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

Abstract

A charging current estimation unit 114b estimates a charging current flowing from a charger to a battery pack on the basis of the voltage of the battery pack and the output power of the charger specified on the basis of identification information of the charger. A discharge current estimation unit 114c estimates a discharge current flowing from the battery pack on the basis of average speed, power consumption, and the voltage of the battery pack. A current transition prediction unit 114d predicts a current transition of the battery pack on the basis of the discharge current, predetermined values of the operation start time and the operation end time in a unit period, the predetermined value of the operation frequency, the charging current, predetermined values of the charging start time and the charging end time, and the predetermined value of the charging frequency. A temperature transition prediction unit 114e predicts a temperature transition of the battery pack on the basis of the current transition of the battery pack. An SOC transition prediction unit 114f predicts an SOC transition of the battery pack on the basis of the current transition of the battery pack.

Description

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

[0006] ,

[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 attach importance to 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 in it. To accurately predict the life of the battery pack installed in a used EV, driving and charging log data up to now are required. However, detailed driving and charging log data from the start of use basically do not remain, and it is difficult for used EV sellers or those considering purchase to obtain them.

[0003] Patent Document 1 discloses promoting the replacement of a battery pack in consideration of the deterioration of the battery pack due to temperature based on a questionnaire to users. However, predicting the transition of current, temperature, and SOC (State Of Charge) from the questionnaire is not disclosed, nor is a specific calculation method for temperature and SOC.

[0004] Japanese Unexamined Patent Application Publication No. 2020-092008

[0005] The present disclosure has been made in view of such circumstances, and an object thereof is to provide a technique for predicting the deterioration of a battery pack mounted on an electric vehicle without using past driving and charging log data.

[0006] To solve the above problems, a battery diagnostic system in one aspect of the present disclosure includes: a declaration information acquisition unit that acquires user-declared information including identification information of an electric vehicle, a planned value for the mileage traveled in a unit period, a planned value for the frequency of operation in a unit period, planned start and end times of operation when operating, identification information of a charger used to charge the electric vehicle, a planned value for the frequency of charging in a unit period, and planned start and end times of charging when charging; an electric vehicle information acquisition unit that acquires the electric vehicle's energy consumption, the voltage and capacity of a battery pack installed in the electric vehicle based on the electric vehicle identification information; an average speed estimation unit that estimates the electric vehicle's average speed based on the planned mileage traveled and the planned operating time between the start and end times of operation; and a charging current that flows from the charger to the battery pack based on the output power of the charger identified based on the charger identification information and the voltage of the battery pack. The system includes: a charging current estimation unit that determines the charging current; a discharge current estimation unit that estimates the discharge current flowing from the battery pack based on the average speed, the power consumption, and the voltage of the battery pack; a current transition prediction unit that predicts the current transition of the battery pack based on the discharge current, the planned start time and end time of operation in the unit period, the planned frequency of operation, the charging current, the planned start time and end time of charging, and the planned frequency of charging; a temperature transition prediction unit that predicts the temperature transition of the battery pack based on the current transition of the battery pack; an SOC transition prediction unit that predicts the SOC transition of the battery pack based on the current transition of the battery pack; and a battery state prediction transition data output unit that outputs battery state prediction transition data usable for predicting the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack.

[0007] 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.

[0008] According to this disclosure, it is possible to predict the degradation of battery packs installed in electric vehicles without using past driving and charging log data.

[0009] This figure shows an overview of the used vehicle leasing system according to this embodiment. This figure illustrates the battery diagnostic system according to this embodiment. This figure shows an example of the battery state prediction trend over one week. This figure shows an example of the State of Health (SOH) trend. This flowchart shows the overall flow of the battery state prediction trend data creation process by the battery diagnostic system according to this embodiment. This flowchart shows the subroutine for the process in step S30 of Figure 5. This flowchart shows the subroutine for the process in step S40 of Figure 5.

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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).

[0014] 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.

[0015] 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, and a degradation transition prediction unit 115. 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 State of Care Unit (SOC) transition prediction unit 114f, and a battery state prediction transition data output unit 114g.

[0016] 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.

[0017] 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 storage degradation map storage unit 123.

[0018] 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.

[0019] 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.

[0020] 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?

[0021] 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.

[0022] 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.

[0023] 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)

[0024] 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.

[0025] 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.

[0026] 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, which is identified based on the charger's identification information, 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].

