Battery characteristic estimation device, battery characteristic estimation method, and storage medium

Abstract

A storage battery characteristic estimation device, a storage battery characteristic estimation method, and a storage medium that reduce the computational load required for optimization processing of alleviation parameters. Provided are: an acquisition unit that acquires time series data of at least voltage and current of a secondary battery having a positive electrode including a positive electrode active material and a negative electrode including a negative electrode active material; a storage unit that stores a reference positive electrode OCP curve and a reference negative electrode OCP curve; an OCV curve estimation unit that transforms the reference positive electrode OCP curve into a positive electrode OCP curve with a first parameter group, transforms the reference negative electrode OCP curve into a negative electrode OCP curve with a second parameter group, and estimates an OCV curve representing the change in open-circuit voltage of the secondary battery with respect to capacity change based on the difference between the positive electrode OCP curve and the negative electrode OCP curve; and an optimization unit that optimizes the first and second parameter groups so that an error function value representing the error of the OCV curve estimated by the OCV curve estimation unit and the time series data is equal to or less than a predetermined value.

Description

Technical Field

[0001] This invention relates to a battery characteristic estimation device, a battery characteristic estimation method, and a storage medium. Background Technology

[0002] Previously, techniques for estimating the degradation state of lithium-ion batteries were known. For example, in Japanese Patent Application Publication No. 2020-046420, the following technique was known: calculating the surface stress of the negative electrode active material based on a specified active material model; calculating the change in open circuit potential of the negative electrode active material from the change in open circuit potential based on the calculated surface stress; correcting the negative electrode open circuit potential based on the calculated change in open circuit potential; and estimating the OCV (open circuit voltage) curve based on the corrected negative electrode open circuit potential. Summary of the Invention

[0003] In the technology described in Patent Document 1, three parameters are optimized to make the measured OCV curve approximately consistent with the estimated OCV curve estimated based on the corrected negative open-circuit potential, thereby estimating the OCV curve. However, in the prior art, when optimizing the parameters, it is sometimes necessary to perform differential processing on the measured OCV data, and the computational load required for the optimization processing sometimes becomes too large.

[0004] The present invention was made in consideration of such circumstances, and one of its objectives is to provide a battery characteristic estimation device, a battery characteristic estimation method, and a storage medium that can reduce the computational load required for parameter optimization processing.

[0005] The battery characteristic estimation device, battery characteristic estimation method and storage medium of the present invention adopt the following structure.

[0006] (1): One aspect of the present invention relates to a battery characteristic estimation device, wherein the battery characteristic estimation device comprises: an acquisition unit that acquires time series data of at least voltage and current of a secondary battery, the secondary battery having a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material; a storage unit that stores a reference positive electrode OCP curve and a reference negative electrode OCP curve, the reference positive electrode OCP curve representing the change in the open circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve representing the change in the open circuit potential of the negative electrode relative to the discharge capacity; and an OCV curve estimation unit that, according to... The first parameter set transforms the reference positive electrode OCP curve into a positive electrode OCP curve, and the second parameter set transforms the reference negative electrode OCP curve into a negative electrode OCP curve. The OCV curve estimation unit estimates an OCV curve representing the change in open-circuit voltage of the secondary battery relative to the capacity change based on the difference between the positive electrode OCP curve and the negative electrode OCP curve. The optimization unit optimizes the first parameter set and the second parameter set so that the value of the error function representing the error between the OCV curve estimated by the OCV curve estimation unit and the time series data becomes below a predetermined value.

[0007] (2): Based on the above (1) scheme, the first parameter group includes the positive electrode expansion / contraction rate for transforming the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the positive electrode shift amount from the reference positive electrode OCP curve to the positive electrode OCP curve in the discharge capacity direction. The second parameter group includes the negative electrode expansion / contraction rate for transforming the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the negative electrode shift amount from the reference negative electrode OCP curve to the negative electrode OCP curve in the discharge capacity direction.

[0008] (3): Based on the above (1) scheme, the first parameter set includes the positive electrode scaling factor for transforming the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the positive electrode shift amount from the reference positive electrode OCP curve to the positive electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor for transforming the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the negative electrode OCP curve relative to the positive electrode OCP curve in the discharge capacity direction. The relative shift amount, or, the first parameter set includes the positive electrode scaling factor that transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the relative shift amount of the positive electrode OCP curve relative to the negative electrode OCP curve in the discharge capacity direction, and the second parameter set includes the negative electrode scaling factor that transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the shift amount from the reference negative electrode OCP curve to the negative electrode OCP curve in the discharge capacity direction, i.e., the negative electrode shift amount.

[0009] (4): Based on the above scheme (1) or (2), the positive electrode active material is a material containing at least one of NCM (nickel cobalt manganese), NCA (nickel cobalt aluminum), LFP (lithium ferrophosphate) and LMO (lithium manganese oxide), and the negative electrode active material is a material containing at least one of hard carbon and graphite. The optimization unit sets the negative electrode expansion and contraction rate based on a mapping representing the change in the negative electrode expansion and contraction rate relative to the cumulative value of the battery's usage time or charge and discharge amount. The optimization unit optimizes the first parameter set and the second parameter set excluding the negative electrode expansion and contraction rate so that the value of the error function representing the error between the time series data and the error is below a predetermined value.

