

[0068] FIG. 9 is a flowchart showing the flow of the deterioration prediction process of the battery module 41 by the arithmetic system 1. The SOH specifying unit 112 specifies the SOH of the battery module 41 mounted on each electric vehicle 3 based on the battery data included in the driving data acquired from each electric vehicle 3 (S10). The deterioration regression curve generation unit 113 generates a deterioration regression curve of each battery module 41 based on the time-series SOH of each battery module 41 (S11). The coefficient regression straight line generation unit 114 generates a regression straight line of the deterioration coefficient w1 based on the average driving distance of a plurality of electric vehicles 3 and the deterioration coefficient w1 of the deterioration curves of a plurality of battery modules 41 (S12).

[0069] The deterioration prediction unit 116 calculates the remaining available days of the battery module 41 mounted on the electric vehicle 3 specified by the user from the deterioration regression curve of the battery module 41 (S13). The display control unit 118 causes the display unit 13 to display the average driving distance of the electric vehicle 3 specified by the average driving distance specifying unit 115 and the remaining available days of the battery module 41 mounted on the electric vehicle 3 (S14).

[0070] During the continuation of the deterioration prediction simulation (N in S15), the operation reception unit 117 can receive a change in the average driving distance of the electric vehicle 3 from the user (S16). The deterioration prediction unit 116 applies the received average driving distance to the regression line of the deterioration coefficient w1 to calculate a new deterioration coefficient w1 (S17). The process proceeds to step S13. The deterioration prediction unit 116 calculates the remaining available days of the battery module 41 from the deterioration regression curve after the change in the deterioration coefficient w1 (S13). The display control unit 118 causes the display unit 13 to display the changed average driving distance and the remaining available days (S14).

[0071] The processes of step S14 - step S17 are repeatedly executed (N in S15) until the deterioration prediction simulation ends (Y in S15).

[0072] In the above-described embodiment, the coefficient regression line generation unit 114 generated the regression line of the deterioration coefficient w1 using the average driving distance per day as the independent variable. In this regard, the coefficient regression line generation unit 114 may generate the regression line of the deterioration coefficient w1 using the average discharge amount per day as the independent variable. When the discharge history of the battery module 41 is recorded in the travel data holding unit 121, the average discharge amount may be used instead of the average driving distance. Also, when the past cumulative discharge amount can be obtained as battery data, the average discharge amount per day can be calculated by dividing the cumulative discharge amount by the number of days from the start date of use of the electric vehicle 3.

[0073] When a regression line of the degradation coefficient w1 is generated with the average daily discharge amount as an independent variable, the degradation prediction unit 116 converts the average daily driving distance received by the operation reception unit 117 into the average daily discharge amount per unit period based on the electricity cost [km / Wh] of the electric vehicle 3. The degradation prediction unit 116 can apply the converted average discharge amount to the regression line of the degradation coefficient w1 to obtain the changed degradation coefficient w1.

[0074] As described above, according to the present embodiment, by generating a regression function of the degradation coefficient w1 based on the degradation coefficients w1 of the degradation curves of the plurality of battery modules 41 mounted on the plurality of electric vehicles 3, the degradation of the battery module 41 mounted on the electric vehicle 3 can be easily predicted. For example, it is possible to easily estimate the change in the remaining life when the driving conditions are changed.

[0075] Also, in the present embodiment, a regression function of the degradation coefficient w1 is generated using the driving distance of the electric vehicle 3 recorded cumulatively. Degradation prediction can be performed intuitively using the driving distance, which is easy for the administrator of the electric vehicle 3 to understand and handle, as a parameter. The driving distance of the electric vehicle 3 recorded cumulatively is robust against data loss due to communication failures or human operation errors, etc., and even when the data interval is skipped, the average driving distance per unit period can be calculated. That is, even when only fragmentary driving data of the electric vehicle 3 remains, highly accurate degradation prediction can be performed.