[0027] If the charger's output power is not included in the user-reported information, the charger information acquisition unit (not shown) accesses the charger manufacturer's server (not shown) via the network 5. Based on the charger identification information included in the user-reported information, the charger information acquisition unit obtains the output power included in the specifications table for the corresponding model and type. Alternatively, a representative of the leasing company may manually input the output power of the charger published in the specifications table for the corresponding model and type by browsing the charger manufacturer's server website from the user terminal device 3.

[0028] The discharge current estimation unit 114c estimates the discharge current Id flowing from the drive battery pack based on the average speed, energy consumption, and voltage of the drive battery pack of the electric vehicle 2. Specifically, it estimates the discharge current Id by calculating the following (Equation 3): Id = (v / E × 1000) / Vb ... (Equation 3) v: average speed [km / h], E: energy consumption [km / kWh]

[0029] 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 planned average load capacity or the planned average number of occupants of the electric vehicle 2 included in the user-reported information. More specifically, the discharge current estimation unit 114c corrects the power consumption listed in the specifications table based on the planned average load capacity included in the user-reported information.

[0030] For example, the energy consumption calculated based on the AC power consumption rate test (JC08 mode) is based on a weight of 110 kg of goods loaded. The discharge current estimation unit 114c corrects the total vehicle weight based on the difference between the expected average load amount included in the user-reported information and the load weight that forms the basis for calculating the energy consumption. The discharge current estimation unit 114c then corrects the energy consumption listed in the specifications table based on the corrected total vehicle weight and the specifications of the drive motor.

[0031] Furthermore, the discharge current estimation unit 114c corrects the fuel consumption listed in the specifications table based on the estimated average number of occupants included in the user-reported information. For example, the fuel consumption calculated based on the AC power consumption rate test (JC08 mode) assumes that there are two people on board (each person weighing 55 kg). The discharge current estimation unit 114c corrects the total vehicle weight based on the difference between the estimated average number of occupants included in the user-reported information and the number of occupants that forms the basis for calculating the fuel consumption. The discharge current estimation unit 114c then corrects the fuel consumption listed in the specifications table based on the corrected total vehicle weight and the specifications of the drive motor.

[0032] The discharge current estimation unit 114c may correct the discharge current Ic flowing from the drive battery pack based on the planned weekly highway driving time included in the user-reported information. Generally, when driving at high speeds, air resistance increases and the energy efficiency decreases. The discharge current estimation unit 114c reduces the energy efficiency listed in the specifications table based on the proportion of highway driving time in the weekly operating time. The rate of decrease in energy efficiency during high-speed driving can be determined as follows: A test vehicle (hereinafter referred to as the test vehicle) of the same model as the target electric vehicle 2 is prepared, and a driving test is conducted, and the rate is determined from the experimental results. Alternatively, it can be determined by simulation based on the specifications table of the target electric vehicle 2.

[0033] 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.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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].

[0040] 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.

[0041] 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.

[0042] Figure 3 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 3, 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.

[0043] The degradation progression prediction unit 115 predicts the degradation progression of the drive battery pack based on the storage degradation map of the drive battery pack and the battery state progression prediction data created by the battery state progression prediction unit 114.

[0044] The degradation of a secondary battery is roughly classified into storage degradation and cycle degradation. Storage degradation is degradation that progresses over time according to the temperature at each time point of the secondary battery and the SOC at each time point. It progresses over time regardless of whether the battery is being charged or discharged. 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 time point and the higher the temperature at each time point, the faster the storage degradation rate increases.

[0045] Cycle degradation of a secondary battery is degradation that progresses as the number of charge-discharge cycles increases. Cycle degradation mainly occurs due to structural degradation (such as wear, breakage, cracking, peeling, etc.) caused by the expansion and contraction of the positive electrode active material. Cycle degradation depends on the SOC range used, temperature, and current rate. Generally, the cycle degradation rate increases in a low SOC range. Also, the higher the current rate and the higher the temperature, the faster the cycle degradation rate increases. The cycle degradation of the drive battery pack mounted on the electric vehicle 2 is approximately proportional to the cumulative driving distance.

[0046] As a result of analyzing the storage degradation and cycle degradation of the drive battery pack mounted on an actually used used electric vehicle 2, it was found that the influence of storage degradation is dominant, and even if the influence of cycle degradation is ignored, the degradation transition of the battery pack can be predicted with relatively high accuracy.

