Battery diagnosis system, battery diagnosis method, and battery diagnosis program

Abstract

In the present invention, a first generation unit generates a predicted SOH curve of a battery pack on the basis of predicted battery state transition data and a deterioration map. A second generation unit performs, on the basis of an SOC difference between two points and a current integrated value obtained by referring to actual measurement data, curve regression on a plurality of SOHs estimated for respective sections of the actual measurement data, thereby generating a first actually measured SOH curve of the battery pack. A third generation unit generates a second actually measured SOH curve of the battery pack on the basis of the actual measurement data and the deterioration map. A determination unit determines that correction of at least one of the deterioration map and the predicted battery state transition data is necessary when the difference between the SOH at a certain time point on the predicted SOH curve and the SOH estimated from the actual measurement data at the time point exceeds a threshold value.

Description

Battery Diagnosis System, Battery Diagnosis Method, and Battery Diagnosis Program

[0001] The present disclosure relates to a battery diagnosis system, a battery diagnosis method, and a battery diagnosis program that predict the transition of a battery state and diagnose a battery based on prediction data.

[0002] More than 10 years have passed since EVs became widely commercially available, and the used EV market has been expanding. Generally, those considering purchasing or leasing a used EV place importance on the remaining service life of the used EV as a factor for judgment. The remaining service life of a used EV largely depends on the life of the battery pack installed. To accurately predict the life of the battery pack installed in a used EV, detailed driving and charging log data from the start of use is required. However, detailed driving and charging log data from the start of use basically does not remain and is difficult for used EV sellers, potential purchasers, etc. to obtain. Therefore, it is conceivable to predict the remaining life of the battery pack based on a charge-discharge plan after reuse and a degradation map representing battery characteristics.

[0003] Patent Document 1 discloses a battery diagnosis device that updates a diagnostic information database that associates and stores the operating state of a storage battery and the degradation risk of the storage battery based on the time-series operating information of the storage battery and the degradation rate of the storage battery, and diagnoses the degradation risk of the storage battery by referring to the diagnostic information database based on the operating state of the storage battery. However, there is no mention of the use of a charge-discharge plan.

[0004] Patent Document 2 discloses a method of estimating the remaining life based on the charge-discharge schedule of a storage battery stored in a charge-discharge schedule storage unit and changing the charge-discharge schedule of the storage battery stored in the charge-discharge schedule storage unit so that the life times of a plurality of storage batteries do not overlap. However, there is no mention of a degradation map.

[0005] Japanese Unexamined Patent Application Publication No. 2023 - 046058, Japanese Unexamined Patent Application Publication No. 2014 - 055896

[0006] To accurately predict the life of the battery pack installed in a used electric vehicle, both the accuracy of the charge-discharge plan and the accuracy of the degradation map need to be good.

[0007] This disclosure is made in light of these circumstances, and its purpose is to provide a technology for accurately predicting the degradation of battery packs installed in electric vehicles without using past driving and charging log data.

[0008] To solve the above problems, a battery diagnostic system in one aspect of the present disclosure includes: a first acquisition unit that acquires battery state prediction transition data of a battery pack installed in an electric vehicle, which is generated based on information declared by the user of the electric vehicle regarding the use of the electric vehicle before the electric vehicle is put into use; and parameters of the battery pack, including at least SOC (State Of Charge) and SOH (State Of The system includes: a second acquisition unit that acquires a degradation map defining the relationship with the amount of decrease in Health; a third acquisition unit that acquires time-series measured data including at least the current and SOC of the battery pack after the electric vehicle has started to be used; a first generation unit that generates a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; an SOH estimation unit that estimates the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; a second generation unit that generates a first measured SOH curve of the battery pack by performing curve regression on a plurality of SOH estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; a third generation unit that generates a second measured SOH curve of the battery pack based on the measured data and the degradation map; and a determination unit that determines that if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold, it is necessary to correct at least one of the degradation map or the battery state prediction transition data.

[0009] Furthermore, any combination of the above components, as well as any conversion of the expressions of this disclosure between devices, systems, methods, computer programs, etc., are also valid forms of this disclosure.

[0010] According to this disclosure, it is possible to predict with high accuracy the degradation of battery packs installed in electric vehicles without using past driving and charging log data.

[0011] This figure shows an overview of the used vehicle leasing system according to this embodiment. This figure is for explaining the battery diagnostic system according to this embodiment. Figure 3(a) is a figure showing a more detailed functional block of the battery state transition prediction unit. Figure 3(b) is a figure showing a more detailed functional block of the degradation transition prediction unit. This figure shows an example of a storage degradation map. Figure 5(a) is a figure showing an example of a charge degradation map. Figure 5(b) is a figure showing an example of a discharge degradation map. This figure shows an example of a battery state prediction transition over one week. This figure shows a concrete image of the FCC estimation method. This figure shows a graph of an example of a regression measured SOH curve of a drive battery pack. This figure shows an example of a predicted SOH curve. This figure shows an example of storage degradation transition and cycle degradation transition obtained by decomposing the regression measured SOH curve. This figure shows an example of storage degradation transition and cycle degradation transition showing the breakdown of the map measured SOH curve. This figure shows an example of storage degradation transition, charge degradation transition and discharge degradation transition showing the breakdown of the predicted SOH curve. This figure shows an example of storage degradation transition, charge degradation transition and discharge degradation transition showing the breakdown of the map measured SOH curve. This flowchart shows the flow of the battery state prediction transition data creation process by the battery diagnostic system according to this embodiment. This flowchart shows the flow of the predicted SOH curve generation and update process by the battery diagnostic system according to the embodiment. This flowchart shows a modified flow of the predicted SOH curve generation and update process by the battery diagnostic system according to the embodiment.

[0012] Figure 1 is a diagram showing an overview of the used vehicle leasing system 1 according to this embodiment. In this embodiment, the leasing company purchases used electric vehicles 2 that have been used once, and leases the purchased used electric vehicles 2 to a customer. In this embodiment, the customer is a corporation that leases multiple used electric vehicles 2 from the leasing company and uses them as company cars or delivery vehicles. The leasing company needs to decide which of the contracting corporation's business locations each of the multiple used electric vehicles 2 should be allocated to. In this embodiment, before deciding which business location each of the multiple used electric vehicles 2 should be allocated to, the leasing company diagnoses the condition of the drive battery pack installed in each electric vehicle 2 and predicts the remaining lifespan of each drive battery pack, thereby achieving the optimal allocation of the multiple used electric vehicles 2.

[0013] Figure 2 is a diagram illustrating a battery diagnostic system 10 according to an embodiment. The battery diagnostic system 10 is a system that is linked to or integrated with the used vehicle leasing system 1. The battery diagnostic system 10 may be built on a cloud server installed in a data center managed by a cloud service provider, or it may be built on a server installed in the leasing company's own facilities or data center.

[0014] The battery diagnostic system 10 comprises a control unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is a communication interface (e.g., NIC: Network Interface Card) for connecting to the network 5 by wire or wireless connection.

[0015] Network 5 is a general term for communication channels such as the Internet, dedicated lines, and VPNs (Virtual Private Networks), and does not specify the communication medium or protocol. Examples of communication mediums include mobile phone networks, wireless LANs, wired LANs, fiber optic networks, ADSL networks, and CATV networks. Examples of communication protocols include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).

[0016] User terminal device 3 is a terminal device used by a representative of the leasing company or an end user to access the battery diagnostic system 10, and can use a PC, tablet, smartphone, or the like.

[0017] The control unit 11 includes a declaration information acquisition unit 111, an electric vehicle information acquisition unit 112, a State of Health (SOH) acquisition unit 113, a battery state transition prediction unit 114, a prediction transition data acquisition unit 115, a measured data acquisition unit 116, a degradation map acquisition unit 117, and a degradation transition prediction unit 118.

[0018] Figure 3(a) shows a more detailed functional block diagram of the battery state transition prediction unit 114. The battery state transition prediction unit 114 includes an average speed estimation unit 114a, a charging current estimation unit 114b, a discharge current estimation unit 114c, a current transition prediction unit 114d, a temperature transition prediction unit 114e, a SOC transition prediction unit 114f, and a battery state prediction transition data output unit 114g.

[0019] Figure 3(b) shows a more detailed functional block diagram of the deterioration progression prediction unit 118. The deterioration progression prediction unit 118 includes a predicted SOH curve generation unit 118a, an SOH estimation unit 118b, a regression measurement SOH curve generation unit 118c, a map measurement curve generation unit 118d, a determination unit 118e, a correction request unit 118f, and a remaining life determination unit 118g.

