JP2023182404A - Battery diagnostic device, battery diagnostic method, and program - Google Patents

Abstract

To provide a battery diagnostic device, a battery diagnostic method and a program which are able to more properly estimate the degree of deterioration of a battery.SOLUTION: The battery diagnostic device comprises: a storage part that stores over-voltage estimation information for estimating over-voltage of a battery on the basis of a variable indicating the discharge condition of the battery, and theoretical value information for giving a theoretical value of the over-voltage to the variable; and an estimation part that derives an estimated value and a theoretical value of the over-voltage corresponding to a designated variable on the basis of the over-voltage estimation information and the theoretical value information, and on the basis of the estimated value and the theoretical value thus derived, estimates a degree of deterioration of the battery corresponding to the designated variable.SELECTED DRAWING: Figure 1

Description

The present invention relates to a battery diagnostic device, a battery diagnostic method, and a program.

Conventionally, the SOH (State of Health) of a battery has been estimated in order to recognize the degree of deterioration of the battery. As a method for estimating such SOH, for example, the relationship between various variables and the degree of deterioration is learned by machine learning using FCD data (Floating Car Data) obtained from vehicles introduced into the market, and this learned A method of estimating SOH based on a model has been proposed (see, for example, Patent Document 1).

JP 2021-124419 Publication

However, with the above techniques, SOH may not be estimated appropriately due to electrochemical factors.

The present invention has been made in consideration of such circumstances, and one of the objects is to provide a battery diagnostic device, a battery diagnostic method, and a program that can more appropriately estimate the degree of battery deterioration. do.

The battery diagnostic device, battery diagnostic method, and program according to the present invention employ the following configuration.

(1): A battery diagnostic device according to one aspect of the present invention includes overvoltage estimation information for estimating the overvoltage of the battery based on variables representing discharge conditions of the battery, and a theory of the overvoltage for the variable. a storage unit that stores theoretical value information that provides a value; and a storage unit that derives an estimated value and a theoretical value of the overvoltage corresponding to a specified variable based on the overvoltage estimation information and the theoretical value information, and the derived estimated value. and an estimation unit that estimates the degree of deterioration of the battery corresponding to the designated variable based on the theoretical value.

(2): In the aspect of (1) above, the estimating unit acquires error estimation information indicating a relationship between the error of the estimated value and the theoretical value and the variable, and based on the acquired error estimation information, This is to estimate the degree of battery deterioration.

(3): In the aspect of (1) or (2) above, when the value of the variable is input for estimation of battery overvoltage, the estimating unit calculates the value of the variable based on the error estimation information. This outputs the error range of the estimated value regarding the battery overvoltage.

(4): In any of the aspects (1) to (3) above, the variable is the temperature of the environment where the battery is used, the current when the battery is used, or the temperature at which the battery is used. This includes the time spent.

(5): In any of the aspects (1) to (4) above, the estimation unit selects a different theoretical formula depending on the type of the variable, and calculates the theoretical value using the selected theoretical formula. It is something to do.

(6): In the aspects (1) to (5) above, when a plurality of types of the variables are given, the estimation unit calculates the error for each of the plurality of variables, and calculates the error as the degree of deterioration of the battery. The integrated value of the overvoltage error for each variable is obtained.

(7): The battery diagnosis method according to one aspect of the present invention includes overvoltage estimation information for estimating overvoltage of the battery based on variables representing discharge conditions of the battery, and Based on the theoretical value information giving the theoretical value of the overvoltage, the estimated value and the theoretical value of the overvoltage corresponding to the specified variable are derived, and based on the derived estimated value and the theoretical value, the specified This method estimates the degree of battery deterioration corresponding to the determined variables.

(8): The program according to one aspect of the present invention provides a computer with overvoltage estimation information for estimating the overvoltage of the battery based on variables representing discharge conditions of the battery, and overvoltage estimation information for estimating the overvoltage of the battery based on the variables representing the discharge conditions of the battery. Based on the theoretical value information giving the theoretical value, the estimated value and the theoretical value of the overvoltage corresponding to the specified variable are derived, and based on the derived estimated value and the theoretical value, the specified This is to estimate the degree of deterioration of the battery corresponding to the variable.