[0047] The storage degradation map is created in advance by the designer based on the relationship between the SOC, temperature, and storage degradation amount obtained from a storage test using a test vehicle. The storage degradation map is described, for example, in a two-dimensional parameter space with the SOC and temperature as input variables and the storage degradation amount as the output. The storage degradation amount is defined by the amount of SOH decrease per unit time elapsed (ΔSOH / √h). Generally, it is known that storage degradation progresses approximately linearly with respect to the square root rule (0.5 power) of the elapsed time (h). Therefore, the unit time of the storage degradation amount [% / √h] is set to the 0.5 power of 1 hour. The designer creates a storage degradation map for each vehicle type and stores it in the storage degradation map holding unit 123.

[0048] The deterioration trend prediction unit 115 reads out the storage deterioration map of the target electric vehicle 2 from the storage deterioration map holding unit 123. The deterioration trend prediction unit 115 reads out the SOH of the target electric vehicle 2 from the reported information / SOH holding unit 121. The deterioration trend prediction unit 115 assumes that the transitions of the SOC and temperature over one week included in the battery state prediction transition data predicted by the battery state transition prediction unit 114 will continue repeatedly over several years. The deterioration trend prediction unit 115 refers to the read storage deterioration map and predicts the SOH transition with the read SOH as the initial value based on the predicted transitions of the SOC and temperature.

[0049] Note that when predicting the temperature transition of the drive battery pack over one week, the temperature transition prediction unit 114e may predict the temperature transition over one week for each season based on the average temperature of each season in the planned usage area. In this case, the SOH transition can be predicted based on the temperature transition considering seasonality.

[0050] FIG. 4 is a diagram showing an example of the SOH transition. For example, when the current SOH of the drive battery pack mounted on the target used electric vehicle 2 is 80%, the deterioration trend prediction unit 115 predicts the SOH transition after SOH = 80%. The deterioration trend prediction unit 115 sets, for example, the earlier of the timing when four years have passed since the start of the secondary use of the used electric vehicle 2 or the timing when the SOH of the drive battery pack reaches 60% as the end-of-use timing of the used electric vehicle 2.

[0051] FIG. 5 is a flowchart showing the overall flow of the battery state prediction transition data creation process by the battery diagnosis system 10 according to the embodiment. The reported information acquisition unit 111 acquires user reported information from the user terminal device 3 (S10). The electric vehicle information acquisition unit 112 acquires the specification information of the electric vehicle 2 based on the identification information of the electric vehicle 2 included in the user reported information (S20).

[0052] 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 operating days (S30). Based on the user-declared information, the battery state transition prediction unit 114 creates a battery state transition prediction for non-operating days (S40).

[0053] Figure 6 is a flowchart showing the subroutine for the process in step S30 of Figure 5. The current transition prediction unit 114d uses the charging time, discharging time, driving distance, charger output power obtained from user-reported information, the energy consumption included in the specifications of the electric vehicle 2, and the voltage and capacity of the drive battery pack as basic data to predict the current transition of the drive battery pack on an operating day (S31).

[0054] The temperature transition prediction unit 114e predicts the temperature transition of the drive battery pack on an operating day 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 day (S32).

[0055] The SOC transition prediction unit 114f predicts the SOC transition of the drive battery pack on an operating day based on the predicted current transition of the drive battery pack on the operating day and the capacity of the drive battery pack included in the specification information of the electric vehicle 2 (S33).

[0056] Figure 7 is a flowchart showing the subroutine for the process in step S40 of Figure 5. The temperature transition prediction unit 114e predicts the temperature transition of the drive battery pack on non-operating days (S41) based on the planned usage area of ​​the electric vehicle 2 included in the user-declared information and the end value of the temperature transition on the immediately preceding operating day.

[0057] Returning to Figure 5, the battery state prediction transition data output unit 114g integrates the current transition of the drive battery pack on operating days and the current transition of the drive battery pack on non-operating days to create one week's worth of battery state prediction transition data, which is then output to the degradation transition prediction unit 115 (S50).