[0020] The functions of the control unit 11 can be realized through the collaboration of hardware and software resources, or solely through hardware resources. A CPU, ROM, RAM, GPU (Graphics Processing Unit), NPU (Neural Network Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other LSIs can be used. Software resources such as operating systems and applications can be utilized.

[0021] The storage unit 12 includes a non-volatile recording medium such as an HDD or SSD, and stores various types of data. The storage unit 12 also includes a declaration information / SOH storage unit 121, a temperature map storage unit 122, and a degradation map storage unit 123. The degradation map storage unit 123 stores a storage degradation map, a charge degradation map, and a discharge degradation map as degradation maps for the drive battery pack.

[0022] The degradation map is a map that defines the relationship between parameters of the drive battery pack, including at least the State of Cooling (SOC), and the decrease in State of Health (SOH). Storage degradation of secondary batteries is a degradation that progresses over time depending on the temperature and SOC at each point in time. It progresses over time regardless of whether charging or discharging is in progress. Storage degradation mainly occurs due to the formation of a film (SEI (Solid Electrolyte Interphase) film) on the negative electrode. Generally, the higher the SOC at each point in time and the higher the temperature at each point in time, the faster the storage degradation rate.

[0023] The charge / discharge degradation of secondary batteries is a type of degradation that progresses as the number of charge / discharge cycles increases. This degradation is primarily caused by structural deterioration (wear, fracture, cracking, delamination, etc.) due to the expansion and contraction of the positive electrode active material. The rate of charge / discharge degradation depends on the state of charge (SOC) range, temperature, and current rate used. Generally, the rate of charge / discharge degradation increases at lower SOC ranges. Furthermore, the rate of charge / discharge degradation increases with higher current rates and temperatures.

[0024] Figure 4 shows an example of a storage degradation map. The storage degradation map represents the capacity degradation of a cell due to the growth of a coating (SEI film) formed on the negative electrode surface of the cell. The horizontal axis shows SOC [%], and the vertical axis shows the amount of storage degradation [% / √h]. The amount of storage degradation is defined as the decrease in SOH (ΔSOH / √h) per unit time. It is generally known that storage degradation progresses approximately linearly with respect to the square root law (0.5 power) of elapsed time (h). Therefore, the unit time is set to 0.5 power of 1 hour.

[0025] Figure 4 shows the storage degradation characteristics extracted and plotted at temperatures of 25°C and 45°C. The actual storage degradation map is described in a two-dimensional parameter space of temperature (°C) and SOC (%). For example, the unit width of temperature may be set to 1°C or 5°C, and the unit width of SOC may be set to 1%. In this way, the storage degradation map defines the relationship between the combination of SOC and temperature and the amount of SOH decrease per unit time.

[0026] Figure 5(a) shows an example of a charge degradation map. The horizontal axis shows the usage range of SOC [%], and the vertical axis shows the charge degradation amount [% / √Ah]. Figure 5(b) shows an example of a discharge degradation map. The horizontal axis shows the usage range of SOC [%], and the vertical axis shows the discharge degradation amount [% / √Ah]. The charge / discharge degradation map is a degradation map that represents the capacity degradation due to physical changes in the positive electrode active material caused by the expansion and contraction of the positive electrode active material due to the charging and discharging of the cell.

[0027] In the charge degradation map shown in Figure 5(a), the amount of charge degradation of a cell is defined as the decrease in SOH per unit charge (ΔSOH / √Ah). Generally, charge degradation is known to progress approximately linearly with respect to the square root of the cumulative charge (Ah). Therefore, the unit charge is set to 1Ah raised to the power of 0.5.

[0028] Figure 5(a) shows the extracted charge degradation characteristics for charge rates of 0.1C and 0.8C at room temperature, plotted on a graph. The actual charge degradation map is described in a three-dimensional parameter space of temperature (°C), SOC band (%), and charge rate (C). For example, the unit width of temperature may be set to 1°C or 5°C, the unit width of the SOC band to 10%, and the unit width of the charge rate to 0.1C. In this way, the charge degradation map defines the relationship between the combination of SOC, charge rate based on current, and temperature, and the amount of SOH decrease per unit charge.

[0029] In the discharge degradation map shown in Figure 5(b), the amount of cell discharge degradation is defined as the amount of SOH decrease per unit discharge (ΔSOH / √Ah). It is generally known that discharge degradation progresses approximately linearly with respect to the square root of the cumulative discharge amount (Ah). Therefore, the unit discharge amount is set to 1Ah raised to the power of 0.5.

[0030] Figure 5(b) shows the discharge degradation characteristics extracted and plotted at room temperature with charge rates of 0.1C and 0.8C. The actual discharge degradation map is described in a three-dimensional parameter space of temperature (°C), SOC band (%), and discharge rate (C). For example, the unit width of temperature may be set to 1°C or 5°C, the unit width of the SOC band to 10%, and the unit width of the discharge rate to 0.1C. In this way, the discharge degradation map defines the relationship between the combination of SOC, discharge rate based on current, and temperature, and the amount of SOH decrease per unit discharge.

[0031] The storage degradation characteristics, charging degradation characteristics, and discharging degradation characteristics are derived in advance through experiments and simulations conducted by a battery diagnostic service provider operating the battery diagnostic system 10, mapped, and stored in the degradation map holding unit 123. The battery diagnostic service provider obtains the drive battery pack installed in the electric vehicle 2 to be leased and derives the storage degradation characteristics, charging degradation characteristics, and discharging degradation characteristics of the drive battery pack for each vehicle model.

[0032] Returning to Figure 2, the Declaration Information Acquisition Unit 111 acquires user-declared information from the user terminal device 3, including the identification information of the electric vehicle 2, the planned value of the mileage traveled in a unit period, the planned value of the operating frequency in a unit period, the planned start and end times of operation when the vehicle is in operation, the identification information of the charger used to charge the electric vehicle 2, the planned value of the charging frequency in a unit period, and the planned start and end times of charging when the vehicle is in operation. The Declaration Information Acquisition Unit 111 stores the user-declared information acquired from the user terminal device 3 in the Declaration Information / SOH Holding Unit 121.

[0033] User-reported information is information based on questionnaires submitted by the contract holder. The leasing company's representative may input the questionnaire responses received from the contract holder as user-reported information from the user terminal device 3, or the contract holder may directly input the questionnaire responses as user-reported information from the user terminal device 3.

[0034] Examples of survey questions include the following: (1) Identification information of electric vehicle 2 (vehicle type and model) (2) Planned area of ​​use of electric vehicle 2 (prefecture) (3) Frequency of operation of electric vehicle 2 (operating days in a week) (4) Start time of operation per day (start time of driving) (5) End time of operation per day (end time of driving) (6) Daily driving distance (7) Average load capacity (weight) per operating day (8) Average number of occupants per operating day (9) Weektime highway driving time (10) Whether to use the air conditioner with the power on during breaks (11) Whether to use the lights with the power on during breaks (12) Start time of breaks per day (multiple entries possible *optional item) (13) End time of breaks per day (multiple entries possible *optional item) (14) Identification information of charger at sales base (model and type, *output power is also acceptable) (15) Frequency of charging at sales base (number of charges per week) (16) Start time of charging at sales base (17) End time of charging at sales base (18) To what percentage of SOC do you charge at sales offices? (19) Identification information of chargers other than sales offices (multiple answers allowed, model and type, *output power is also acceptable *optional item) (20) Frequency of charging outside sales offices (multiple answers allowed, number of charges per week *optional item) (21) Start time of charging outside sales offices (multiple entries allowed *optional item) (22) End time of charging outside sales offices (multiple entries allowed *optional item) (23) Charging duration outside sales offices (multiple entries allowed *optional item) (24) To what percentage of SOC do you charge at sales offices? (25) Is there an operational rule to charge when the SOC reaches XX%? (26) If there is such an operational rule, what is the SOC percentage? (27) Is there a set operational rule regarding the timing of charging when there are non-working days?

[0035] The electric vehicle information acquisition unit 112 accesses the vehicle manufacturer server 4 via the network 5. Based on the identification information of the electric vehicle 2 included in the user-reported information, the electric vehicle information acquisition unit 112 identifies the specifications table for the corresponding vehicle type and model, and acquires the data of the identified specifications table in, for example, CSV format. From the acquired specifications table data, the electric vehicle information acquisition unit 112 identifies the electric vehicle 2's energy consumption, the voltage (total voltage), and the capacity (total energy) of the drive battery pack installed in the electric vehicle 2.

[0036] Alternatively, a representative of the leasing company may manually input the power consumption of the electric vehicle 2, the voltage and capacity of the drive battery pack, etc., from the specifications table of the corresponding vehicle model and type, by browsing the vehicle manufacturer's server 4 website using the user terminal device 3. Information already obtained from user-reported information does not need to be obtained from the specifications table of the electric vehicle 2.