According to aspects (1) to (8), overvoltage estimation information is provided for estimating the overvoltage of the battery based on variables representing discharge conditions of the battery, and a theoretical value of the overvoltage is provided for the variable. The estimated value and theoretical value of the overvoltage corresponding to the specified variable are derived based on the theoretical value information, and the battery corresponding to the specified variable is calculated based on the derived estimated value and the theoretical value. By estimating the degree of deterioration of the battery, it is possible to provide a battery diagnosis device, a battery diagnosis method, and a program that can more appropriately estimate the degree of deterioration of the battery.

1 is a diagram schematically showing the configuration of a battery diagnostic system 1. FIG. 1 is a diagram showing an example of the configuration of a vehicle 10 to which a battery diagnostic device 100 is applied. 1 is a diagram showing an example of the configuration of a battery diagnostic device 100. FIG. FIG. 3 is a diagram showing an example of a theoretical formula. 2 is a flowchart illustrating an example of a process (preprocessing) for generating an overvoltage error model. FIG. 3 is a diagram schematically showing a method for generating an overvoltage error model. 12 is a flowchart illustrating an example of a process of estimating an error range for an estimated value of overvoltage using an overvoltage error model (overvoltage estimation process). FIG. 3 is a diagram schematically showing a method for estimating an error range for an estimated value of overvoltage.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a battery diagnostic device, a battery diagnostic method, and a program of the present invention will be described with reference to the drawings. In the following, a case will be described in which the battery to be diagnosed by the battery diagnostic device is a battery in which battery cells are integrated, but the present invention is not limited to this. The battery may be an individual battery cell. Furthermore, although a case will be described below in which a battery is used as a power source for a vehicle, the present invention is not limited thereto. The battery may be used in any electric device as long as information on its use can be obtained.

FIG. 1 is a diagram schematically showing the configuration of a battery diagnostic system 1. As shown in FIG. The battery diagnosis system 1 is a system that diagnoses the degree of deterioration of battery groups introduced into the market based on information regarding the usage status of batteries obtained from a plurality of vehicles 10 (hereinafter referred to as "battery information"). . For example, battery information may be acquired from a plurality of vehicles 10 in the form of FCD data (Floating Car Data). The battery diagnostic system 1 includes, for example, a plurality of vehicles 10 and a battery diagnostic device 100. The plurality of vehicles 10 and the battery diagnostic device 100 can communicate with each other via the network NW. The network NW may include the Internet, a LAN (Local Area Network), a cellular network, a leased line, a mobile communication line, and the like.

Each of the plurality of vehicles 10 is equipped with a battery 40, and can convert electric power supplied from the battery 40 into driving force. The plurality of vehicles 10 may be so-called hybrid vehicles that use both the driving force obtained by the battery 40 and the driving force obtained by the internal combustion engine. The plurality of vehicles 10 supply battery information regarding the battery 40 to the battery diagnostic device 100. For example, the plurality of vehicles 10 may transmit battery information via an on-vehicle device having a wireless communication function, such as a car navigation device.

The battery diagnostic device 100 collects battery information from a plurality of vehicles 10 and generates an estimation model for estimating the degree of deterioration of the battery 40 based on the collected battery information. By generating such an estimation model, the battery diagnostic device 100 can diagnose (estimate) the degree of deterioration of the battery 40 put on the market. In addition, the battery diagnostic device 100 learns the tendency of deterioration based on a huge amount of battery information about the batteries 40 put on the market, so that even batteries for which battery information cannot be obtained, the tendency of deterioration is similar. It also becomes possible to estimate the degree of deterioration of batteries.

The battery diagnostic system 1 of this embodiment is capable of more appropriately estimating the degree of battery deterioration by appropriately considering electrochemical factors in estimating the degree of battery deterioration. Hereinafter, the configuration of the battery diagnostic system 1 according to the embodiment that can produce such effects will be described in detail.

[Vehicle configuration]
FIG. 2 is a diagram showing an example of the configuration of a vehicle 10 to which the battery diagnostic device 100 is applied. The vehicle 10 shown in FIG. 2 is a BEV (Battery Electric Vehicle) that runs on an electric motor that is driven by electric power supplied from a battery (secondary battery) for driving. Alternatively, the vehicle 10 may be a PHV (Plug-in Hybrid Vehicle) or a PHEV (Plug-in Hybrid Electric Vehicle), which is a hybrid vehicle equipped with an external charging function. Note that the vehicle 10 is, for example, not only a four-wheeled vehicle, but also a saddle-type two-wheeled vehicle, a three-wheeled vehicle (including a vehicle with one front wheel and two rear wheels, and a vehicle with two front wheels and one rear wheel), and an assist vehicle. This includes all types of moving objects that are driven by electric motors that are driven by electric power supplied from batteries, such as type bicycles and electric boats.