[0058] The degradation progression prediction unit 115 acquires one week's worth of battery state prediction progression data output from the battery state prediction progression data output unit 114g, and repeats this one-week data to create battery state prediction progression data for a predetermined number of years. Based on the battery state prediction progression data for the predetermined number of years, the storage degradation map, and the State of Health (SOH) of the target electric vehicle 2, the degradation progression prediction unit 115 simulates the degradation progression of the target electric vehicle 2. For example, the degradation progression prediction unit 115 sets the timing for ending the use of the used electric vehicle 2 to whichever comes first: four years after the start of secondary use of the used electric vehicle 2, or when the SOH of the drive battery pack reaches 60%.

[0059] 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 options.

[0060] 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.

[0061] As described above, according to this embodiment, by creating battery state prediction trend data based on user-reported information and specification information of the electric vehicle 2, it is possible to predict the deterioration of the drive battery pack installed in the electric vehicle 2 without using past driving and charging log data.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] The embodiments may be specified by the following items.

[0067] [Item 1] A declaration information acquisition unit (111) that acquires user-declared information including identification information of the electric vehicle (2), planned value of the mileage traveled in a unit period, planned value of the operating frequency in a unit period, planned start time and end time of operation when operating, identification information of the charger used to charge the electric vehicle (2), planned value of the charging frequency in a unit period, and planned start time and end time of charging when charging; an electric vehicle information acquisition unit (112) that acquires the power consumption of the electric vehicle (2), the voltage and capacity of the battery pack installed in the electric vehicle (2) based on the identification information of the electric vehicle (2); an average speed estimation unit (114a) that estimates the average speed of the electric vehicle (2) based on the planned mileage traveled and the planned operating time between the start time and the end time of operation; a charging current estimation unit (114b) that estimates the charging current flowing from the charger to the battery pack based on the output power of the charger identified based on the identification information of the charger and the voltage of the battery pack. A battery diagnostic system (10) comprising: a discharge current estimation unit (114c) that estimates the discharge current flowing from the battery pack based on the average speed, the power consumption, and the voltage of the battery pack; a current transition prediction unit (114d) that predicts the current transition of the battery pack based on the discharge current, the planned start time and end time of operation in the unit period, the planned operating frequency, the charging current, the planned start time and end time of charging, and the planned charging frequency; a temperature transition prediction unit (114e) that predicts the temperature transition of the battery pack based on the current transition of the battery pack; an SOC transition prediction unit (114f) that predicts the SOC transition of the battery pack based on the current transition of the battery pack; and a battery state prediction transition data output unit (114g) that outputs battery state prediction transition data usable for predicting the deterioration of the battery pack, including at least the temperature transition and SOC transition of the battery pack. According to this, the deterioration of the battery pack mounted on the electric vehicle (2) can be predicted without using past driving and charging log data.[Item 2] The battery diagnostic system (10) described in Item 1 further acquires the planned usage area of ​​the electric vehicle (2), and the temperature transition prediction unit (114e) refers to a temperature map created from the current and temperature transitions previously acquired from the electric vehicle (2) and an electric vehicle (2) of the same type used for data acquisition, and predicts the temperature transition of the battery pack based on the current transition of the battery pack and the planned usage area. According to this, the accuracy of predicting the temperature transition of the battery pack can be improved by considering the planned usage area of ​​the electric vehicle (2). [Item 3] The battery diagnostic system (10) described in Item 1 further acquires the planned usage area of ​​the electric vehicle (2), and the temperature transition prediction unit (114e) refers to a temperature map created from the current and temperature transitions previously acquired from the electric vehicle (2) and an electric vehicle (2) of the same type used for data acquisition, and predicts the temperature transition of the battery pack based on the current transition of the battery pack. According to this, the accuracy of predicting the temperature transition of the battery pack can be improved. [Item 4] The battery diagnostic system (10) described in Item 1, wherein the declared information acquisition unit (111) further acquires at least one of the planned average load capacity or the planned average number of occupants of the electric vehicle (2), and the discharge current estimation unit (114c) corrects the discharge current flowing from the battery pack based on at least one of the planned average load capacity or the planned average number of occupants of the electric vehicle (2). This makes it possible to improve the accuracy of predicting the current transition of the battery pack. [Item 5] The battery state prediction transition data output unit creates the battery state prediction transition data before the primary use of the electric vehicle (2) ends and secondary use begins. This makes it possible to perform a degradation simulation before leasing or purchasing a used electric vehicle (2). [Item 6] The battery diagnostic system (10) described in Item 1, further comprising a degradation transition prediction unit (115) that predicts the degradation transition of the battery pack based on the storage degradation map of the battery pack and the battery state prediction transition data. According to this, it is possible to easily perform a deterioration simulation of a used electric vehicle (2).[Item 7] Steps to obtain user-declared information including identification information of the electric vehicle (2), planned value of the mileage traveled in a unit period, planned value of the operating frequency in a unit period, planned start and end times of operation when operating, identification information of the charger used to charge the electric vehicle (2), planned value of the charging frequency in a unit period, and planned start and end times of charging when charging; Steps to obtain the power consumption of the electric vehicle (2), the voltage and capacity of the battery pack installed in the electric vehicle (2) based on the identification information of the electric vehicle (2); Steps to estimate the average speed of the electric vehicle (2) based on the planned mileage and planned operating time between the start and end times of operation; Steps to estimate the charging current flowing from the charger to the battery pack based on the output power of the charger identified based on the identification information of the charger and the voltage of the battery pack; Steps to estimate the discharge current flowing from the battery pack based on the average speed, power consumption and voltage of the battery pack. A battery diagnostic method comprising: a step of predicting the current transition of the battery pack based on the discharge current, the planned start and end times of operation in the unit period, the planned operating frequency, the charging current, the planned start and end times of charging, and the planned charging frequency; a step of predicting the temperature transition of the battery pack based on the current transition of the battery pack; a step of predicting the state of charge (SOC) transition of the battery pack based on the current transition of the battery pack; and a step of outputting battery state prediction transition data that can be used to predict the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack. According to this method, the degradation of a battery pack mounted on an electric vehicle (2) can be predicted without using past driving and charging log data.[Item 8] A process to acquire user-declared information including identification information of the electric vehicle (2), planned value of the mileage traveled in a unit period, planned value of the operating frequency in a unit period, planned start and end times of operation when operating, identification information of the charger used to charge the electric vehicle (2), planned value of the charging frequency in a unit period, and planned start and end times of charging when charging; a process to acquire the energy consumption of the electric vehicle (2), the voltage and capacity of the battery pack installed in the electric vehicle (2) based on the identification information of the electric vehicle (2); a process to estimate the average speed of the electric vehicle (2) based on the planned mileage and the planned operating time between the start and end times of operation; a process to estimate the charging current flowing from the charger to the battery pack based on the output power of the charger identified based on the identification information of the charger and the voltage of the battery pack; a process to estimate the discharge current flowing from the battery pack based on the average speed, the energy consumption and the voltage of the battery pack. A battery diagnostic program that causes a computer to perform the following steps: a process to predict the current transition of the battery pack based on the discharge current, the planned start and end times of operation in the unit period, the planned operating frequency, the charging current, the planned start and end times of charging, and the planned charging frequency; a process to predict the temperature transition of the battery pack based on the current transition of the battery pack; a process to predict the state of charge (SOC) transition of the battery pack based on the current transition of the battery pack; and a process to output battery state prediction transition data that can be used to predict the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack.