[0037] The SOH acquisition unit 113 acquires the current SOH of the electric vehicle 2, which is entered by the leasing company's representative from the user terminal device 3, and stores it in the declaration information / SOH storage unit 121. SOH is defined as the ratio of the current FCC (Full Charge Capacity) to the initial FCC (Full Charge Capacity), as shown in the following (Equation 1). A lower value (closer to 0%) indicates that deterioration is progressing. SOH = Current FCC / Initial FCC × 100 ... (Equation 1)

[0038] State of Health (SOH) can be measured by charging the drive battery pack from a completely discharged state to a fully charged state. In addition, some vehicle models display segment information indicating the degree of degradation of the drive battery pack on the driver's seat display.

[0039] The battery state transition prediction unit 114 generates battery state transition data for the drive battery pack installed in the electric vehicle 2, based on the user's declaration information regarding the use of the electric vehicle 2 before the start of use of the used electric vehicle 2. This will be explained in detail below.

[0040] The average speed estimation unit 114a estimates the average speed of the electric vehicle 2 based on the planned daily mileage and planned daily operating hours of the electric vehicle 2, which are included in the user-reported information. Daily operating hours are defined as the time between the start time and end time of operation for the day.

[0041] The charging current estimation unit 114b estimates the charging current Ic when charging the drive battery pack from the charger based on the output power of the charger specified based on the identification information of the charger and the voltage of the drive battery pack. Specifically, the charging current Ic is estimated by calculating the following (Equation 2). Ic = P / Vb... (Equation 2) P: Output power of the charger [W], Vb: Total voltage of the drive battery pack [V].

[0042] When the output power of the charger is not included in the user declaration information, the charger information acquisition unit (not shown) accesses the charger manufacturer server (not shown) via the network 5. The charger information acquisition unit acquires the output power included in the specifications table of the corresponding model based on the identification information of the charger included in the user declaration information. Note that a person in charge of the leasing business may browse the website of the charger manufacturer server from the user terminal device 3 and manually input the output power of the charger published in the specifications table of the corresponding model of the corresponding model.

[0043] The discharge current estimation unit 114c estimates the discharge current Id flowing from the drive battery pack based on the average speed of the electric vehicle 2, the electricity cost, and the voltage of the drive battery pack. Specifically, the discharge current Id is estimated by calculating the following (Equation 3). Id = (v / E × 1000) / Vb... (Equation 3) v: Average speed [km / h], E: Electricity cost [km / kWh]

[0044] The discharge current estimation unit 114c may correct the discharge current Ic flowing from the drive battery pack based on at least one of the predicted value of the average load of the electric vehicle 2 or the predicted value of the average number of passengers included in the user declaration information. More specifically, the discharge current estimation unit 114c corrects the electricity cost described in the specifications table based on the predicted value of the average load included in the user declaration information.

[0045] For example, the electricity cost calculated based on the AC power consumption rate test (JC08 mode) assumes a load weight of 110 kg. The discharge current estimation unit 114c corrects the total vehicle weight based on the difference between the predicted value of the average load included in the user declaration information and the load weight on which the electricity cost calculation is based. The discharge current estimation unit 114c corrects the electricity cost described in the specifications table based on the corrected total vehicle weight and the specifications of the drive motor.

[0046] Also, the discharge current estimation unit 114c corrects the electricity cost described in the specifications table based on the predicted value of the average number of passengers included in the user declaration information. For example, the electricity cost calculated based on the AC power consumption rate test (JC08 mode) assumes that two persons (the weight of one person is 55 kg) are on board. The discharge current estimation unit 114c corrects the total vehicle weight based on the difference between the predicted value of the average number of passengers included in the user declaration information and the number of passengers on which the electricity cost calculation is based. The discharge current estimation unit 114c corrects the electricity cost described in the specifications table based on the corrected total vehicle weight and the specifications of the drive motor.

[0047] The discharge current estimation unit 114c may correct the discharge current Ic flowing from the drive battery pack based on the predicted value of the highway driving time for one week included in the user declaration information. Generally, when driving at high speed, the air resistance increases and the electricity cost decreases. The discharge current estimation unit 114c reduces the electricity cost described in the specifications table based on the ratio of the highway driving time in the operating time for one week. The reduction rate of the electricity cost during the high-speed driving period can be obtained as follows. Prepare an electric vehicle 2 for data acquisition of the same vehicle type as the target electric vehicle 2 (hereinafter referred to as the test vehicle) and conduct a driving test, and obtain it from the experimental results. Also, obtain it by simulation based on the specifications table of the target electric vehicle 2.

[0048] The current transition prediction unit 114d predicts the current transition of the drive battery pack based on the estimated discharge current Id, the planned start and end times of operation within a unit period (e.g., one day) included in the user-declared information, the planned operating frequency, the estimated charging current Ic, the planned start and end times of charging in a single charge included in the user-declared information, and the planned charging frequency. For example, the current transition prediction unit 114d predicts the current transition of the drive battery pack over a one-week period as a charge-discharge plan.

[0049] The current transition prediction unit 114d predicts the discharge current Id during the break based on the planned start and end times of operation within a unit period included in the user-reported information, the user's response regarding whether or not to use the air conditioner in a powered-on state during the break, and the user's response regarding whether or not to use the lighting in a powered-on state during the break. In a vehicle design where power is supplied to the air conditioner from the drive battery pack, if the user has selected to use the air conditioner during the break, the current transition prediction unit 114d adds the air conditioner's current consumption to the discharge current Id during the break. In a vehicle design where power is supplied to the lighting from the drive battery pack, if the user has selected to use the lighting during the break, the current transition prediction unit 114d adds the lighting's current consumption to the discharge current Id during the break.

[0050] The current transition prediction unit 114d sets the current to 0 [A] during parking time (times other than driving time, rest time, and charging time).

[0051] The temperature transition prediction unit 114e predicts the temperature transition of the drive battery pack over a one-week period by referring to a temperature map. The temperature map is created in advance by the designer based on the current and temperature transitions obtained from driving tests and charging tests using a test vehicle. The temperature map may be described in a two-dimensional parameter space, for example, with current value and elapsed time as input variables and temperature rise value as output. Alternatively, it may be described in a three-dimensional parameter space, with current value, elapsed time and ambient temperature as input variables and temperature rise value as output. The designer creates a temperature map for each vehicle model and stores it in the temperature map holding unit 122.

[0052] The temperature transition prediction unit 114e reads the temperature map of the target electric vehicle 2 from the temperature map holding unit 122. The temperature transition prediction unit 114e refers to the read temperature map and predicts the temperature transition of the drive battery pack based on the current transition of the drive battery pack predicted by the current transition prediction unit 114d and the planned usage area included in the user-declared information.

[0053] The temperature change prediction unit 114e obtains the average temperature of the area to be used from a weather forecast site on the network 5. The temperature change prediction unit 114e may use the same average temperature for all time periods in a simplified manner, or it may use the average temperature trend for the day. The temperature change prediction unit 114e predicts the temperature trend after the charging and discharging of the drive battery pack is completed, assuming that the temperature of the drive battery pack will decrease to the ambient temperature according to Newton's law of cooling.

[0054] The SOC transition prediction unit 114f predicts the SOC transition for one week based on the integrated current value based on the current transition of the drive battery pack predicted by the current transition prediction unit 114d and the capacity of the drive battery pack. Specifically, it estimates the SOC [%] by calculating the following (Equation 4): SOC = ΣI × Δt / C ... (Equation 4) C: Total capacity of the drive battery pack [kWh].

[0055] The SOC transition prediction unit 114f predicts the SOC transition over a week, for example, based on the transition of the integrated current value from a fully charged state (SOC = 100%). The SOC is reset to 100% each time the battery is fully charged.

[0056] The battery state prediction transition data output unit 114g outputs battery state prediction transition data for a unit period (e.g., one week), including the predicted current transition, temperature transition, and SOC transition. This battery state prediction transition data can be used to predict the degradation of the drive battery pack.

[0057] Figure 6 shows an example of the predicted battery state changes over one week. It shows the changes in current, temperature, and SOC over one week. In Figure 6, discharge current is defined as positive and charge current as negative. When discharge current flows, SOC decreases, and when charge current flows, SOC increases. During periods when there is no charging or discharging (periods when current = 0 [A]), SOC remains at the same value.