The motor 12 is, for example, a three-phase AC motor. A rotor of the motor 12 is connected to a drive wheel 14 . The motor 12 is driven by electric power supplied from a power storage unit (not shown) included in the battery 40 and transmits rotational power to the drive wheels 14 . Furthermore, the motor 12 generates electricity using the kinetic energy of the vehicle 10 when the vehicle 10 is decelerated.

The brake device 16 includes, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, and an electric motor that generates hydraulic pressure in the cylinder. The brake device 16 may be provided with a mechanism as a backup mechanism that transmits hydraulic pressure generated by the operation of a brake pedal (not shown) by a user (driver) of the vehicle 10 to a cylinder via a master cylinder. Note that the brake device 16 is not limited to the configuration described above, and may be an electronically controlled hydraulic brake device that transmits the hydraulic pressure of the master cylinder to the cylinder.

The vehicle sensor 20 includes, for example, an accelerator opening sensor, a vehicle speed sensor, and a brake depression amount sensor. The accelerator opening sensor is attached to the accelerator pedal, detects the amount of operation of the accelerator pedal by the driver, and outputs the detected operation amount as the accelerator opening to a control unit 36, which will be described later. The vehicle speed sensor includes, for example, a wheel speed sensor and a speed calculator attached to each wheel of the vehicle 10, and integrates the wheel speeds detected by the wheel speed sensors to derive the speed (vehicle speed) of the vehicle 10 and performs control. It is output to section 36. The brake depression amount sensor is attached to the brake pedal, detects the amount of operation of the brake pedal by the driver, and outputs the detected amount of operation to the control unit 36 as the amount of brake depression.

The PCU 30 includes, for example, a converter 32 and a VCU (Voltage Control Unit) 34. In addition, in FIG. 2, the configuration in which these components are integrated as a PCU 30 is merely an example, and these components in the vehicle 10 may be arranged in a dispersed manner.

Converter 32 is, for example, an AC-DC converter. A DC side terminal of the converter 32 is connected to a DC link DL. A battery 40 is connected to the DC link DL via a VCU 34. The converter 32 converts the alternating current generated by the motor 12 into direct current and outputs it to the direct current link DL.

The VCU 34 is, for example, a DC-DC converter. The VCU 34 boosts the power supplied from the battery 40 and outputs it to the DC link DL.

The control unit 36 controls the drive of the motor 12 based on the output from the accelerator opening sensor included in the vehicle sensor 20. The control unit 36 also controls the brake device 16 based on the output from the brake pedal amount sensor included in the vehicle sensor 20. The control unit 36 also calculates, for example, the SOC (State of Charge) of the battery 40 based on an output from a battery sensor 42 connected to the battery 40 and described later, and outputs it to the VCU 34. The VCU 34 increases the voltage of the DC link DL in response to instructions from the control unit 36.

The battery 40 is, for example, a secondary battery such as a lithium ion battery that can be repeatedly charged and discharged. The positive electrode active material constituting the positive electrode of the battery 40 includes at least one of materials such as NCM (Nickel Cobalt Manganese), NCA (Nickel Cobalt Aluminum), LFP (Lithium Ferro Phosphate), and LMO (Lithium Manganese Oxide). The negative electrode active material that constitutes the negative electrode of the battery 40 is, for example, a substance that contains at least one of materials such as hard carbon and graphite. Further, the battery 40 may be, for example, a cassette-type battery pack that is detachably attached to the vehicle 10. The battery 40 stores electric power supplied from a charger (not shown) outside the vehicle 10 and discharges it for driving the vehicle 10.

The battery sensor 42 detects physical quantities such as current, voltage, and temperature of the battery 40. The battery sensor 42 includes, for example, a current sensor, a voltage sensor, and a temperature sensor. The battery sensor 42 detects the current of the secondary battery that constitutes the battery 40 with a current sensor, the voltage of the battery 40 with a voltage sensor, and the temperature of the battery 40 with a temperature sensor. The battery sensor 42 outputs detected physical quantity data such as current value, voltage value, and temperature of the battery 40 to the control unit 36 and the communication device 50.