[0068] This disclosure can be used to predict the degradation of battery packs.

[0069] 1 Used vehicle leasing system, 2 Electric vehicle, 3 User terminal device, 4 Vehicle manufacturer server, 5 Network, 10 Battery diagnostic system, 11 Control unit, 12 Memory 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 Degradation transition prediction unit, 121 Declaration information / SOH holding unit, 122 Temperature map holding unit, 123 Storage degradation map holding unit.

Claims

1. A declaration information acquisition unit that acquires user-declared information including identification information of an electric vehicle, a planned value for the distance traveled in a unit period, a planned value for the frequency of operation in a unit period, planned start and end times of operation when operating, identification information of a charger used to charge the electric vehicle, a planned value for the frequency of charging in a unit period, and planned start and end times of charging when charging; an electric vehicle information acquisition unit that acquires the electric vehicle's energy consumption, the voltage and capacity of the battery pack installed in the electric vehicle based on the electric vehicle's identification information; an average speed estimation unit that estimates the electric vehicle's average speed based on the planned distance traveled and the planned operating time between the start and end times of operation; a charging current estimation unit that estimates the charging current flowing from the charger to the battery pack based on the charger's output power identified based on the charger's identification information and the battery pack's voltage; and a discharge current estimation unit that estimates the discharge current flowing from the battery pack based on the average speed, energy consumption, and the battery pack's voltage. A battery diagnostic system comprising: a current transition prediction unit that predicts the current transition of the battery pack based on the discharge current, the planned start time and end time of operation in the unit period, the planned operating frequency, the charging current, the planned start time and end time of charging, and the planned charging frequency; a temperature transition prediction unit that predicts the temperature transition of the battery pack based on the current transition of the battery pack; an SOC (State of Charge) transition prediction unit that predicts the SOC transition of the battery pack based on the current transition of the battery pack; and a battery state prediction transition data output unit that outputs battery state prediction transition data usable for predicting the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack.