[0058] The prediction trend data acquisition unit 115 acquires the battery state prediction trend data output from the battery state prediction trend data output unit 114g and inputs it to the degradation trend prediction unit 118. The actual measurement data acquisition unit 116 acquires time-series actual measurement data, including voltage, current, temperature, and SOC of the drive battery pack of the used electric vehicle 2 after it has been put into use, from the data logger 2a installed on the electric vehicle 2 and inputs it to the degradation trend prediction unit 118.

[0059] The data logger 2a is installed in at least one of the leased electric vehicles 2. The data logger 2a is connected to an in-vehicle network (e.g., CAN (Controller Area Network)) within the vehicle and periodically acquires and stores measured data from a BMU (Battery Management Unit) in the drive battery pack connected to the in-vehicle network.

[0060] The BMU estimates the State of Charge (SOC) by combining the Open Circuit Voltage (OCV) method and the current integration method. The OCV method estimates the SOC based on the OCV of the cells contained in the drive battery pack being measured and the SOC-OCV curve of the cells. The SOC-OCV curve of the cells is created in advance based on characteristic tests conducted by the battery manufacturer and registered in the BMU at the time of shipment.

[0061] The current integration method is a method for estimating the State of Charge (SOC) based on the OCV at the start of charging and discharging of the cell and the integrated value of the measured current. In the current integration method, measurement errors of the current accumulate as the charging and discharging time increases. Therefore, it is preferable to use a weighted average of the SOC estimated by the current integration method and the SOC estimated by the OCV method.

[0062] The BMU periodically (for example, at 10-second, 30-second, or 1-minute intervals) transmits battery data, including voltage, current, temperature, and SOC of the drive battery pack, to the data logger 2a via the in-vehicle network.

[0063] A representative of the battery diagnostic service provider reads the time-series measured data of the drive battery pack stored in the data logger 2a at predetermined intervals (for example, every three or six months), and inputs the read time-series measured data into the battery diagnostic system 10.

[0064] Alternatively, a transmitter may be used instead of the data logger 2a. The transmitter periodically acquires actual measurement data from the BMU in the drive battery pack connected to the in-vehicle network and transmits the acquired actual measurement data to a cloud server via a wireless network. In this case, the time-series actual measurement data is stored on the cloud server. In this case, the actual measurement data acquisition unit 116 acquires the time-series actual measurement data from the cloud server and inputs it to the degradation progression prediction unit 118.

[0065] The degradation map acquisition unit 117 acquires the storage degradation map, charge degradation map, and discharge degradation map of the drive battery pack installed in the target vehicle from the degradation map holding unit 123, and inputs them to the degradation progression prediction unit 118.

[0066] The degradation progression prediction unit 118 simulates the remaining lifespan of the drive battery pack installed in the used electric vehicle 2. This will be explained in detail below. The predicted SOH curve generation unit 118a generates battery state prediction progression data for multiple years (for example, 5 to 10 years), assuming that the weekly SOC and temperature changes included in the battery state prediction progression data predicted by the battery state progression prediction unit 114 are repeated over multiple years. Based on the battery state prediction progression data of the drive battery pack installed in the target used electric vehicle 2 and the degradation map, the predicted SOH curve generation unit 118a generates a predicted SOH curve for the drive battery pack.

[0067] The predicted SOH curve generation unit 118a generates a predicted SOH curve for the drive battery pack after secondary use begins, based on battery state prediction transition data, a degradation map, and the SOH of the drive battery pack at the start of secondary use, before secondary use of the electric vehicle 2 begins. The initial value of the predicted SOH curve is not 100%, but the SOH of the drive battery pack at the start of secondary use. The SOH of the drive battery pack at the start of secondary use for each electric vehicle 2 is stored in the declared information / SOH holding unit 121. For example, if the SOH at the start of secondary use is 80%, the predicted SOH curve generation unit 118a generates a predicted SOH curve for SOH = 80% and above.

[0068] The predicted SOH curve generation unit 118a reduces the SOH by an amount obtained by referring to a storage degradation map based on the SOC and temperature during the unit period, each time a unit of time has elapsed. The predicted SOH curve generation unit 118a reduces the SOH by an amount obtained by referring to a charge degradation map based on the SOC, charge rate, and temperature during that period, each time a unit of charge amount increases. The predicted SOH curve generation unit 118a reduces the SOH by an amount obtained by referring to a discharge degradation map based on the SOC, discharge rate, and temperature during that period, each time a unit of discharge amount increases. The predicted SOH curve generation unit 118a generates a predicted SOH curve by summing up the amount of SOH reduction due to storage degradation, the amount of SOH reduction due to charge degradation, and the amount of SOH reduction due to discharge degradation.

[0069] Furthermore, when the temperature trend prediction unit 114e predicts the temperature trend of the drive battery pack over a one-week period, it may predict the temperature trend for each season based on the average temperature of each season in the area where the battery pack is intended to be used. In this case, a predicted SOH curve can be generated based on the temperature trend that takes seasonality into account.

[0070] The SOH estimation unit 118b estimates the State of Health (SOH) of the drive battery pack based on the SOC difference between two points and the integrated current value obtained by referring to the time-series measured data acquired from the data logger 2a. The SOH estimation unit 118b determines the SOC difference (ΔSOC) between the SOC of the first resting state and the SOC of the second resting state based on the voltage of the drive battery pack in the first resting state and the voltage of the second resting state and the SOC-OCV curve. The SOH estimation unit 118b determines the integrated current value Q for the period between the first resting state and the second resting state based on the current value included in the measured data. The SOH estimation unit 118b determines the current FCC of the drive battery pack based on the integrated current value Q and ΔSOC. The SOH estimation unit 118b calculates the SOH based on the ratio of the current FCC of the drive battery pack to the initial FCC.

[0071] Figure 7 shows a concrete image of the FCC estimation method. The SOH estimation unit 118b identifies the voltages at two points, the first rest state and the second rest state, and sets them as the OCVs at two points. The SOH estimation unit 118b refers to the SOC-OCV curve to identify the two SOCs corresponding to the two OCVs, and calculates the ΔSOC between the two SOCs.

[0072] The SOH estimation unit 118b calculates the integrated current (= charge / discharge capacity) Q between two points from which OCV values ​​have been obtained. The SOH estimation unit 118b estimates the FCC by calculating the following (Equation 5): FCC = Q / ΔSOC ... (Equation 5)

[0073] The SOH estimation unit 118b estimates the SOH by calculating the ratio of the current FCC to the initial FCC, as shown in (Equation 1) above. The SOH estimation unit 118b calculates the SOH at regular intervals (for example, every month, every two weeks, or every week) using the two-point OCV method based on the time-series measured data.

[0074] The regression measurement SOH curve generation unit 118c performs curve regression on multiple SOHs estimated for each interval of time-series measurement data by the SOH estimation unit 118b to generate the regression measurement SOH curve of the drive battery pack (SOH = m + K × X ｆ This generates a curve regression, for example, using the least squares method.

[0075] Figure 8 is a graph showing an example of a regression measured SOH curve for a drive battery pack. The horizontal axis represents elapsed time, and the vertical axis represents SOH. Assuming that the SOH of the secondary battery decreases in proportion to the square root of elapsed time (root law, 0.5 power law), the regression measured SOH curve can be defined as follows (Equation 6): SOH = m + K√t ... (Equation 6) where m is the initial value and K is the degradation coefficient.

[0076] The regression measurement SOH curve generation unit 118c determines the degradation coefficient K in (Equation 6) above by exponential regression to the power of 0.5, with elapsed time as the independent variable and SOH as the dependent variable. m is common and is usually set in the range of 1.0 to 1.1. If the actual initial capacity matches the nominal value, m is set to 1.0. If 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.

[0077] The map measurement curve generation unit 118d generates a map measurement SOH curve for the drive battery pack based on the time-series measurement data acquired from the data logger 2a and the degradation map. The map measurement curve generation unit 118d reduces the SOH by an amount obtained by referring to the storage degradation map based on the SOC and temperature during the unit period as a unit of time elapses. The map measurement curve generation unit 118d reduces the SOH by an amount obtained by referring to the charge degradation map based on the SOC, charge rate, and temperature during the period as a unit of charge increases as a unit of discharge increases as a unit of discharge increases as the SOC, discharge rate, and temperature during the period are referred to. The map measurement curve generation unit 118d generates a map measurement SOH curve by summing the amount of SOH reduction due to storage degradation, the amount of SOH reduction due to charge degradation, and the amount of SOH reduction due to discharge degradation.

[0078] The determination unit 118e calculates the difference between the SOH at a certain point in time on the predicted SOH curve generated by the predicted SOH curve generation unit 118a and the SOH estimated by the SOH estimation unit 118b from the actual measurement data at that point in time.