The communication device 50 includes a wireless module for connecting to a cellular network or a Wi-Fi network. The communication device 50 may include a wireless module for utilizing Bluetooth (registered trademark) or the like. The communication device 50 transmits and receives various information regarding the vehicle 10 to and from, for example, the battery diagnostic device 100 through communication in the wireless module. The communication device 50 transmits data on the physical quantity of the battery 40 output by the control unit 36 or the battery sensor 42 to the battery diagnostic device 100. The communication device 50 receives deterioration information indicating the degree of deterioration of the battery 40 that has been diagnosed and transmitted by a battery diagnostic device 100 (described later), and outputs the received deterioration information of the battery 40 to the HMI (not shown) of the vehicle 10. It's okay.

[Configuration of battery diagnostic device]
Next, an example of the battery diagnostic device 100 that estimates the degree of deterioration of the battery 40 of the vehicle 10 will be described.
FIG. 3 is a diagram showing an example of the configuration of the battery diagnostic device 100 according to the embodiment. The battery diagnostic device 100 includes, for example, an acquisition section 110, a learning section 120, an estimation section 130, an input section 140, a display section 150, and a storage section 160. The acquisition unit 110, the learning unit 120, and the estimation unit 130 are realized, for example, by a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components are hardware (circuit parts) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit). (including circuitry), or may be realized by collaboration between software and hardware. The program may be stored in advance in a storage device (a storage device equipped with a non-transitory storage medium) such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory, or a DVD or CD-ROM. It is stored in a removable storage medium (non-transitory storage medium) such as a ROM, and may be installed by attaching the storage medium to a drive device. The storage unit 160 is, for example, an HDD, flash memory, RAM (Random Access Memory), or the like.

The acquisition unit 110 uses a communication interface (not shown) installed in the battery diagnostic device 100 to obtain the usage status of the battery 40 (current value, voltage value, temperature, etc., hereinafter also referred to as “discharge condition”) from the communication device 50. ) is acquired and stored in the storage unit 160. The acquisition unit 110 may further calculate the discharge capacity (discharge amount) by integrating the current values included in the acquired time series data, and store the calculated discharge capacity in the storage unit 160 as the time series data 160A. At this time, the acquisition unit 110 may perform a process of excluding data in which loss or abnormality has occurred from among the acquired time series data. Further, the discharge capacity may not be calculated by the battery diagnostic device 100, but may be calculated by the vehicle 10 and then transmitted to the battery diagnostic device 100 via the communication device 50.

The learning unit 120 learns an overvoltage estimation model 160B of the battery 40 and an error estimation model 160D for estimating an error in the overvoltage estimated by the overvoltage estimation model 160B. The overvoltage estimation model 160B is a model for estimating overvoltage (SOH: State of Health) as the degree of deterioration of the battery 40 using the discharging condition (usage status) of the battery 40 as a variable. The learning unit 120 generates an overvoltage estimation model 160B by learning the relationship between various discharge conditions and overvoltage through machine learning using time series data 160A. The learning unit 120 stores the overvoltage estimation model 160B generated in this way in the storage unit 160.

Further, the learning unit 120 acquires an estimated value of overvoltage using the learned overvoltage estimation model 160B, and learns an error estimation model 160D based on the estimated value and the theoretical value of overvoltage derived from the overvoltage theoretical formula 160C. . Although details will be described later, the learning unit 120 learns the relationship between the difference between the estimated value of overvoltage and the theoretical value and the discharge condition, thereby creating an error that gives an estimation error of the estimated overvoltage value in response to the input of the discharge condition. An estimated model 160D is generated. The learning unit 120 stores the error estimation model 160D generated in this way in the storage unit 160.

The estimation unit 130 estimates the overvoltage value of the battery 40 as an index value of the degree of deterioration thereof. Specifically, the estimation unit 130 is trained based on the overvoltage estimation value obtained based on the overvoltage estimation model 160B learned by the learning unit 120, and the overvoltage theoretical formula 160C based on electrochemical laws. Based on the overvoltage estimation error estimated by the error estimation model 160D, an estimated value of the overvoltage is determined as a final diagnosis result of the degree of deterioration. In the following, in order to make the explanation easier to understand, the overvoltage value estimated based on the overvoltage estimation model 160B will be referred to as the "first overvoltage estimated value", and the overvoltage estimated as the final diagnosis result of the degree of deterioration will be referred to as the "first overvoltage estimated value". The value of is sometimes referred to as a "second overvoltage estimated value."