[0010] (5): Based on any of the above schemes (1) to (4), the battery characteristic estimation device further includes a data filtering unit, which extracts data with small voltage changes caused by charging and discharging from the time series data, and the optimization unit performs optimization so that the error between the OCV curve and the data extracted by the data filtering unit is below the specified value.

[0011] (6): Based on the above scheme (5), the error function is a function that increases according to the following total value, which is the sum of the values ​​obtained by multiplying the error between the OCV curve and each data extracted by the data filtering unit by a weight. The weight is set as follows: the number of data extracted by the data filtering unit is counted for multiple intervals of discharge capacity or voltage value. The more data counted, the smaller the value of the weight corresponding to the interval.

[0012] (7): Another aspect of the present invention relates to a method for estimating the characteristics of a storage battery, wherein the method causes a computer to perform the following processing: acquiring time series data of at least the voltage and current of a secondary battery having a positive electrode containing positive active material and a negative electrode containing negative active material; storing a reference positive electrode OCP curve and a reference negative electrode OCP curve, the reference positive electrode OCP curve representing the change in the open circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve representing the change in the open circuit potential of the negative electrode relative to the discharge capacity; transforming the reference positive electrode OCP curve into a positive electrode OCP curve according to a first parameter group, and transforming the reference negative electrode OCP curve into a negative electrode OCP curve according to a second parameter group; estimating an OCV curve representing the change in the open circuit voltage of the secondary battery relative to the capacity change based on the difference between the positive electrode OCP curve and the negative electrode OCP curve; and optimizing the first parameter group and the second parameter group so that the value of the error function representing the error between the estimated OCV curve and the time series data is below a predetermined value.

[0013] (8): Another aspect of the present invention relates to a storage medium storing a program, wherein the program causes a computer to perform the following processing: acquiring time-series data of at least voltage and current of a secondary battery having a positive electrode containing a positive active material and a negative electrode containing a negative active material; storing a reference positive electrode OCP curve and a reference negative electrode OCP curve, the reference positive electrode OCP curve representing the change in open-circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve representing the change in open-circuit potential of the negative electrode relative to the discharge capacity; transforming the reference positive electrode OCP curve into a positive electrode OCP curve according to a first set of parameters, and transforming the reference negative electrode OCP curve into a negative electrode OCP curve according to a second set of parameters; estimating an OCV curve representing the change in open-circuit voltage of the secondary battery relative to the capacity change based on the difference between the positive electrode OCP curve and the negative electrode OCP curve; and optimizing the first set of parameters and the second set of parameters so that the value of an error function representing the error between the estimated OCV curve and the time-series data is below a predetermined value.

[0014] According to the schemes (1) to (8), the computational load required for parameter optimization can be reduced.

[0015] According to the scheme in (4), even if data in the low SOC region cannot be obtained, parameter optimization can still be performed.

[0016] According to the scheme in (5), by performing optimization processing only on data that can be regarded as OCV, the OCV curve can be estimated with higher accuracy.

[0017] According to the scheme in (6), the fitting bias caused by fluctuations in the amount of data can be suppressed. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating an example of the structure of a vehicle using a battery characteristic estimation device according to an applicable embodiment.

[0019] Figure 2 This is a diagram illustrating an example of the structure of a battery characteristic estimation device according to an embodiment.

[0020] Figure 3 This is a diagram showing an example of a reference positive electrode OCP (open circuit potential) curve and a positive electrode OCP curve obtained by transforming the reference positive electrode OCP curve.

[0021] Figure 4 This is a diagram showing an example of the reference negative electrode OCP curve and the negative electrode OCP curve obtained by transforming the reference negative electrode OCP curve.

[0022] Figure 5 This is a diagram showing an example of an OCV curve derived from the positive electrode OCP curve and the negative electrode OCP curve.

[0023] Figure 6 This is a diagram used to illustrate the optimization process of the OCV curve performed by the optimization unit.

[0024] Figure 7 This is a flowchart illustrating an example of the process performed by a battery characteristic estimation device.

[0025] Figure 8 This is a flowchart illustrating an example of the OCV curve generation process performed by the OCV curve estimation unit.

[0026] Figure 9 This is a flowchart illustrating another example of the process of generating OCV curves performed by the OCV curve estimation unit. Detailed Implementation

[0027] Hereinafter, embodiments of the battery characteristic estimation device, battery characteristic estimation method and storage medium of the present invention will be described with reference to the accompanying drawings.

[0028] [Vehicle Structure]

[0029] Figure 1 This is a diagram illustrating an example of the structure of a vehicle 10 with a battery characteristic estimation device 100 according to an applicable embodiment. Figure 1 The vehicle 10 shown is a BEV (Battery Electric Vehicle) driven by an electric motor powered by electricity supplied from a storage battery (secondary battery). Alternatively, the vehicle 10 may be a PHV (Plug-in Hybrid Vehicle) or PHEV (Plug-in Hybrid Electric Vehicle) that enables external charging. It should be noted that the vehicle 10 includes, for example, not only four-wheeled vehicles, but also two-wheeled vehicles of straddle type, three-wheeled vehicles (including vehicles with two front wheels and one rear wheel in addition to those with one front wheel and two rear wheels), electric bicycles, and electric boats, as well as all mobile bodies driven by an electric motor powered by electricity supplied from a storage battery.