[0079] Figure 9 shows an example of a predicted SOH curve. In the example shown in Figure 9, secondary use begins approximately three years after the start of use of the drive battery pack. The determination unit 118e determines the error between the SOH on the predicted SOH curve and the SOH estimated from the measured data, approximately five years after the start of use of the drive battery pack.

[0080] If the error in SOH is large, two possible causes are considered: (1) The battery state prediction data generated as part of the charge / discharge plan deviates from the actual operational status of the electric vehicle 2. (2) The battery degradation characteristics defined in the degradation map do not match the degradation characteristics of the target drive battery pack. In order to predict the remaining lifespan of the drive battery pack with high accuracy, it is necessary to identify the factors causing the large error in SOH and take corrective measures.

[0081] The determination unit 118e determines that if the difference in SOH at a certain point in time exceeds a threshold (for example, about 5%), it is necessary to correct at least one of the degradation map or the battery state prediction transition data.

[0082] The determination unit 118e calculates a first deviation between the measured regression SOH curve and the measured map SOH curve. The determination unit 118e calculates the first deviation by, for example, calculating the following (Equation 7). The predetermined period is set to, for example, one month. In this embodiment, since the measured regression SOH curve is considered the true value, the deviation normalized by the area of ​​the measured regression SOH curve is used.

[0083] First deviation = Σ|(difference in area between the measured regression SOH curve and the measured map SOH curve for each predetermined period)| / Σ(area of ​​the measured regression SOH curve for each predetermined period) ... (Equation 7)

[0084] The determination unit 118e determines that if the first deviation is greater than the first set value, the main reason the SOH difference exceeds the threshold is due to an error in the degradation map. The correction request unit 118f notifies the developer of the battery diagnostic system 10 of a request to correct the degradation map. The developer verifies the regression measured SOH curve, the map measured SOH curve, and the degradation map, and corrects the degradation map.

[0085] The determination unit 118e determines that if the first deviation is less than or equal to the first set value, the main reason the SOH difference exceeds the threshold is due to an error in the battery state prediction transition data. The correction request unit 118f notifies the developer of a request to correct the battery state prediction transition data. The developer verifies the battery state prediction transition data, the predicted SOH curve, and the map measured SOH curve, and corrects the battery state prediction transition data. If possible, the developer may also conduct a follow-up survey of the contractors who have leased the electric vehicle 2 to ask if there have been any changes in the usage of the electric vehicle 2. If information from the follow-up survey is obtained from the contractors, the developer corrects the battery state prediction transition data based on the answers to the new survey.

[0086] Even if the first deviation is less than or equal to the first set value, if the first deviation is greater than the second set value (second set value < first set value), the correction request unit 118f may also notify the developer of a request to correct the degradation map. If the second set value is set to zero, unless the regression measured SOH curve and the map measured SOH curve do not match, if the first deviation is less than or equal to the first set value, corrections will be required for both the degradation map and the battery state prediction transition data.

[0087] Instead of calculating a first deviation between the regression measured SOH curve and the map measured SOH curve, the determination unit 118e may calculate a second deviation between the battery state prediction transition data and the time-series measured data.

[0088] If the second deviation is greater than the third set value, the determination unit 118e determines that the main reason the SOH difference exceeds the threshold is due to errors in the battery state prediction transition data. The correction request unit 118f notifies the developer of a request to correct the battery state prediction transition data. If the second deviation is less than or equal to the third set value, the determination unit 118e determines that the main reason the SOH difference exceeds the threshold is due to errors in the degradation map. The correction request unit 118f notifies the developer of a request to correct the degradation map.

[0089] Even if the second deviation is greater than the third setting value, if the second deviation is less than or equal to the fourth setting value (fourth setting value > third setting value), the correction request unit 118f may also notify the developer of a request to correct the degradation map.

[0090] If the determination unit 118e determines that correction of the degradation map is necessary, it decomposes the regression measured SOH curve into a storage degradation progression according to the passage of time and a cycle degradation progression according to the cumulative charge and discharge amount, and generates a breakdown of the map measured SOH curve into a storage degradation progression according to the passage of time and a cycle curve according to the cumulative charge and discharge amount. The determination unit 118e calculates the error between the storage degradation progression obtained from the regression measured SOH curve and the storage degradation progression that constitutes the map measured SOH curve. The determination unit 118e calculates the error between the cycle degradation progression obtained from the regression measured SOH curve and the cycle degradation progression that constitutes the map measured SOH curve.

[0091] Figure 10 shows an example of storage degradation and cycle degradation trends obtained by decomposing the regression-measured SOH curve. The storage degradation trend and cycle degradation trend can be determined, for example, as follows: The storage degradation trend is assumed to decrease according to a value obtained by multiplying the elapsed time to the power of 0.5 by a constant a, and the cycle degradation trend is assumed to decrease according to a value obtained by multiplying the elapsed time by a constant b. The determination unit 118e determines the constants a and b such that the sum of the storage degradation trend and the cycle degradation trend best fits the regression-measured SOH curve shown in (Equation 6) above. In this way, the regression-measured SOH curve can be decomposed into a storage degradation trend and a cycle degradation trend.

[0092] Figure 11 is a diagram showing an example of storage degradation progression and cycle degradation progression, illustrating the breakdown of the measured SOH curve on the map. As described above, the measured map curve generation unit 118d, based on time-series measured data, reduces the amount of SOH obtained by referring to the storage degradation map, using the SOC and temperature spent in the unit period as parameters, each time a unit of time has elapsed. The determination unit 118e generates the storage degradation progression by accumulating the amount of SOH reduction for each unit period.

[0093] The map measurement curve generation unit 118d reduces the amount of SOH obtained by referring to the charge degradation map using the SOC, charge rate, and temperature for that period as parameters, based on time-series measurement data, each time the unit charge amount increases. The determination unit 118e generates a charge degradation trend by accumulating the amount of SOH reduction for each unit charge amount. The map measurement curve generation unit 118d reduces the amount of SOH obtained by referring to the discharge degradation map using the SOC, discharge rate, and temperature for that period as parameters, based on time-series measurement data, each time the unit discharge amount increases. The determination unit 118e generates a discharge degradation trend by accumulating the amount of SOH reduction for each unit discharge amount. The determination unit 118e generates a cycle degradation trend by adding the charge degradation trend and the discharge degradation trend, and converts the generated cycle degradation trend into a time-axis cycle degradation trend.

[0094] The correction request unit 118f notifies the developer of a correction request for the storage degradation map if the error between the storage degradation trend obtained from the regression measured SOH curve and the storage degradation trend that constitutes the map measured SOH curve is greater than the fifth setting value. The correction request unit 118f notifies the developer of a correction request for the charge degradation map and discharge degradation map if the error between the cycle degradation trend obtained from the regression measured SOH curve and the cycle degradation trend that constitutes the map measured SOH curve is greater than the sixth setting value.

[0095] If the determination unit 118e determines that correction of the battery state prediction transition data is necessary, it generates a breakdown of the predicted SOH curve, including a storage degradation transition according to the passage of time, a charge degradation transition according to the cumulative charge amount, and a discharge degradation transition according to the cumulative discharge amount, and generates a breakdown of the map SOH curve, including a storage degradation transition according to the passage of time, a charge degradation transition according to the cumulative charge amount, and a discharge degradation transition according to the cumulative discharge amount. The determination unit 118e calculates the error between the storage degradation transition that makes up the predicted SOH curve and the storage degradation transition that makes up the map measured SOH curve. The determination unit 118e calculates the error between the charge degradation transition that makes up the predicted SOH curve and the charge degradation transition that makes up the map measured SOH curve. The determination unit 118e calculates the error between the discharge degradation transition that makes up the predicted SOH curve and the discharge degradation transition that makes up the map measured SOH curve.

[0096] Figure 12 is a diagram showing an example of storage degradation trends, charge degradation trends, and discharge degradation trends, which are components of the predicted SOH curve. As described above, the predicted SOH curve generation unit 118a reduces the amount of SOH obtained by referring to the storage degradation map, using the SOC and temperature that remained during the unit period as parameters, based on the battery state prediction trend data, each time a unit of time has elapsed. The determination unit 118e generates the storage degradation trend by accumulating the amount of SOH reduction for each unit period.

[0097] The predicted SOH curve generation unit 118a reduces the SOH by an amount obtained by referring to a charge degradation map using the SOC, charge rate, and temperature for that period as parameters, based on the battery state prediction transition data, each time the unit charge amount increases. The determination unit 118e generates a charge degradation transition by accumulating the amount of SOH reduction for each unit charge amount. The predicted SOH curve generation unit 118a reduces the SOH by an amount obtained by referring to a discharge degradation map using the SOC, discharge rate, and temperature for that period as parameters, based on the battery state prediction transition data, each time the unit discharge amount increases. The determination unit 118e generates a discharge degradation transition by accumulating the amount of SOH reduction for each unit discharge amount.