The input unit 140 includes input devices such as a mouse, keyboard, trackball, switch, button, joystick, and touch panel. The input unit 140 receives inputs of various operations by the user of the battery diagnostic device 100. For example, the input unit 140 receives input of a discharge condition to be described later, and outputs the input discharge condition to the estimation unit 130.

The display unit 150 is configured using a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. The display unit 150 displays various information regarding the operation of the battery diagnostic device 100. For example, the display unit 150 displays the diagnosis result of the battery 40 by the estimation unit 130. Note that the display of the diagnosis result by the display unit 150 is an example of output of diagnosis result information. In addition, the output of the diagnosis result may be communication of the diagnosis result information, recording of the diagnosis result information, or audio output of the diagnosis result information.

The storage unit 160 is a storage device such as an HDD, SSD, or flash memory. The storage unit 160 is used as a storage area for various information in the battery diagnostic device 100. For example, the storage unit 160 stores the above-described time series data 160A, an overvoltage estimation model 160B, an overvoltage theoretical formula 160C, an error estimation model 160D, and the like. Here, the overvoltage estimation model 160B is an example of "overvoltage estimation information". The overvoltage theoretical formula 160C is an example of "theoretical value information". The error estimation model 160D is an example of "error estimation information".

FIG. 4 is a diagram showing an example of overvoltage theoretical formula 160C. The graph of FIG. 4 is a diagram showing a specific example of the relationship between overvoltage and temperature predicted based on Arrhenius law for the battery 40. Overvoltage theoretical formula 160C is a theoretical formula that expresses the relationship between overvoltage and temperature as shown in Figure 4, as well as a theoretical formula that expresses the relationship between overvoltage and current, and a theoretical expression that expresses the relationship between overvoltage and time. Includes theoretical formulas, etc. In this case, a theoretical formula expressing the relationship between overvoltage and current can be created based on, for example, Ohm's law or Tafel formula. For example, according to Ohm's law, a theoretical formula in which the overvoltage changes in proportion to the current is obtained, and according to the Tafel equation, a theoretical formula in which the overvoltage changes in proportion to the logarithm of the current is obtained. Further, a theoretical formula expressing the relationship between overvoltage and time can be created based on, for example, Cottrell's formula or Fick's law. For example, according to Cottrell's equation and Fick's law, overvoltage changes in proportion to the square root of time shortly after energization starts, and asymptotically approaches a constant value as energization time increases. A theoretical formula that changes to is obtained.

The learning unit 120 applies the discharging conditions of the battery 40 to such overvoltage theoretical formula 160C, thereby acquiring the theoretical value of the overvoltage of the battery 40 under the discharging conditions. On the other hand, the learning unit 120 applies the same discharging condition to the overvoltage estimation model 160B, thereby obtaining a first estimated overvoltage value as the overvoltage of the battery 40 under the discharging condition. Based on the first estimated overvoltage value and the theoretical value obtained in this way, the learning unit 120 creates a model for estimating the error between the estimated value and the theoretical value regarding the overvoltage of the battery 40 (hereinafter referred to as "error estimation model"). ) is generated.

The battery diagnostic device 100 of this embodiment generates the error estimation model described above in advance before diagnosing the degree of deterioration of the battery 40, and uses the error estimation model in the estimation result of the overvoltage of the battery 40 by the overvoltage estimation model 160B. The final diagnosis result of the degree of deterioration is output in consideration of the estimation error obtained by the model. According to the battery diagnostic device 100 of the present embodiment configured in this way, the state of deterioration of the battery 40 can be recognized by taking into consideration errors that can be assumed based on electrochemical factors. The degree of deterioration of can be estimated more appropriately.

Hereinafter, the process executed by the battery diagnostic device 100 to diagnose the degree of deterioration of the battery 40 will be described in more detail. In the following, the process up to the generation of an overvoltage estimation model and an error estimation model prior to diagnosing the battery 40 will be referred to as "preprocessing", and the overvoltage estimation model and error estimation model generated in the preprocessing will be used to diagnose the battery 40. The process of diagnosing the degree of deterioration is defined as "main process."

FIG. 5 is a flowchart illustrating an example of a process flow in which battery diagnostic device 100 generates an overvoltage estimation model and an error estimation model by performing preprocessing. First, in the battery diagnostic device 100, the learning unit 120 learns the overvoltage estimation model 160B for estimating the overvoltage of the battery 40 by performing machine learning using the time series data 160A acquired by the acquisition unit 110. (Step S101). The learning unit 120 stores the overvoltage estimation model 160B obtained through learning in the storage unit 160 as a learned model.