[0030] Motor 12 is, for example, a three-phase AC motor. The rotor of motor 12 is connected to drive wheel 14. Motor 12 is driven by power supplied from the energy storage unit (not shown) of battery 40, transmitting rotational power to drive wheel 14. In addition, motor 12 uses the kinetic energy of vehicle 10 to generate electricity when vehicle 10 decelerates.

[0031] The braking device 16 may include, for example, a brake caliper, a hydraulic cylinder that transmits hydraulic pressure to the brake caliper, and an electric motor that generates hydraulic pressure in the hydraulic cylinder. The braking device 16 may also include, as a backup, a mechanism for transmitting hydraulic pressure generated by the user (driver) of the vehicle 10 operating the brake pedal (not shown) via the master hydraulic cylinder to the hydraulic cylinder. It should be noted that the braking device 16 is not limited to the structure described above, and may also be an electronically controlled hydraulic braking device that transmits hydraulic pressure from the master hydraulic cylinder to the hydraulic cylinder.

[0032] Vehicle sensors 20 include, for example, a throttle opening sensor, a vehicle speed sensor, and a brake pedal pressure sensor. The throttle opening sensor is mounted on the throttle pedal and detects the amount of operation performed by the driver on the throttle pedal, outputting the detected operation as the throttle opening to the control unit 36 ​​(described later). The vehicle speed sensor includes, for example, wheel speed sensors mounted on each wheel of the vehicle 10 and a speed computer, which synthesizes the wheel speeds detected by the wheel speed sensors to derive the vehicle speed (vehicle speed) and outputs it to the control unit 36. The brake pedal pressure sensor is mounted on the brake pedal and detects the amount of operation performed by the driver on the brake pedal, outputting the detected operation as the brake pedal pressure to the control unit 36.

[0033] PCU30, for example, includes a converter 32 and a VCU (Voltage Control Unit) 34. It should be noted that... Figure 1 The structure of PCU30, which unifies the above-mentioned components, is just one example. The above-mentioned components in vehicle 10 can also be configured separately.

[0034] Converter 32 is, for example, an AC-DC converter. The DC side terminal of converter 32 is connected to DC line DL. DC line DL is connected to battery 40 via VCU34. Converter 32 converts the AC power generated by motor 12 into DC power and outputs it to DC line DL.

[0035] VCU34 is, for example, a DC-DC converter. VCU34 boosts the power supplied from battery 40 and outputs it to DC line DL.

[0036] The control unit 36 ​​controls the drive of the motor 12 based on the output of the throttle opening sensor included in the vehicle sensor 20. The control unit 36 ​​also controls the braking device 16 based on the output of the brake pedal position sensor included in the vehicle sensor 20. The control unit 36 ​​further calculates, for example, the SOC (State of Charge) of the battery 40 (hereinafter also referred to as "battery charge rate") based on the output of the battery sensor 42 connected to the battery 40 (described later), and outputs it to the VCU 34. The VCU 34 increases the voltage of the DC line DL according to the instruction from the control unit 36.

[0037] The battery 40 is, for example, a secondary battery capable of repeated charging and discharging, such as a lithium-ion battery. The positive electrode active material constituting the positive electrode of the battery 40 is, for example, a material containing at least one of the following: NCM (nickel cobalt manganese), NCA (nickel cobalt aluminum), LFP (lithium ferrophosphate), and LMO (lithium manganese oxide). The negative electrode active material constituting the negative electrode of the battery 40 is, for example, a material containing at least one of the following: hard carbon, graphite, etc. Alternatively, the battery 40 may be a battery enclosure, such as a box-type enclosure, that is detachably mounted to the vehicle 10. The battery 40 stores electricity supplied from an external charger (not shown) to the vehicle 10 and discharges it for the purpose of driving the vehicle 10.

[0038] 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 (hereinafter referred to as "battery 40") constituting the battery 40 using the current sensor, detects the voltage of the battery 40 using the voltage sensor, and detects the temperature of the battery 40 using the temperature sensor. The battery sensor 42 outputs the detected data of the current, voltage, and temperature of the battery 40 to the control unit 36 ​​and the communication device 50.

[0039] The communication device 50 includes a wireless module for connecting to a cellular network or a Wi-Fi network. The communication device 50 may also include a wireless module for utilizing Bluetooth (registered trademark), etc. The communication device 50 transmits and receives various information related to the vehicle 10, for example, with the battery characteristic estimation device 100 via communication in the wireless module. The communication device 50 transmits data on the physical quantities of the battery 40 output by the control unit 36 ​​or the battery sensor 42 to the battery characteristic estimation device 100. The communication device 50 may also receive information indicating the characteristics of the battery 40 diagnosed and transmitted by the battery characteristic estimation device 100 (described later), and output the received information indicating the characteristics of the battery 40 to the HMI (not shown) of the vehicle 10.