2. The battery diagnostic system according to claim 1, wherein the declaration information acquisition unit further acquires the planned usage area of ​​the electric vehicle, and the temperature change prediction unit refers to a temperature map created from current and temperature changes previously acquired from an electric vehicle of the same type as the electric vehicle for data acquisition, and predicts the temperature change of the battery pack based on the current change of the battery pack and the planned usage area.

3. The battery diagnostic system according to claim 1, wherein the SOC transition prediction unit predicts the SOC transition based on the current integrated value based on the current transition and the capacity of the battery pack.

4. The battery diagnostic system according to claim 1, wherein the declaration information acquisition unit further acquires at least one of the planned average load capacity or the planned average number of occupants of the electric vehicle, and the discharge current estimation unit corrects the discharge current flowing from the battery pack based on at least one of the planned average load capacity or the planned average number of occupants of the electric vehicle.

5. The battery diagnostic system according to claim 1, wherein the battery state prediction transition data output unit creates the battery state prediction transition data before the primary use of the electric vehicle ends and secondary use begins.

6. The battery diagnostic system according to claim 1, further comprising a degradation trend prediction unit that predicts the degradation trend of the battery pack based on the storage degradation map of the battery pack and the battery state prediction trend data.

7. Steps to obtain user-reported information including identification information of an electric vehicle, planned mileage in a unit period, planned operating frequency in a unit period, planned start and end times of operation when operating, identification information of a charger used to charge the electric vehicle, planned charging frequency in a unit period, and planned start and end times of charging when charging; Steps to obtain the electric vehicle's energy consumption, the voltage and capacity of the battery pack installed in the electric vehicle based on the electric vehicle's identification information; Steps to estimate the electric vehicle's average speed based on the planned mileage and the planned operating time between the start and end times of operation; Steps to estimate the charging current flowing from the charger to the battery pack based on the charger's output power identified based on the charger's identification information and the battery pack's voltage; Steps to estimate the discharge current flowing from the battery pack based on the average speed, energy consumption and the battery pack's voltage. A battery diagnostic method comprising: a step of predicting the current transition of the battery pack based on the discharge current, the planned start time and end time of operation in the unit period, the planned operating frequency, the charging current, the planned start time and end time of charging, and the planned charging frequency; a step of predicting the temperature transition of the battery pack based on the current transition of the battery pack; a step of predicting the State of Charge (SOC) transition of the battery pack based on the current transition of the battery pack; and a step of outputting battery state prediction transition data that can be used to predict the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack.

8. A process for acquiring user-reported information including identification information of an electric vehicle, a planned value for the distance traveled in a unit period, a planned value for the frequency of operation in a unit period, planned start and end times of operation when operating, identification information of a charger used to charge the electric vehicle, a planned value for the frequency of charging in a unit period, and planned start and end times of charging when charging; a process for acquiring the electric vehicle's energy consumption, the voltage and capacity of the battery pack installed in the electric vehicle based on the electric vehicle's identification information; a process for estimating the electric vehicle's average speed based on the planned distance traveled and the planned operating time between the start and end times of operation; a process for estimating the charging current flowing from the charger to the battery pack based on the charger's output power identified based on the charger's identification information and the battery pack's voltage; and a process for estimating the discharge current flowing from the battery pack based on the average speed, energy consumption, and the battery pack's voltage. A battery diagnostic program that causes a computer to perform the following steps: a process to predict the current transition of the battery pack based on the discharge current, the planned start and end times of operation within the unit period, the planned operating frequency, the charging current, the planned start and end times of charging, and the planned charging frequency; a process to predict the temperature transition of the battery pack based on the current transition of the battery pack; a process to predict the State of Charge (SOC) transition of the battery pack based on the current transition of the battery pack; and a process to output battery state prediction transition data that can be used to predict the degradation of the battery pack, including at least the temperature transition and SOC transition of the battery pack.

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