[0076] As described above, past driving data is used when predicting the degradation of the electric vehicle 3, but in reality, sufficient driving data often does not remain as log data. There may be cases where data for several months is missing or abnormal values are included. Also, although there is an item for the cumulative driving distance in the log data of the electric vehicle 3, there may be no item for the cumulative discharge amount in the battery-related log data, and only instantaneous values during charging and discharging exist as items recorded for a short period.

[0077] In such a case, it is effective to use the cumulative mileage. Even for log data including several months of missing data, the start date of running can be estimated from the change amount of the cumulative mileage (mileage per day), and deterioration prediction can be performed based on the start date of running.

[0078] Although the electricity cost can be calculated from the relationship between the mileage and the charge / discharge current value, if the data includes abnormal values, the calculation result of the electricity cost may become unstable. On the other hand, although the mileage can be considered as an alternative parameter for the discharge electric energy amount, it is a general item in the running data of the electric vehicle 3, and since it is a monotonically increasing cumulative value, it is easy to detect abnormal values. Thus, it can be said that the mileage is a robust parameter for performing deterioration prediction.

[0079] In addition, since the deterioration prediction according to the present embodiment is not a model for obtaining a deterioration coefficient for each category of a plurality of usage conditions of the battery module 41, deterioration prediction can be performed by simple calculation. In the battery module 41 mounted on the electric vehicle 3, the current may change greatly irregularly in a short time, so it is often difficult to obtain a deterioration coefficient for each of a plurality of categories. Also, for the user, there is no need to input conditions other than the mileage, and the operation is easy.

[0080] As described above, the present disclosure has been described based on the embodiments. It is understood by those skilled in the art that the embodiments are examples, and various modifications are possible for each of the constituent elements and combinations of the respective processing processes, and such modifications are also within the scope of the present disclosure.

[0081] In the above-described embodiment, a regression function of the deterioration coefficient w1 was generated using the mileage per day as an explanatory variable. In this regard, in addition to the mileage per day, a regression function of the deterioration coefficient w1 may be generated by multiple regression analysis using a plurality of parameters including temperature, charge current rate, etc. as explanatory variables. In this case, the estimation accuracy of the deterioration coefficient w1 can be further improved.

[0082] As one item of deterioration prediction of the battery module 41, the occurrence of a rapid deterioration (hereinafter referred to as rapid deterioration or tertiary deterioration) of the capacity of the battery module 41 may be predicted.

[0083] When a usage method that places a large burden on the battery module 41, such as charging and discharging in a low-temperature or high-temperature environment or charging and discharging at a high rate, is repeated, rapid deterioration is likely to occur. When rapid deterioration occurs, the battery module 41 basically becomes unusable, so the life of the battery module 41 is shortened. The main factor in rapid deterioration is the decrease in the electrolyte, but to directly measure the amount of the electrolyte, it is necessary to disassemble the battery module 41. However, it is not practical to disassemble the battery module 41 during its use.

[0084] Therefore, an AC signal in a frequency band (for example, 100 Hz to 10 kHz) where the electrolyte reacts is applied from the outside of the battery module 41 to measure the AC impedance value of the battery module 41. Alternatively, the AC impedance value of the battery module 41 is estimated by measuring the transient response at the start or stop of charging and discharging of the battery module 41. The deterioration prediction unit 116 predicts the period until rapid deterioration of the battery module 41 occurs based on the measured or estimated AC impedance value. When it is predicted that rapid deterioration will occur in the battery module 41 mounted on the designated electric vehicle 3, the display control unit 118 displays the number of days until rapid deterioration occurs as the remaining life of the battery module 41.

[0085] In the above-described embodiment, an example of predicting the deterioration of the battery module 41 mounted on the electric vehicle 3 is assumed. In this regard, the electric vehicle 3 may be a two-wheeled electric motorcycle (electric scooter) or an electric bicycle. Also, the electric vehicle 3 includes low-speed electric vehicles 3 such as golf carts and land cars used in shopping malls and entertainment facilities. Further, the object on which the battery module 41 is mounted is not limited to the electric vehicle 3. For example, it also includes electric moving bodies such as electric ships, railway vehicles, and multicopters (drones).