[0040] [Structure of the battery characteristic estimation device]

[0041] Next, an example of a battery characteristic estimation device 100 for estimating the characteristics of the battery 40 of the vehicle 10 will be described. Figure 2This diagram illustrates an example of the structure of the battery characteristic estimation device 100 according to an embodiment. The battery characteristic estimation device 100 includes, for example, an acquisition unit 110, a data filtering unit 120, an OCV curve estimation unit 130, an optimization unit 140, and a storage unit 150. The acquisition unit 110, data filtering unit 120, OCV curve estimation unit 130, and optimization unit 140 are implemented, for example, by executing a program (software) using a hardware processor such as a CPU (Central Processing Unit). Some or all of these components can be implemented by hardware (including the circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit), or through a combination of software and hardware. The program can be pre-saved on storage devices such as HDDs (Hard Disk Drives) and flash memory (storage devices with non-transitory storage media), or it can be saved on removable storage media such as DVDs and CD-ROMs (non-transitory storage media), and installed by mounting the storage media onto a drive device. The storage unit 150 is, for example, an HDD, flash memory, or RAM (Random Access Memory). The storage unit 150 stores, for example, time-series data 150A, a reference positive OCP curve 150B, a reference negative OCP curve 150C, and an OCV curve 150D.

[0042] The acquisition unit 110 uses a communication interface (not shown) mounted on the battery characteristic estimation device 100 to acquire time-series data of at least the voltage and current in the battery 40 from the communication device 50, and stores it in the storage unit 150 as first-time acquisition data (not shown). The acquisition unit 110 further calculates the discharge capacity (discharge amount) by accumulating the current values ​​included in the acquired time-series data, and stores it in the storage unit 150 as time-series data 150A. At this time, the acquisition unit 110 can also perform processing to exclude data that is missing or abnormal in the acquired first-time acquisition data. Moreover, the discharge capacity can be calculated on the vehicle 10 side instead of in the battery characteristic estimation device 100, and then sent to the battery characteristic estimation device 100 via the communication device 50.

[0043] The data filtering unit 120 extracts data from the acquired data obtained by the acquisition unit 110 and stored in the storage unit 150, specifically data where the voltage change due to charging and discharging is small, i.e., the voltage change is below a predetermined value. The voltage change is the amount of voltage change over a reference time. The acquisition unit 110 also extracts time-series data of the current of the battery 40 from the communication device 50. The data filtering unit 120 can extract data from the extracted time-series data where the current value is below a predetermined value. Additionally, the data filtering unit 120 can also extract data where the voltage change is below a first predetermined value and the current value is below a second predetermined value. Thus, it is possible to obtain time-series data of the voltage and discharge capacity of the battery 40 at an OCV (Open Voltage Capacity) time. The data filtering unit 120 stores the extracted time-series data as time-series data 150A in the storage unit 150.

[0044] The OCV curve estimation unit 130 transforms the reference positive electrode OCP curve 150B into a positive electrode OCP curve 150B# representing the change in the open-circuit potential of the positive electrode relative to the discharge capacity, according to the first parameter set described later. It also transforms the reference negative electrode OCP curve 150C into a negative electrode OCP curve 150C# representing the change in the open-circuit potential of the negative electrode relative to the discharge capacity, according to the second parameter set described later. Based on the difference between the positive electrode OCP curve 150B# and the negative electrode OCP curve 150C# obtained through the transformation, it estimates the OCV curve 150D representing the change in the open-circuit voltage of the battery 40 relative to the capacity change. The OCV curve estimation unit 130 stores the estimated OCV curve 150D in the storage unit 150.

[0045] The optimization unit 140 optimizes the first parameter set and the second parameter set so that the value of the error function, which represents the error between the OCV curve 150D estimated by the OCV curve estimation unit 130 and the time series data 150A extracted by the data filtering unit 120, is below a predetermined value. The OCV curve 150D optimized by the optimization unit 140 represents the finally estimated characteristics of the battery 40. The specific optimization process performed by the optimization unit 140 will be described later.

[0046] Figure 3 This is a diagram showing an example of the reference positive electrode OCP curve 150B and the positive electrode OCP curve 150B# obtained by transforming the reference positive electrode OCP curve 150B. Figure 3 The left side represents the reference positive electrode OCP curve 150B. Figure 3 The right side represents the positive electrode OCP curve 150B# obtained by transforming the reference positive electrode OCP curve 150B.

[0047] like Figure 3As shown on the left, the reference positive OCP curve 150B represents the mathematical model f. ca (x), the mathematical model f ca (x) serves as the reference for deriving the positive electrode OCP curve 150B#, which represents the change in the open-circuit potential of the positive electrode relative to the discharge capacity. The width of the discharge capacity x is normalized to 1. The OCV curve estimation unit 130 uses the positive electrode scaling factor a, which transforms the normalized discharge capacity width of the positive electrode to the actual discharge capacity width, and the positive electrode shift amount b, which is the shift in the discharge capacity direction from the reference positive electrode OCP curve 150B to the positive electrode OCP curve 150B#, to transform the reference positive electrode OCP curve 150B into the positive electrode OCP curve 150B#.