Subsequently, the learning unit 120 estimates the overvoltage of the battery 40 at a plurality of different temperatures by applying the time series data 160A to the overvoltage estimation model 160B generated in step S101 (step S102). This corresponds to estimating the temperature dependence of the overvoltage of the battery 40.

Next, the learning unit 120 performs curve fitting on the temperature dependence estimation result obtained in step S102 based on the overvoltage theoretical formula 160C (step S103), and compares the overvoltage-temperature curve obtained by the fitting with the temperature An error estimation model for the overvoltage estimation model is generated based on the difference data with the dependency estimation result (step S104). As described above, the error estimation model is a model for estimating the error assumed for the overvoltage estimate (first overvoltage estimate) estimated by the overvoltage estimation model. The learning unit 120 stores the error estimation model 160D generated in this way in the storage unit 160.

FIG. 6 is a diagram illustrating an outline of a method for generating an error estimation model. Graph G61 is an image diagram showing the overvoltage estimation result obtained in step S102. For example, the learning unit 120 first obtains an estimated value of overvoltage for each discharge condition by applying the overvoltage estimation model to each discharge condition included in the time series data 160A. The learning unit 120 can plot the graph G61 by finding a representative value for each temperature of the obtained estimated values of overvoltage. The representative value is typically an average value, but other statistical values such as the median, mode, maximum value, and minimum value may also be used. That is, graph G61 shows the typical temperature dependence of overvoltage for the entire battery 40 for which time series data 160A (battery information) was acquired.

Graph G62 is an image diagram showing curve L62 as a result of curve fitting in step S103. For example, the learning unit 120 can obtain the curve L62 by fitting the overvoltage theoretical formula 160C illustrated in FIG. 4 to the graph G61. In addition, when the overvoltage theoretical formula 160C is provided including unknown coefficients, the curve L62 may be determined by adjusting the position in the vertical and horizontal directions and changing the scale to identify the overvoltage theoretical formula 160C in curve fitting. .

Graph G63 is an image diagram showing curve L63 as the error estimation model generated in step S104. For example, the learning unit 120 plots the point group of the graph G63 by taking the difference between the temperature-dependent curve L62 determined by the curve fitting in step S103 and the estimation result plotted on the graph G61, and Curve L63 of the error estimation model can be determined by performing curve fitting on the point group plotted on G63. Note that any optimization method typified by the least squares method, the undetermined coefficient method, etc. may be used for the above-mentioned curve fitting.

FIG. 7 is a flowchart showing an example of a process flow in which the battery diagnostic device 100 diagnoses the degree of deterioration of the battery 40 by executing this process. First, in the battery diagnostic device 100, the input unit 140 receives input of discharge conditions for diagnosing the degree of deterioration of the battery 40 (step S201). The discharging conditions are conditions regarding usage conditions (variables) assumed for the battery 40. As described above, in this embodiment, temperature is assumed as one of the variables of the overvoltage estimation model 160B and the error estimation model 160D, so here it is assumed that discharge conditions including at least temperature are input. Hereinafter, the temperature value given as a discharge condition will be referred to as a temperature condition. The input unit 140 supplies the input temperature conditions to the estimation unit 130.

Next, the estimation unit 130 applies the discharge conditions including the temperature conditions supplied from the input unit 140 to the overvoltage estimation model 160B, thereby calculating a first estimated overvoltage value for the battery 40 used under the temperature conditions. (Step S202).

On the other hand, the estimation unit 130 estimates the error of the first overvoltage estimation value estimated by the overvoltage estimation model 160B for the battery 40 by applying the temperature condition supplied from the input unit 140 to the error estimation model 160D. (Step S203).

Subsequently, the estimating unit 130 calculates the estimated value for the battery 40 used under the temperature condition based on the first overvoltage estimation value obtained in step S202 and the overvoltage estimation error obtained in step S202. The range of overvoltage to be obtained is calculated (step S204).

The estimation unit 130 outputs the overvoltage range acquired in step S204 as a diagnosis result of the deterioration state of the battery 40 (step S205).