[0098] Figure 13 is a diagram showing an example of storage degradation trends, charging degradation trends, and discharging degradation trends, which are components of the measured SOH curve on the map. As described above, the measured map curve generation unit 118d reduces the amount of SOH obtained by referring to the storage degradation map, using the SOC and temperature that remained during the unit period as parameters, based on the time-series measured data, each time a unit of time has elapsed. The determination unit 118e generates the storage degradation trend by accumulating the amount of SOH reduction for each unit period.

[0099] The map measurement curve generation unit 118d reduces the SOH by an amount obtained by referring to a charge degradation map using the SOC, charge rate, and temperature for that period as parameters, based on time-series measurement data, each time the unit charge amount increases. The determination unit 118e generates a charge degradation trend by accumulating the amount of SOH reduction for each unit charge amount. The map measurement curve generation unit 118d reduces the SOH by an amount obtained by referring to a discharge degradation map using the SOC, discharge rate, and temperature for that period as parameters, based on time-series measurement data, each time the unit discharge amount increases. The determination unit 118e generates a discharge degradation trend by accumulating the amount of SOH reduction for each unit discharge amount.

[0100] If the error between the storage degradation transition that makes up the predicted SOH curve and the storage degradation transition that makes up the map-measured SOH curve is greater than the seventh setting value, the correction request unit 118f notifies the developer of a request to correct the pause interval of the battery state prediction transition data. If the error between the charging degradation transition that makes up the predicted SOH curve and the charging degradation transition that makes up the map-measured SOH curve is greater than the eighth setting value, the correction request unit 118f notifies the developer of a request to correct the charging interval of the battery state prediction transition data. If the error between the discharge degradation transition that makes up the predicted SOH curve and the discharge degradation transition that makes up the map-measured SOH curve is greater than the ninth setting value, the correction request unit 118f notifies the developer of a request to correct the discharge interval of the battery state prediction transition data.

[0101] The values ​​for the first deviation-second deviation and the first-to-ninth setting values ​​mentioned above are set by the developers, taking into account their knowledge, experiments, and simulation results.

[0102] The remaining life determination unit 118g determines the remaining life of the drive battery pack based on the predicted SOH curve generated by the predicted SOH curve generation unit 118a. The predicted SOH curve generation unit 118a determines the remaining life of the drive battery pack (i.e., the end of use timing for the used electric vehicle 2) to be, for example, the earlier of either four years having passed since the start of secondary use of the used electric vehicle 2, or the timing when the SOH of the drive battery pack reaches 60%.

[0103] In this embodiment, at least one of the battery state prediction transition data or the degradation map is modified as appropriate. The predicted SOH curve generation unit 118a generates a predicted SOH curve each time at least one of the battery state prediction transition data or the degradation map is modified. The remaining life determination unit 118g re-determines the remaining life of the drive battery pack each time the predicted SOH curve is updated.

[0104] Figure 14 is a flowchart showing the flow of the battery state prediction transition data creation process by the battery diagnostic system 10 according to the embodiment. The declaration information acquisition unit 111 acquires user declaration information from the user terminal device 3 (S10). The electric vehicle information acquisition unit 112 acquires specification information of the electric vehicle 2 based on the identification information of the electric vehicle 2 included in the user declaration information (S11).

[0105] The battery state transition prediction unit 114 sets the operating days and non-operating days for one week based on the planned operating frequency of the electric vehicle 2 included in the user-declared information. Based on the user-declared information and the specification information of the electric vehicle 2, the battery state transition prediction unit 114 creates a battery state transition prediction for the operating days (S12). Specifically, the current transition prediction unit 114d predicts the current transition of the drive battery pack on the operating days, using the charging time, discharge time, driving distance, charger output power obtained from the user-declared information, the energy consumption included in the specification information of the electric vehicle 2, and the voltage and capacity of the drive battery pack as basic data. The temperature transition prediction unit 114e predicts the temperature transition of the drive battery pack on the operating days based on the temperature map, the planned usage area of ​​the electric vehicle 2 included in the user-declared information, and the predicted current transition of the drive battery pack on the operating days. The SOC transition prediction unit 114f predicts the SOC transition of the drive battery pack on the operating days based on the predicted current transition of the drive battery pack on the operating days and the capacity of the drive battery pack included in the specification information of the electric vehicle 2.

[0106] The battery state transition prediction unit 114 creates a battery state transition prediction for non-operating days based on user-reported information (S13). Specifically, the temperature transition prediction unit 114e predicts the temperature transition of the drive battery pack on non-operating days based on the planned usage area of ​​the electric vehicle 2 included in the user-reported information and the end value of the temperature transition on the immediately preceding operating day.

[0107] The battery state prediction trend data output unit 114g integrates the current trend of the drive battery pack on operating days and the current trend of the drive battery pack on non-operating days to create one week's worth of battery state prediction trend data, and outputs it to the degradation trend prediction unit 118 (S14).

[0108] Figure 15 is a flowchart showing the flow of the predicted SOH curve generation and update process by the battery diagnostic system 10 according to the embodiment. The predicted SOH curve generation unit 118a generates a predicted SOH curve for the drive battery pack based on the battery state prediction transition data, degradation map, and SOH of the drive battery pack installed in the target used electric vehicle 2 (S20). This SOH is used as the initial value of the predicted SOH curve. In the first predicted SOH curve generation process, the SOH stored in the declared information / SOH holding unit 121 is used. In the second and subsequent predicted SOH curve generation processes, the SOH estimated by the SOH estimation unit 118b at that time is used.

[0109] After a predetermined period (for example, 3 or 6 months) has elapsed since the last predicted SOH curve was generated (Y in S21), the measured data acquisition unit 116 acquires time-series measured data from the data logger 2a installed on the electric vehicle 2 (S22). The SOH estimation unit 118b estimates the current SOH of the drive battery pack based on the time-series measured data acquired from the data logger 2a (S23).

[0110] The determination unit 118e calculates the difference between the current SOH on the predicted SOH curve and the current SOH estimated from the measured data, and compares this difference with a threshold (S24). If the difference is less than or equal to the threshold (N in S24), the process proceeds to step S21.

[0111] If the difference exceeds a threshold (Y in S24), the regression measured SOH curve generation unit 118c performs curve regression on multiple SOHs estimated for each interval of the time-series measured data to generate a regression measured SOH curve for the drive battery pack (S25). The map measured curve generation unit 118d generates a map measured SOH curve for the drive battery pack based on the time-series measured data acquired from the data logger 2a and the degradation map (S26).

[0112] The determination unit 118e calculates a first deviation between the regression measured SOH curve and the map measured SOH curve. If the first deviation is greater than the first set value (Y in S27), the correction request unit 118f notifies the developer of the battery diagnostic system 10 of a request to correct the degradation map (S210). The developer verifies the regression measured SOH curve, the map measured SOH curve, and the degradation map, and corrects the degradation map.

[0113] If the first deviation is less than or equal to the first set value (N in S27), the correction request unit 118f notifies the developer of a request to correct the battery state transition data (S28). The developer verifies the battery state prediction transition data, the predicted SOH curve, and the measured map SOH curve, and corrects the battery state prediction transition data. If the first deviation is greater than the second set value (Y in S29), the correction request unit 118f also notifies the developer of a request to correct the degradation map (S210). If the first deviation is less than or equal to the second set value (N in S29), the processing in step S210 is skipped.

[0114] While the used electric vehicle 2 is in operation (N in S211), the process returns to step S20, and the predicted SOH curve generation and update process from steps S20 to S210 is repeated.

[0115] Figure 16 is a flowchart showing a modified example of the predicted SOH curve generation and update process by the battery diagnostic system 10 according to the embodiment. The process from steps S20 to S26 in the flowchart of Figure 16 is the same as the process from steps S20 to S26 in the flowchart of Figure 15, so the explanation is omitted.

[0116] The determination unit 118e calculates the second deviation between the battery state prediction transition data and the time-series measured data. If the second deviation is less than or equal to the third set value (Y in S27a), the correction request unit 118f notifies the developer of a request to correct the degradation map (S210). The developer verifies the regression measured SOH curve, the map measured SOH curve, and the degradation map, and corrects the degradation map.