[0048] More specifically, the OCV curve estimation unit 130 transforms the dimensionless variable x into a variable X with the dimension of discharge capacity (Ah) through X = ax + b, and substitutes x = (Xb) / a into f. ca (x), from which we obtain the mathematical model F representing the positive electrode OCP curve 150B#. ca (X). Thus, the positive electrode expansion / contraction rate a and the positive electrode displacement b are examples of the "first parameter group".

[0049] Figure 4 This is a diagram showing an example of the reference negative electrode OCP curve 150C and the negative electrode OCP curve 150C# obtained by transforming the reference negative electrode OCP curve 150C. Figure 4 The left side represents the reference negative electrode OCP curve at 150°C. Figure 4 The right side represents the negative electrode OCP curve 150C# obtained by transforming the reference negative electrode OCP curve 150C.

[0050] like Figure 4 As shown on the left, the reference negative electrode OCP curve 150C represents the mathematical model f. an (x), the mathematical model f an (x) serves as the reference for deriving the negative electrode OCP curve 150C#, which represents the change in the open-circuit potential of the negative electrode relative to the discharge capacity. The width of the discharge capacity x is normalized to 1. The OCV curve estimation unit 130 uses the negative electrode scaling factor c, which transforms the normalized discharge capacity width of the negative electrode to the actual discharge capacity width, and the negative electrode shift amount d, which is the shift in the discharge capacity direction from the reference negative electrode OCP curve 150C to the negative electrode OCP curve 150C#, to transform the reference negative electrode OCP curve 150C into the negative electrode OCP curve 150C#.

[0051] More specifically, the OCV curve estimation unit 130 transforms the dimensionless variable x into a variable X with the dimension of discharge capacity (Ah) through X = cx + d, and substitutes x = (Xd) / c into f. an (x), from which we obtain the mathematical model F representing the negative electrode OCP curve 150C#. an (X). Thus, the negative electrode expansion / contraction rate c and the negative electrode shift d are examples of the "second parameter set".

[0052] It should be noted that, in Figure 3 and Figure 4 In the example of reference positive electrode OCP curve 150B and reference negative electrode OCP curve 150C, the amplitude of discharge capacity x is normalized to 1. However, the present invention is not limited to such a structure. More generally, the reference positive electrode OCP curve 150B and reference negative electrode OCP curve 150C can function as a reference for optimizing the first set of parameters and the second set of parameters, or they can be normalized to arbitrary values.

[0053] Furthermore, in the above description, the first parameter set is set to the positive electrode expansion / contraction rate *a* and the positive electrode shift amount *b*, and the second parameter set is set to the negative electrode expansion / contraction rate *c* and the negative electrode shift amount *d*. However, the present invention is not limited to this structure. For example, the first parameter set may be set to the positive electrode expansion / contraction rate *a* and the positive electrode shift amount *b*, and the second parameter set may be set to the negative electrode expansion / contraction rate *c* and the relative shift amount *e* of the negative electrode OCP curve 150C# relative to the positive electrode OCP curve 150B# in the discharge capacity direction. In this case, the OCV curve estimation unit 130 can derive the negative electrode shift amount *d* based on the positive electrode shift amount *b* and the relative shift amount *e*. Alternatively, for example, the first parameter set may be set to the positive electrode expansion / contraction rate *a* and the relative shift amount *e* of the positive electrode OCP curve 150B# relative to the negative electrode OCP curve 150C# in the discharge capacity direction, and the second parameter set may be set to the negative electrode expansion / contraction rate *c* and the negative electrode shift amount *d*. In this case, the OCV curve estimation unit 130 can derive the positive electrode shift amount b based on the negative electrode shift amount d and the relative shift amount e.

[0054] Figure 5 This is a diagram illustrating an example of the OCV curve 150D derived from the positive electrode OCP curve 150B# and the negative electrode OCP curve 150C#. (See diagram for example.) Figure 5 As shown, the OCV curve estimation unit 130 obtains the curve from the data in the OCV curve estimation unit 130. Figure 3 The positive electrode OCP curve obtained in the process is 150B# minus the value obtained in the process. Figure 4The negative electrode OCP curve 150C# obtained in the process is used to estimate the OCV curve 150D. The optimization unit 140 then optimizes the first parameter set and the second parameter set so that the value of the error function representing the error between the estimated OCV curve 150D and the time series data 150A is below a predetermined value.

[0055] Figure 6 This is a diagram used to illustrate the optimization process of the OCV curve 150D performed by the optimization unit 140. For example... Figure 6 As shown, the optimization unit 140 optimizes the first parameter set and the second parameter set so that the value of the error function, which represents the error between the OCV curve 150D estimated by the OCV curve estimation unit 130 and the time series data 150A, is below a predetermined value. More specifically, the optimization unit 140 uses, for example, local optimization algorithms such as the BFGS method, the conjugate gradient method, and the COBYLA method, as well as global optimization algorithms such as the genetic algorithm, the differential evolution method, the SHGO method, and the simulated annealing method, to optimize the first parameter set and the second parameter set so that the value of the error function is below a predetermined value.