As mentioned above, the case where the temperature condition is specified as the discharge condition to be applied to the error estimation model as the discharge condition has been described, but the discharge condition corresponds to the variables of the overvoltage estimation model 160B and the error estimation model 160D. If any, discharge conditions other than temperature conditions may be specified. For example, when current is included in the variables of overvoltage estimation model 160B and error estimation model 160D, a discharge condition including the current value may be specified. Further, for example, when voltage is included in the variables of the overvoltage estimation model 160B and the error estimation model 160D, a discharge condition including the voltage value may be specified.

FIG. 8 is an image diagram illustrating a method of diagnosing the degree of deterioration of the battery 40. Graph G81 shows a curve L81 indicating the first overvoltage estimate estimated in step S202 and a curve L82 indicating the error of the first overvoltage estimate estimated in step S203 under different discharge conditions other than temperature. is expressed on the horizontal axis. Other discharge conditions are not limited to specific conditions, and may be, for example, time, current, voltage, etc.

In this case, the estimation unit 130 can obtain the range of possible overvoltage for the battery 40 as a diagnosis result by calculating the sum or difference between the curve L81 and the curve L82. Graph G82 shows a display example of such a diagnosis result. For example, the curve L83 shows the sum of the curve L81 and the curve L82, and can show the upper limit for the estimated value of overvoltage. On the other hand, the curve L84 shows the difference between the curve L81 and the curve L82, and can show the lower limit of the estimated value of overvoltage.

In this way, when one of the discharging conditions of the battery 40 (temperature in this case) is given, the dependence of overvoltage on other discharging conditions is displayed as a diagnostic result of the degree of deterioration of the battery 40. The user can appropriately grasp the deterioration status of the battery 40. This allows the user to macroscopically control the progress of deterioration of the plurality of batteries 40 put on the market, and to formulate an appropriate development plan for battery product development.

In the example of FIG. 8, the case where the overvoltage estimation model 160B and the error estimation model 160D have temperature and time as variables related to the discharge conditions, and the time dependence of the overvoltage is shown when the temperature conditions are given is explained. . However, the overvoltage estimation model 160B and the error estimation model 160D may have a single variable, or may have three or more variables. When the overvoltage estimation model 160B and the error estimation model 160D have a single variable, the estimated value of the overvoltage is uniquely determined by specifying the discharge condition by the variable. Therefore, in such a case, the estimation unit 130 may be configured to output an estimated range of overvoltage for the given discharge condition. In addition, when the overvoltage estimation model 160B and the error estimation model 160D have three or more variables, the estimating unit 130 determines that the overvoltage other than the overvoltage is It may be configured to indicate dependence on more than one variable. For example, when a discharge condition is given for one of the three variables, the estimation unit 130 is configured to display a three-dimensional graph that uniquely determines the estimated value of overvoltage for the other two variables. may be done.

According to the battery diagnostic device 100 of the embodiment configured as described above, the overvoltage estimation model 160B for estimating the overvoltage of the battery 40 based on the variable representing the discharge condition of the battery 40, and the Based on the overvoltage theoretical formula 160C that gives the theoretical value of overvoltage, the estimated value and theoretical value of overvoltage corresponding to the specified variable are derived, and based on the derived estimated value and theoretical value, the above specified variable is calculated. By estimating the degree of deterioration of the battery corresponding to , the degree of deterioration of the battery 40 can be estimated more appropriately.

<Modified example>
In the battery diagnostic device 100, the learning unit 120 may generate the overvoltage estimation model 160B and the error estimation model 160D as different models for each variable. In this case, the battery diagnostic device 100 stores an overvoltage theoretical formula 160C that gives an estimated value of overvoltage for each variable input in the storage unit 160, and calculates the overvoltage theoretical formula 160C according to the type of input variable. You can use them differently. That is, the estimation unit 130 may select different theoretical formulas depending on the type of variable, and calculate the theoretical value using the selected theoretical formula. Furthermore, in this case, the estimation unit 130 may be configured to calculate a total estimated value (for example, an integrated value of estimated values) based on the estimated values of overvoltage calculated for each variable. Similarly, in this case, the estimation unit 130 may be configured to calculate a total estimation error (for example, an integrated value of estimation errors) based on the overvoltage estimation error calculated for each variable.