[0117] If the second deviation is greater than the third setting value (N in S27a), the correction request unit 118f notifies the developer of a request to correct the battery state transition data (S28). The developer verifies the battery state prediction transition data, the predicted SOH curve, and the measured map SOH curve, and corrects the battery state prediction transition data. If the second deviation is less than or equal to the fourth setting value (Y in S29a), the correction request unit 118f also notifies the developer of a request to correct the degradation map (S210). If the second deviation is greater than the fourth setting value (N in S29a), the process in step S210 is skipped.

[0118] The used vehicle leasing system 1 allocates multiple used electric vehicles 2 based on the end-of-use timing of each used electric vehicle 2 diagnosed by the battery diagnostic system 10. For example, for a corporation that desires a lease agreement with a four-year usage guarantee, the system selects and leases used electric vehicles 2 whose end-of-use timing is four years from the start of secondary use. In this case, the vehicles may be allocated to business locations with high priority within the contracting corporation, in order of the highest State of Health (SOH) of the drive battery pack after four years from the start of secondary use. Furthermore, for corporations that desire an inexpensive lease agreement, used electric vehicles 2 whose SOH of the drive battery pack reaches 60% before four years from the start of secondary use are also included as lease targets. If the predicted end-of-use timing of multiple used electric vehicles 2 changes after the start of operation of multiple used electric vehicles 2, the allocation of multiple used electric vehicles 2 may be changed.

[0119] Up to this point, the explanation has been based on the example of leasing multiple used electric vehicles 2 to a corporation, but the battery diagnostic system 10 according to this embodiment can also be used when leasing a single used electric vehicle 2 to an individual. Furthermore, the battery diagnostic system 10 according to this embodiment can also be used when selling a used electric vehicle 2 to a corporation or an individual. The end user can refer to the simulation results of the remaining lifespan of the used electric vehicle 2 to decide whether or not to lease or purchase the used electric vehicle 2.

[0120] As described above, according to this embodiment, battery state prediction transition data is created based on user-reported information and the specifications of the electric vehicle 2, and at least one of the degradation map or the battery state prediction transition data is updated as appropriate after operation begins. This makes it possible to predict the degradation of the drive battery pack installed in the electric vehicle 2 with high accuracy without using past driving and charging log data. In addition, since the range of data that needs to be corrected can be identified, developers can correct the data efficiently.

[0121] The present disclosure has been described above based on embodiments. The embodiments are illustrative, and it will be understood by those skilled in the art that various modifications are possible in combinations of their components and processing processes, and that such modifications are also within the scope of the present disclosure.

[0122] The survey items mentioned above may differ for commercial vehicles and private vehicles. For example, for private vehicles, the charging station location would be the home, not the business premises. Also, for private vehicles, items related to charging stations other than those at business premises may be omitted.

[0123] The above-mentioned questionnaire items may include a question to select whether the parking environment is in direct sunlight or in the shade. If the parking environment is in direct sunlight, an adjustment value may be added to the predicted temperature change over the parking period.

[0124] Furthermore, in addition to SOH (State of Health), accident history and maintenance history may also be obtained as information for the used electric vehicle 2. This will allow for compatibility with replaced drive battery packs.

[0125] The embodiments may be specified by the following items.

[0126] [Item 1] A first acquisition unit (115) that acquires battery state prediction transition data of the battery pack installed in the electric vehicle (2), which is generated based on the user's declaration information regarding the use of the electric vehicle (2) before the electric vehicle (2) is put into use; a second acquisition unit (117) that acquires a degradation map that defines the relationship between parameters of the battery pack, including at least SOC, and the amount of SOH decrease; a third acquisition unit (116) that acquires time-series measured data of the battery pack, including at least the current and SOC, after the electric vehicle (2) is put into use; a first generation unit (118a) that generates a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; and an SOH estimation unit (118b) that estimates the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data. A battery diagnostic system (10) comprising: a second generation unit (118c) that generates a first measured SOH curve of the battery pack by performing curve regression on multiple SOHs estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; a third generation unit (118d) that generates a second measured SOH curve of the battery pack based on the measured data and the degradation map; and a determination unit (118e) that determines that if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold, it is necessary to correct at least one of the degradation map or the battery state prediction transition data. According to this, the degradation of the battery pack mounted on the electric vehicle (2) can be predicted with high accuracy without using past driving and charging log data. [Item 2] The battery diagnostic system (10) according to Item 1, wherein the determination unit (118e) calculates the degree of deviation between the first measured SOH curve and the second measured SOH curve, and if the degree of deviation is greater than a set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the degradation map, and if the degree of deviation is less than or equal to the set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the battery state prediction transition data.According to this, it is possible to accurately determine whether the main cause of the error in SOH is the degradation map or the battery state prediction transition data. [Item 3] The determination unit (118e) calculates the degree of deviation between the battery state prediction transition data and the time-series measured data, and if the degree of deviation is greater than a set value, it determines that the main cause of the difference in SOH exceeding the threshold is the error in the battery state prediction transition data, and if the degree of deviation is less than or equal to the set value, it determines that the main cause of the difference in SOH exceeding the threshold is the error in the degradation map, as described in Item 1, Battery diagnostic system (10). According to this, it is possible to accurately determine whether the main cause of the error in SOH is the degradation map or the battery state prediction transition data. [Item 4] The second acquisition unit (117) acquires a storage degradation map that defines the relationship between the combination of the battery pack's SOC and temperature and the amount of SOH decrease per unit time, a charging degradation map that defines the relationship between the combination of the battery pack's SOC, charge rate based on current, and temperature and the amount of SOH decrease per unit charge, and a discharge degradation map that defines the relationship between the combination of the battery pack's SOC, discharge rate based on current, and temperature and the amount of SOH decrease per unit discharge, the third acquisition unit (116) acquires time-series measured data including the current, temperature, and SOC of the battery pack after the start of use of the electric vehicle (2), the determination unit (118e) decomposes the first measured SOH curve into a storage degradation progression according to the passage of time and a cycle degradation curve according to the cumulative charge-discharge cycle, generates a storage degradation progression according to the passage of time and a cycle degradation progression according to the cumulative charge-discharge cycle as components of the second measured SOH curve, and calculates the error of the storage degradation progression and the error of the cycle degradation progression, respectively. A battery diagnostic system (10) described in any one of items 1 to 3. This system makes it possible to accurately determine whether the error in the degradation map is due to an error in the storage degradation map or an error in the charge degradation map or discharge degradation map.[Item 5] The first acquisition unit (115) acquires time-series data including the current, temperature, and SOC of the battery pack as battery state prediction transition data; the second acquisition unit (117) acquires a storage degradation map that defines the relationship between the combination of the SOC and temperature of the battery pack and the amount of SOH decrease due to storage degradation; a charging degradation map that defines the relationship between the combination of the SOC, charging rate based on the current, and temperature of the battery pack and the amount of SOH decrease due to charging degradation; and a discharge degradation map that defines the relationship between the combination of the SOC, discharging rate based on the current, and temperature of the battery pack and the amount of SOH decrease due to discharge degradation; the third acquisition unit (116) acquires time-series measured data including the current, temperature, and SOC of the battery pack after the start of use of the electric vehicle (2). The determination unit (118e) generates a breakdown of the predicted SOH curve, including a storage degradation trend according to the passage of time, a charge degradation trend according to the cumulative charge amount, and a discharge degradation trend according to the cumulative discharge amount, and generates a breakdown of the second measured SOH curve, including a storage degradation trend according to the passage of time, a charge degradation trend according to the cumulative charge amount, and a discharge degradation trend according to the cumulative discharge amount, and calculates the error of the storage degradation trend, the error of the charge degradation trend, and the error of the discharge degradation trend, as described in any one of items 1 to 3. According to this, it is possible to accurately determine whether the error in the battery state prediction data is due to an error in the idle period, an error in the charging period, or an error in the discharge period. [Item 6] The battery diagnostic system (10) described in Item 1, wherein the first generation unit (118a) generates a predicted SOH curve for the drive battery pack after the start of secondary use, based on the battery state prediction transition data, the degradation map, and the SOH of the drive battery pack at the start of secondary use, before the start of secondary use of the electric vehicle (2). This makes it possible to perform degradation simulation before leasing or purchasing a used electric vehicle (2).[Item 7] Steps to obtain battery state prediction transition data of the battery pack installed in the electric vehicle (2), generated based on declaration information regarding the use of the electric vehicle (2) by the user of the electric vehicle (2) before the electric vehicle (2) is put into use; Steps to obtain a degradation map that defines the relationship between parameters of the battery pack, including at least SOC, and the amount of SOH decrease; Steps to obtain time-series measured data of the battery pack, including at least the current and SOC, after the electric vehicle (2) is put into use; Steps to generate a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; Steps to estimate the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; Steps to generate a first measured SOH curve of the battery pack by performing curve regression on a plurality of SOH estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; Steps to generate a second measured SOH curve of the battery pack based on the measured data and the degradation map. A battery diagnostic method comprising the step of determining that if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold, it is necessary to correct at least one of the degradation map or the battery state prediction transition data. According to this method, the degradation of the battery pack mounted on the electric vehicle (2) can be predicted with high accuracy without using past driving and charging log data.[Item 8] Before the start of use of the electric vehicle (2), a process to acquire battery state prediction transition data of the battery pack installed in the electric vehicle (2), which is generated based on the user's declaration information regarding the use of the electric vehicle (2) by the user of the electric vehicle (2); a process to acquire a degradation map that defines the relationship between parameters of the battery pack, including at least SOC, and the amount of SOH decrease; a process to acquire time-series measured data of the battery pack, including at least the current and SOC, after the start of use of the electric vehicle (2); a process to generate a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; a process to estimate the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; a process to generate a first measured SOH curve of the battery pack by performing curve regression on multiple SOHs estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; a process to generate a second measured SOH curve of the battery pack based on the measured data and the degradation map. A battery diagnostic program that causes a computer to perform a process that determines that correction is necessary to at least one of the degradation map or the battery state prediction transition data if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold. According to this, the degradation of the battery pack installed in the electric vehicle (2) can be predicted with high accuracy without using past driving and charging log data.