[0056] At this time, the optimization unit 140 sets the function that increases based on the following total value as the error function, which is the sum of the values ​​obtained by multiplying the error between the OCV curve 150D and each data extracted by the data filtering unit 120 by a weight. More specifically, the optimization unit 140 first divides the discharge capacity (Ah) into predetermined intervals I1, I2, I3, ..., and calculates the amount of data n1, n2, n3, ... contained in each interval. Then, the optimization unit 140 takes the reciprocal of each data amount n1, n2, n3, ... to adjust the weight w for each interval. k Calculate to be w k =(1 / n k ) / (sum(1 / n i Next, the optimization unit 140, as shown in Equation (1) below, uses the calculated weights to define the weighted root mean square error (RMSE) as an error function.

[0057]

[0058] It should be noted that, in Figure 6 The text describes how the amount of 150A of time-series data is counted in each interval of discharge capacity (i.e., along the horizontal axis), and the weight w corresponding to that interval is calculated. k Examples are given, but the invention is not limited to such structures. For instance, it is also possible to count the amount of 150A of time-series data in each interval of voltage (i.e., the vertical axis direction) and calculate the weight w corresponding to that interval. k Moreover, the weight w kThe calculation method is not limited to taking the reciprocal. More generally, the larger the data range, the smaller the value should be assigned.

[0059] In equation (1), pred i The estimated open-circuit voltage on the OCV curve 150D is represented by Acti, which represents the open-circuit voltage value recorded as time series data 150A. It should be noted that in equation (1), the error is calculated by taking the square root of the mean square error as an example, but the present invention is not limited to that structure, and any power of the mean square error can be taken. Alternatively, the optimization unit 140 can define the weighted average absolute error (MAE) as an error function using the calculated weights, as shown in equation (2) below. By performing such weighting, it is possible to prevent the OCV curve 150D from overfitting to the time series data 150A in the interval with a large amount of data.

[0060]

[0061] When the optimization unit 140 optimizes the positive electrode scaling ratio a and positive electrode shift b (as the first parameter set) and the negative electrode scaling ratio c and negative electrode shift d (as the second parameter set) based on the aforementioned error function, it constrains the relationship between the negative electrode scaling ratio c and the negative electrode shift d using an arbitrary function c = f(d). This is because, as Figure 4 As shown, in the negative electrode OCP curve 150C#, there is a tendency for the negative electrode OCP to take a roughly constant value before the discharge capacity reaches a predetermined point P. In particular, the optimization unit 140 can also constrain the negative electrode expansion / contraction rate c to a constant. Furthermore, when the optimization unit 140 uses the aforementioned relative shift amount e as a parameter, the relationship between the negative electrode expansion / contraction rate c and the relative shift amount e can be constrained using an arbitrary function c = g(e).

[0062] It should be noted that when the negative electrode of the 40-type battery is made of graphite-based material, the negative electrode OCP curve at 150°C# becomes flat within a small discharge capacity range, for example, for batteries located in... Figure 4 Determining the characteristic region after point P requires data from the low SOC (high discharge capacity) region. Sometimes, data from such a low SOC region is difficult to obtain. Therefore, the optimization unit 140 may not optimize the negative electrode expansion / contraction rate c, but instead maintain a mapping representing the change in the negative electrode expansion / contraction rate c relative to the battery's usage time or cumulative charge / discharge amount, and set the negative electrode expansion / contraction rate c by referring to this mapping. This further reduces the computational load required for parameter optimization processing.

[0063] Next, refer to Figure 7The process flow of the battery characteristic estimation device 100 of this embodiment will be explained. Figure 7 This is a flowchart illustrating an example of the process performed by the battery characteristic estimation device 100.

[0064] First, the battery characteristic estimation device 100 acquires at least the time-series data 150A of the voltage and current of the vehicle 10 (S101). Next, the battery characteristic estimation device 100 sets a first parameter set and a second parameter set, and based on these parameter sets, the reference positive electrode OCP curve 150B, and the reference negative electrode OCP curve 150C, generates an OCV curve 150D (S102). For a detailed flowchart of step S102, please refer to [link to relevant documentation]. Figure 8 and Figure 9 To be described later.

[0065] Next, the battery characteristic estimation device 100 calculates the error between the acquired time series data 150A and the generated OCV curve 150D (S103). Then, the battery characteristic estimation device 100 determines whether the calculated error is within a predetermined value (S104). If it is determined that the calculated error is not within the predetermined value, the battery characteristic estimation device 100 returns to step S102, re-sets the first and second parameter sets, and generates the OCV curve 150D. On the other hand, if it is determined that the calculated error is within the predetermined value, the battery characteristic estimation device 100 determines the first and second parameter sets set in step S102 as the optimized parameters (S105). Thus, the processing of this flowchart ends.

[0066] Next, refer to Figure 8 The process of generating OCV curve 150D by OCV curve estimation unit 130 will be explained. Figure 8 This is a flowchart illustrating an example of the process for generating the OCV curve 150D performed by the OCV curve estimation unit 130. The OCV curve estimation unit 130 first uses the positive electrode expansion / contraction rate a and the positive electrode shift amount b as the first parameter set to transform the reference positive electrode OCP curve 150B into the positive electrode OCP curve 150B# (S201). More specifically, the OCV curve estimation unit 130 uses a function f representing the reference positive electrode OCP curve 150B... ca The variable (x) is transformed into X = ax + b, thus obtaining the function F representing the positive electrode OCP curve 150B#. ca(X). Next, the OCV curve estimation unit 130 uses the negative electrode expansion / contraction rate c and the negative electrode shift amount d as the second parameter set to transform the reference negative electrode OCP curve 150C into a negative electrode OCP curve 150C# (S202). More specifically, the OCV curve estimation unit 130 uses the function f representing the reference negative electrode OCP curve 150C... an The variable (x) is transformed into X = cx + d, thus obtaining the function F representing the negative electrode OCP curve 150C#. an (X). Next, the OCV curve estimation unit 130 obtains the OCV curve 150D by subtracting the light negative electrode OCP curve 150C# from the positive electrode OCP curve 150B# (S203). Thus, the processing of this flowchart ends.