Some of the functions that battery diagnostic device 100 has may be provided on each vehicle 10 side. For example, among the functional units included in battery diagnostic device 100, part or all of estimating unit 130, input unit 140, and display unit 150 may be included in vehicle 10. In this case, battery diagnostic device 100 may supply learning results (overvoltage estimation model 160B, error estimation model 160D) by learning section 120 to each vehicle 10. In this case, in the vehicle 10, the estimating unit 130 calculates whether the own vehicle The degree of deterioration of the battery 40 may be diagnosed and the diagnosis result may be displayed on the display unit 150. For example, the input unit 140 and the display unit 150 may be provided on the vehicle 10 side, and the vehicle 10 may diagnose the degree of deterioration of the battery 40 of its own vehicle by transmitting specified discharge conditions to the battery diagnostic device 100. The configuration may be such that the request is made to the device 100. In this case, the battery diagnostic device 100 may be configured to diagnose the degree of deterioration of the battery 40 based on the received discharge conditions and transmit the diagnosis result to the vehicle 10.

The embodiment described above can be expressed as follows.
a storage medium for storing computer-readable instructions;
a processor connected to the storage medium;
the processor executing the computer-readable instructions to:
A specified variable is calculated based on overvoltage estimation information for estimating the overvoltage of the battery based on a variable representing the discharge condition of the battery, and theoretical value information that gives a theoretical value of the overvoltage for the variable. Derive an estimated value and a theoretical value of the overvoltage corresponding to
estimating the degree of deterioration of the battery corresponding to the specified variable based on the derived estimated value and the theoretical value;
Battery diagnostic device.

Although the mode for implementing the present invention has been described above using embodiments, the present invention is not limited to these embodiments in any way, and various modifications and substitutions can be made without departing from the gist of the present invention. can be added.

DESCRIPTION OF SYMBOLS 1...Battery diagnosis system, 10...Vehicle, 12...Motor, 14...Drive wheel, 16...Brake device, 20...Vehicle sensor, 30...PCU, 32...Converter, 36...Control unit, 40...Battery, 42...Battery Sensor, 50... Communication device, 100... Battery diagnosis device, 110... Acquisition unit, 120... Learning unit, 130... Estimating unit, 140... Input unit, 150... Display unit, 160... Storage unit, 160A... Time series data, 160B ...Overvoltage estimation model, 160C...Overvoltage theoretical formula, 160D...Error estimation model

Claims (8)

a storage unit that stores overvoltage estimation information for estimating the overvoltage of the battery based on variables representing discharge conditions of the battery, and theoretical value information that provides a theoretical value of the overvoltage for the variable;
The estimated value and theoretical value of the overvoltage corresponding to the specified variable are derived based on the overvoltage estimation information and the theoretical value information, and the calculated value is applied to the specified variable based on the derived estimated value and the theoretical value. an estimation unit that estimates the degree of deterioration of the corresponding battery;
A battery diagnostic device comprising: The estimation unit obtains error estimation information indicating a relationship between the error of the estimated value and the theoretical value and the variable, and estimates the degree of deterioration of the battery based on the obtained error estimation information.
The battery diagnostic device according to claim 1. When the value of the variable for estimating battery overvoltage is input, the estimating unit outputs an error range of the estimated value for battery overvoltage at the value of the variable based on the error estimation information. do,
The battery diagnostic device according to claim 2. The variables include the temperature of the environment in which the battery was used, the current when the battery was used, or the time period in which the battery was used.
The battery diagnostic device according to claim 2 or 3. The estimation unit selects a different theoretical formula depending on the type of the variable, and calculates the theoretical value using the selected theoretical formula.
The battery diagnostic device according to claim 4. When a plurality of types of the variables are given, the estimation unit calculates the error for each of the plurality of variables, and obtains an integrated value of overvoltage errors for each variable as a degree of battery deterioration.
The battery diagnostic device according to claim 4. The computer is
A specified variable is calculated based on overvoltage estimation information for estimating the overvoltage of the battery based on a variable representing the discharge condition of the battery, and theoretical value information that gives a theoretical value of the overvoltage for the variable. Derive an estimated value and a theoretical value of the overvoltage corresponding to
estimating the degree of deterioration of the battery corresponding to the specified variable based on the derived estimated value and the theoretical value;
Battery diagnosis method. to the computer,
A specified variable is calculated based on overvoltage estimation information for estimating the overvoltage of the battery based on a variable representing the discharge condition of the battery, and theoretical value information that gives a theoretical value of the overvoltage for the variable. Derive an estimated value and a theoretical value of the overvoltage corresponding to
estimating the degree of deterioration of the battery corresponding to the designated variable based on the derived estimated value and the theoretical value;
program for.

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