[0086] Note that the embodiment may be specified by the following items.

[0087] [Item 1] A data acquisition unit (111) that acquires running data including data of batteries (E1, 41) respectively mounted on a plurality of electric moving bodies (3); An SOH specifying unit (112) that specifies the SOH (State Of Health) of the batteries (E1, 41) mounted on each electric moving body (3) based on the battery data included in the acquired running data; A deterioration regression curve generation unit (113) that performs curve regression on a plurality of SOHs specified in time series for each battery (E1, 41) to generate a deterioration regression curve for each battery (E1, 41); A coefficient regression function generation unit (114) that generates a regression function of the deterioration coefficient, with the average running distance or average discharge amount per unit period of the plurality of electric moving bodies (3) as an independent variable and the deterioration coefficient of the deterioration regression curves of the plurality of batteries (E1, 41) as a dependent variable; A deterioration prediction unit (116) that predicts the remaining life of a specific battery (E1, 41) based on the set SOH to be the life of the battery (E1, 41) and the deterioration regression curve of the specific battery (E1, 41); A reception unit (117) that receives a change in the running condition of the electric moving body (3) on which the battery (E1, 41) is mounted, which is input by the user, and The deterioration prediction unit (116) specifies the average running distance or average discharge amount per unit period corresponding to the received change in the running condition, applies the average running distance or average discharge amount per unit period to the regression function of the deterioration coefficient to specify the deterioration coefficient after the change in the running condition, and uses the deterioration coefficient to change the deterioration regression curve of the battery (E1, 41) mounted on the electric moving body (3). An arithmetic system (1) characterized by the above.

[0088] The battery (E1, 41) may be a cell E1 or a module 41.

[0089] According to this, the deterioration of the battery (E1, 41) mounted on the electric moving body (3) can be easily predicted.

[0090] [Item 2] The average mileage calculation unit (115) that calculates the average mileage per unit period of each electric moving body (3) based on the cumulative mileage obtained from each electric moving body (3) and the usage period of each electric moving body (3) is further provided. The arithmetic system (1) according to item 1, characterized in that.

[0091] According to this, by calculating the average mileage from the cumulative mileage that is easy to obtain as data and regressing the deterioration coefficient based on the average mileage, a highly versatile system can be constructed.

[0092] [Item 3] The coefficient regression function generation unit (114) generates a regression function of the deterioration coefficient, with the average discharge amount per unit period of the plurality of electric moving bodies (3) as an independent variable and the deterioration coefficient of the deterioration regression curve of the plurality of batteries (E1, 41) as a dependent variable. The reception unit (117) receives a change in the average mileage per unit period of the electric moving body (3) on which the battery (E1, 41) is mounted. The deterioration prediction unit (116) converts the received average mileage per unit period into an average discharge amount per unit period based on the electricity cost of the electric moving body (3). The arithmetic system (1) according to item 1, characterized in that.

[0093] According to this, even when the average discharge amount is used as a parameter for regressing the deterioration coefficient and the average mileage is used as a parameter for the user to change the conditions, deterioration prediction can be suitably performed.

[0094] [Item 4] The deterioration prediction unit (116) calculates at least one of the remaining usable period of the battery (E1, 41) and the remaining travelable distance of the electric moving body (3) on which the battery (E1, 41) is mounted as the remaining life of the battery (E1, 41) based on the changed deterioration regression curve of the battery (E1, 41), and causes the display unit to display it. The arithmetic system (1) according to any one of Items 1 to 3, characterized in that...

[0095] According to this, a user interface that is easy for the user to understand can be constructed.