[0067] Next, refer to Figure 9 This will be used to explain the process of generating OCV curve 150D performed by OCV curve estimation unit 130. Figure 9 This is a flowchart illustrating another example of the process for generating the OCV curve 150D performed by the OCV curve estimation unit 130. The OCV curve estimation unit 130 first uses the positive electrode expansion / contraction rate a and the positive electrode shift amount b as first parameter sets to transform the reference positive electrode OCP curve 150B into the positive electrode OCP curve 150B# (S301). More specifically, the OCV curve estimation unit 130 uses a function f representing the reference positive electrode OCP curve 150B... ca The variable (x) is transformed into X = ax + b, thus obtaining the function F representing the positive electrode OCP curve 150B#. ca (X). Next, the OCV curve estimation unit 130 uses the positive electrode shift amount b, the negative electrode expansion / contraction rate c (as a second parameter group), and the relative shift amount e of the negative electrode OCP curve 150C# relative to the positive electrode OCP curve 150B# in the discharge capacity direction to transform the reference negative electrode OCP curve 150C into the negative electrode OCP curve 150C# (S302). More specifically, the OCV curve estimation unit 130 uses the function f representing the reference negative electrode OCP curve 150C... an The variable (x) is transformed into X = cx + b + e, thus obtaining the function F representing the negative electrode OCP curve 150C#. an (X). Next, the OCV curve estimation unit 130 generates the OCV curve 150D by subtracting the negative electrode OCP curve 150C# from the positive electrode OCP curve 150B# (S303). Thus, the processing of this flowchart ends.

[0068] It should be noted that, in Figure 9The flowchart illustrates an example where the OCV curve estimation unit 130 generates the OCV curve 150D using the positive electrode shift amount b and the relative shift amount e of the negative electrode OCP curve 150C# relative to the positive electrode OCP curve 150B# in the discharge capacity direction. However, when the OCV curve estimation unit 130 uses the negative electrode shift amount d and the relative shift amount e of the positive electrode OCP curve 150B# relative to the negative electrode OCP curve 150C# in the discharge capacity direction, it generates the OCV curve 150D using a function f representing the reference positive electrode OCP curve 150B. ca (x) The variable is transformed into X = ax + d + e to obtain the function F representing the positive electrode OCP curve 150B#. ca (X).

[0069] According to this embodiment as described above, the battery characteristic estimation device 100 optimizes the parameter set without performing differential processing, so that the value of the error function representing the error between the time series data obtained from the vehicle 10 and the OCV curve estimated based on the parameter set is below a predetermined value. This reduces the computational load required for parameter optimization processing.

[0070] The implementation methods described above can be performed as follows.

[0071] A battery characteristic estimation device is configured to include:

[0072] A storage device containing a program; and

[0073] Hardware processor,

[0074] The hardware processor executes the program stored in the storage device to perform the following processing:

[0075] Obtain time-series data of at least the voltage and discharge capacity of a secondary battery, wherein the secondary battery has a positive electrode containing positive electrode active material and a negative electrode containing negative electrode active material;

[0076] The storage includes a reference positive electrode OCP curve and a reference negative electrode OCP curve. The reference positive electrode OCP curve represents the change in the open circuit potential of the positive electrode relative to the normalized discharge capacity, and the reference negative electrode OCP curve represents the change in the open circuit potential of the negative electrode relative to the normalized discharge capacity.

[0077] The reference positive OCP curve is transformed into a positive OCP curve representing the change in open-circuit potential relative to discharge capacity according to the first parameter group; the reference negative OCP curve is transformed into a negative OCP curve representing the change in open-circuit potential relative to discharge capacity according to the second parameter group; and based on the difference between the positive and negative OCP curves, an OCV curve representing the change in open-circuit voltage of the secondary battery relative to capacity change is estimated; and

[0078] The first parameter set and the second parameter set are optimized so that the value of the error function, which represents the error between the estimated OCV curve and the time series data, is below a specified value.