[0127] This disclosure can be used to diagnose battery packs installed in used electric vehicles.

[0128] 1 Used vehicle leasing system, 2 Electric vehicle, 2a Data logger, 3 User terminal device, 4 Vehicle manufacturer server, 5 Network, 10 Battery diagnostic system, 11 Control unit, 12 Storage unit, 13 Communication unit, 111 Declaration information acquisition unit, 112 Electric vehicle information acquisition unit, 113 SOH acquisition unit, 114 Battery state transition prediction unit, 114a Average speed estimation unit, 114b Charging current estimation unit, 114c Discharge current estimation unit, 114d Current transition prediction unit, 114e Temperature transition prediction unit, 114f SOC transition prediction unit, 114g Battery state prediction transition data output unit, 115 Prediction transition data acquisition unit, 116 Actual measurement data acquisition unit, 117 Degradation map acquisition unit, 118 Degradation transition prediction unit, 118a Prediction SOH curve generation unit, 118b SOH estimation unit, 118c regression measured SOH curve generation unit, 118d map measured curve generation unit, 118e determination unit, 118f correction request unit, 118g remaining life determination unit, 121 declared information / SOH retention unit, 122 temperature map retention unit, 123 degradation map retention unit.

Claims

1. A first acquisition unit that acquires battery state prediction transition data of the battery pack installed in the electric vehicle, generated based on information declared by the user of the electric vehicle regarding the use of the electric vehicle before the electric vehicle is put into use; a second acquisition unit that acquires a degradation map that defines the relationship between parameters of the battery pack, including at least SOC (State of Charge), and the amount of SOH (State of Health) decrease; a third acquisition unit that acquires time-series measured data of the battery pack, including at least the current and SOC, after the electric vehicle is put into use; a first generation unit that generates a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; an SOH estimation unit that estimates the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; and a second generation unit that generates a first measured SOH curve of the battery pack by performing curve regression on a plurality of SOH estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data. A battery diagnostic system comprising: a third generation unit that generates a second measured SOH curve of the battery pack based on the measured data and the degradation map; and a determination unit that determines that if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold, it is necessary to correct at least one of the degradation map or the battery state prediction transition data.

2. The determination unit calculates the degree of deviation between the first measured SOH curve and the second measured SOH curve, and if the degree of deviation is greater than a set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the degradation map, and if the degree of deviation is less than or equal to the set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the battery state prediction transition data, the battery diagnostic system according to claim 1.

3. The determination unit calculates the degree of deviation between the battery state prediction transition data and the time-series measured data, and if the degree of deviation is greater than a set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the battery state prediction transition data, and if the degree of deviation is less than or equal to the set value, it determines that the main reason the difference in SOH exceeds the threshold is due to an error in the degradation map, the battery diagnostic system according to claim 1.

4. The second acquisition unit acquires a storage degradation map that defines the relationship between the combination of the battery pack's SOC and temperature and the amount of SOH decrease per unit time, a charge degradation map that defines the relationship between the combination of the battery pack's SOC, charge rate based on current, and temperature and the amount of SOH decrease per unit charge, and a discharge degradation map that defines the relationship between the combination of the battery pack's SOC, discharge rate based on current, and temperature and the amount of SOH decrease per unit discharge; the third acquisition unit acquires time-series measured data including the current, temperature, and SOC of the battery pack after the electric vehicle has started to be used; the determination unit decomposes the first measured SOH curve into a storage degradation curve corresponding to the passage of time and a cycle degradation curve corresponding to the cumulative charge-discharge cycles, generates a storage degradation curve corresponding to the passage of time and a cycle degradation curve corresponding to the cumulative charge-discharge cycles as components of the second measured SOH curve, and calculates the error of the storage degradation curve and the error of the cycle degradation curve, respectively.

5. The first acquisition unit acquires time-series data including the current, temperature, and SOC of the battery pack as battery state prediction transition data; the second acquisition unit acquires a storage degradation map that defines the relationship between the combination of the SOC and temperature of the battery pack and the amount of SOH decrease due to storage degradation; a charging degradation map that defines the relationship between the combination of the SOC, charging rate based on the current, and temperature of the battery pack and the amount of SOH decrease due to charging degradation; and a discharge degradation map that defines the relationship between the combination of the SOC, discharging rate based on the current, and temperature of the battery pack and the amount of SOH decrease due to discharge degradation; the third acquisition unit acquires time-series measured data including the current, temperature, and SOC of the battery pack after the start of use of the electric vehicle. The determination unit generates a breakdown of the predicted SOH curve, including a storage degradation trend according to the passage of time, a charge degradation trend according to the cumulative charge amount, and a discharge degradation trend according to the cumulative discharge amount; generates a breakdown of the second measured SOH curve, including a storage degradation trend according to the passage of time, a charge degradation trend according to the cumulative charge amount, and a discharge degradation trend according to the cumulative discharge amount; and calculates an error in the storage degradation trend, an error in the charge degradation trend, and an error in the discharge degradation trend, according to any one of claims 1 to 3.

6. The battery diagnostic system according to claim 1, wherein the first generation unit generates a predicted SOH curve for the drive battery pack after the start of secondary use, based on the battery state prediction transition data, the degradation map, and the SOH of the drive battery pack at the start of secondary use, before the start of secondary use of the electric vehicle.

7. Steps to obtain battery state prediction transition data of the battery pack installed in the electric vehicle, generated based on information declared by the user of the electric vehicle regarding the use of the electric vehicle before the electric vehicle is put into use; Steps to obtain a degradation map that defines the relationship between parameters of the battery pack, including at least SOC (State of Charge), and the amount of SOH (State of Health) decrease; Steps to obtain time-series measured data of the battery pack, including at least the current and SOC, after the electric vehicle is put into use; Steps to generate a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; Steps to estimate the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; Steps to generate a first measured SOH curve of the battery pack by performing curve regression on multiple SOHs estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; Steps to generate a second measured SOH curve of the battery pack based on the measured data and the degradation map. A battery diagnostic method comprising the step of determining that if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold, it is necessary to correct at least one of the degradation map or the battery state prediction transition data.

8. A process to acquire battery state prediction transition data of the battery pack installed in the electric vehicle, generated based on information declared by the user of the electric vehicle regarding the use of the electric vehicle before the electric vehicle is put into use; a process to acquire a degradation map that defines the relationship between parameters of the battery pack, including at least SOC (State of Charge), and the amount of SOH (State of Health) decrease; a process to acquire time-series measured data of the battery pack, including at least the current and SOC, after the electric vehicle is put into use; a process to generate a predicted SOH curve of the battery pack based on the battery state prediction transition data and the degradation map; a process to estimate the SOH of the battery pack based on the SOC difference and current integration value between two points obtained by referring to the measured data; a process to generate a first measured SOH curve of the battery pack by performing curve regression on multiple SOHs estimated for each interval of the measured data based on the SOC difference and current integration value between two points obtained by referring to the measured data; and a process to generate a second measured SOH curve of the battery pack based on the measured data and the degradation map. A battery diagnostic program that causes a computer to perform a process to determine that correction is necessary for at least one of the degradation map or the battery state prediction transition data if the difference between the SOH at a certain point in time on the predicted SOH curve and the SOH estimated from the measured data at that point in time exceeds a threshold.

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