[0096] [Item 5] The step of obtaining running data including data of batteries (E1, 41) respectively mounted on a plurality of electric moving bodies (3); The step of specifying the SOH of the batteries (E1, 41) mounted on each electric moving body (3) based on the battery data included in the obtained running data; The step of performing curve regression on a plurality of SOHs specified in time series for each battery (E1, 41) to generate a degradation regression curve for each battery (E1, 41); The step of generating a regression function of the degradation coefficient, with the average running distance or average discharge amount per unit period of the plurality of electric moving bodies (3) as the independent variable and the degradation coefficient of the degradation regression curves of the plurality of batteries (E1, 41) as the dependent variable; The step of predicting the remaining life of a specific battery (E1, 41) based on the set SOH to be the life of the battery (E1, 41) and the degradation regression curve of the specific battery (E1, 41); The step of receiving a change in the running condition of the electric moving body (3) on which the battery (E1, 41) is mounted, which is input by the user; Specifying the average running distance or average discharge amount per unit period according to the received change in the running condition, applying the average running distance or average discharge amount per unit period to the regression function of the degradation coefficient to specify the degradation coefficient after the change in the running condition, and using the degradation coefficient to change the degradation regression curve of the battery (E1, 41) mounted on the electric moving body (3); A method for predicting the degradation of a battery (E1, 41), characterized by comprising the above steps.

[0097] According to this, the degradation of the battery (E1, 41) mounted on the electric moving body (3) can be easily predicted.

[0098] [Item 6] A process of acquiring driving data including data of batteries (E1, 41) respectively mounted on a plurality of electric moving bodies (3); A process of specifying the SOH of the batteries (E1, 41) mounted on each electric moving body (3) based on the battery data included in the acquired driving data; A process of performing curve regression on a plurality of SOHs specified in time series for each battery (E1, 41) to generate a degradation regression curve for each battery (E1, 41); A process of generating a regression function of a degradation coefficient, where the average driving distance or average discharge amount per unit period of the plurality of electric moving bodies (3) is an independent variable and the degradation coefficient of the degradation regression curve of the plurality of batteries (E1, 41) is a dependent variable; A process of predicting the remaining life of a specific battery (E1, 41) based on the set SOH to be the life of the battery (E1, 41) and the degradation regression curve of the specific battery (E1, 41); A process of accepting a change in the driving condition of the electric moving body (3) on which the battery (E1, 41) is mounted, which is input by a user; Specifying the average driving distance or average discharge amount per unit period according to the accepted change in the driving condition, applying the average driving distance or average discharge amount per unit period to the regression function of the degradation coefficient to specify the degradation coefficient after the change in the driving condition, and changing the degradation regression curve of the battery (E1, 41) mounted on the electric moving body (3) using the degradation coefficient; A battery (E1, 41) degradation prediction program, characterized in that it causes a computer to execute the above.

[0099] According to this, the degradation of the battery (E1, 41) mounted on the electric moving body (3) can be easily predicted.

Explanation of Signs

[0100] 1 Calculation system, E1 - En cells, T1, T2 temperature sensors, RY1, RY2 relays, 3 Electric vehicle, 4 Charger, 11 Processing unit, 111 Data acquisition unit, 112 SOH determination unit, 113 Degradation regression curve generation unit, 114 Coefficient regression line generation unit, 115 Average driving distance determination unit, 116 Degradation prediction unit, 117 Operation reception unit, 118 Display control unit, 12 Memory unit, 121 Driving data holding unit, 122 Driver data holding unit, 123 SOC - OCV characteristic holding unit, 124 Time - series SOH value holding unit, 13 Display unit, 14 Operation unit, 30 Vehicle control unit, 34 Motor, 35 Inverter, 36 Wireless communication unit, 36a Antenna, 38 Charging cable, 40 Battery system, 41 Battery module, 42 Management unit, 43 Voltage measurement unit, 44 Temperature measurement unit, 45 Current measurement unit, 46 Battery control unit, 46a SOC - OCV map.

Claims

1. A data acquisition unit that acquires driving data including data of batteries respectively mounted on a plurality of electric moving bodies; An SOH identification unit that identifies the SOH (State Of Health) of the batteries mounted on each electric moving body based on the battery data included in the acquired driving data; A degradation regression curve generation unit that performs curve regression on a plurality of SOHs identified in time series for each battery to generate a degradation regression curve for each battery; A coefficient regression function generation unit that generates a regression function of the degradation coefficient, with the average driving distance or average discharge amount per unit period of the plurality of electric moving bodies as the independent variable and the degradation coefficients of the degradation regression curves of the plurality of batteries as the dependent variable; A degradation prediction unit that predicts the remaining life of a specific battery based on the set SOH to be the battery life and the degradation regression curve of the specific battery; A reception unit that receives a change in the driving condition of the electric moving body on which the battery is mounted, input by the user, and is provided with: The degradation prediction unit identifies the average driving distance or average discharge amount per unit period corresponding to the received change in the driving condition, applies the average driving distance or average discharge amount per unit period to the regression function of the degradation coefficient to identify the degradation coefficient after the change in the driving condition, and uses the degradation coefficient to change the degradation regression curve of the battery mounted on the electric moving body. An arithmetic system characterized by the above.

2. The arithmetic system according to claim 1, further comprising an average driving distance identification unit that calculates the average driving distance per unit period of each electric moving body based on the cumulative driving distance acquired from each electric moving body and the usage period of each electric moving body. The arithmetic system according to claim 1, characterized by the above.

3. The coefficient regression function generation unit generates the regression function of the degradation coefficient, with the average discharge amount per unit period of the plurality of electric moving bodies as the independent variable and the degradation coefficients of the degradation regression curves of the plurality of batteries as the dependent variable. The reception unit receives a change in the average driving distance per unit period of the electric moving body on which the battery is mounted. The degradation prediction unit converts the received average driving distance per unit period into an average discharge amount per unit period based on the electricity cost of the electric moving body. The arithmetic system according to claim 1, characterized by the above.

4. Based on the changed degradation regression curve of the battery, the degradation prediction unit calculates at least one of the remaining usable period of the battery and the remaining drivable distance of the electric moving body on which the battery is mounted as the remaining life of the battery, and causes the display unit to display it. The arithmetic system according to any one of claims 1 to 3, characterized in that...

5. A step of acquiring running data including data of batteries respectively mounted on a plurality of electric moving bodies; A step of specifying the SOH of the battery mounted on each electric moving body based on the battery data included in the acquired running data; A step of performing curve regression on a plurality of SOHs specified in chronological order for each battery to generate a degradation regression curve for each battery; A step of generating a regression function of the degradation coefficient, using the average running distance or average discharge amount per unit period of the plurality of electric moving bodies as an independent variable and the degradation coefficient of the degradation regression curves of the plurality of batteries as a dependent variable; A step of predicting the remaining life of a specific battery based on the set SOH to be the battery life and the degradation regression curve of the specific battery; A step of receiving a change in the running condition of the electric moving body on which the battery is mounted, which is input by the user; A step of specifying the average running distance or average discharge amount per unit period corresponding to the received change in the running condition, applying the average running distance or average discharge amount per unit period to the regression function of the degradation coefficient to specify the degradation coefficient after the change in the running condition, and using the degradation coefficient to change the degradation regression curve of the battery mounted on the electric moving body; A method for predicting battery degradation, characterized by comprising the above steps.

6. A process of acquiring running data including data of batteries respectively mounted on a plurality of electric moving bodies; A process of specifying the SOH of the battery mounted on each electric moving body based on the battery data included in the acquired running data; A process of performing curve regression on a plurality of SOHs specified in chronological order for each battery to generate a degradation regression curve for each battery; A process of generating a regression function of the degradation coefficient, using the average running distance or average discharge amount per unit period of the plurality of electric moving bodies as an independent variable and the degradation coefficient of the degradation regression curves of the plurality of batteries as a dependent variable; A process of predicting the remaining life of a specific battery based on the set SOH to be the battery life and the degradation regression curve of the specific battery; A process of receiving a change in the running condition of the electric moving body on which the battery is mounted, which is input by the user; Specify the average travel distance or average discharge amount per unit period in response to the received change in driving conditions, apply the average travel distance or average discharge amount per unit period to the regression function of the deterioration coefficient to specify the deterioration coefficient after the change in driving conditions, and use the deterioration coefficient to change the deterioration regression curve of the battery mounted on the electric moving body; A battery deterioration prediction program characterized by causing a computer to execute the above.

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