[0079] The above description illustrates specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. A battery characteristic estimation device, wherein, The battery characteristic estimation device includes: The acquisition unit acquires time-series data of at least the voltage and current of a secondary battery, the secondary battery having a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material. The storage unit stores a reference positive electrode OCP curve and a reference negative electrode OCP curve. The reference positive electrode OCP curve represents the change in the open circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve represents the change in the open circuit potential of the negative electrode relative to the discharge capacity. The OCV curve estimation unit transforms the reference positive electrode OCP curve into a positive electrode OCP curve according to a first parameter set, and transforms the reference negative electrode OCP curve into a negative electrode OCP curve according to a second parameter set. Based on the difference between the positive electrode OCP curve and the negative electrode OCP curve, the OCV curve estimation unit estimates an OCV curve representing the change in open-circuit voltage of the secondary battery relative to capacity changes. The optimization unit optimizes the first parameter set and the second parameter set so that the value of the error function, which represents the error between the OCV curve estimated by the OCV curve estimation unit and the time series data, becomes below a predetermined value. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the positive electrode shift amount, which is the amount of shift from the reference positive electrode OCP curve to the positive electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the relative shift amount of the negative electrode OCP curve relative to the positive electrode OCP curve in the discharge capacity direction. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the relative shift of the positive electrode OCP curve relative to the negative electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the shift from the reference negative electrode OCP curve to the negative electrode OCP curve in the discharge capacity direction, i.e., the negative electrode shift.

2. The battery characteristic estimation device according to claim 1, wherein, The positive electrode active material is a substance containing at least one of NCM (nickel cobalt manganese), NCA (nickel cobalt aluminum), LFP (lithium ferrophosphate), and LMO (lithium manganese oxide). The negative electrode active material is a substance containing at least one of hard carbon and graphite. The optimization unit sets the negative electrode expansion / contraction rate based on a mapping representing the change in the negative electrode expansion / contraction rate relative to the cumulative value of the secondary battery's usage time or charge / discharge amount. The optimization unit optimizes the first parameter set and the second parameter set excluding the negative electrode scaling ratio, so that the value of the error function representing the error between the time series data and the error is below a predetermined value.

3. The battery characteristic estimation device according to claim 1 or 2, wherein, The battery characteristic estimation device also includes a data filtering unit. The data filtering unit extracts data from the time-series data that shows small voltage changes due to charging and discharging. The optimization unit performs optimization so that the error between the OCV curve and the data extracted by the data filtering unit is below the specified value.

4. The battery characteristic estimation device according to claim 3, wherein, The error function is a function that increases based on the following total value, which is the sum of the errors between the OCV curve and each data point extracted by the data filtering unit multiplied by a weight. The weight is set as follows: the number of data points extracted by the data filtering unit is counted for multiple intervals of discharge capacity or voltage value. The more data points are counted, the smaller the weight value corresponding to the interval.

5. A method for estimating the characteristics of a storage battery, wherein, The battery characteristic estimation method causes the computer to perform the following processing: Obtain time-series data of at least the voltage and current of a secondary battery, the secondary battery having a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material; The storage includes a reference positive electrode OCP curve and a reference negative electrode OCP curve. The reference positive electrode OCP curve represents the change in the open circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve represents the change in the open circuit potential of the negative electrode relative to the discharge capacity. The reference positive OCP curve is transformed into a positive OCP curve according to the first parameter group, and the reference negative OCP curve is transformed into a negative OCP curve according to the second parameter group. Based on the difference between the positive and negative OCP curves, an OCV curve representing the change in open-circuit voltage of the secondary battery relative to capacity change is estimated; and The first and second parameter sets are optimized so that the value of the error function, representing the error between the estimated OCV curve and the time series data, is below a predetermined value. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the positive electrode shift amount, which is the amount of shift from the reference positive electrode OCP curve to the positive electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the relative shift amount of the negative electrode OCP curve relative to the positive electrode OCP curve in the discharge capacity direction. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the relative shift of the positive electrode OCP curve relative to the negative electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the shift from the reference negative electrode OCP curve to the negative electrode OCP curve in the discharge capacity direction, i.e., the negative electrode shift.

6. A storage medium storing a program, wherein, The program causes the computer to perform the following processing: Obtain time-series data of at least the voltage and current of a secondary battery, the secondary battery having a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material; The storage includes a reference positive electrode OCP curve and a reference negative electrode OCP curve. The reference positive electrode OCP curve represents the change in the open circuit potential of the positive electrode relative to the discharge capacity, and the reference negative electrode OCP curve represents the change in the open circuit potential of the negative electrode relative to the discharge capacity. The reference positive OCP curve is transformed into a positive OCP curve according to the first parameter group, and the reference negative OCP curve is transformed into a negative OCP curve according to the second parameter group. Based on the difference between the positive and negative OCP curves, an OCV curve representing the change in open-circuit voltage of the secondary battery relative to capacity change is estimated; and The first and second parameter sets are optimized so that the value of the error function, representing the error between the estimated OCV curve and the time series data, is below a predetermined value. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the positive electrode shift amount, which is the amount of shift from the reference positive electrode OCP curve to the positive electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the relative shift amount of the negative electrode OCP curve relative to the positive electrode OCP curve in the discharge capacity direction. The first parameter set includes the positive electrode scaling factor, which transforms the width of the reference discharge capacity of the positive electrode to the width of the actual discharge capacity, and the relative shift of the positive electrode OCP curve relative to the negative electrode OCP curve in the discharge capacity direction. The second parameter set includes the negative electrode scaling factor, which transforms the width of the reference discharge capacity of the negative electrode to the width of the actual discharge capacity, and the shift from the reference negative electrode OCP curve to the negative electrode OCP curve in the discharge capacity direction, i.e., the negative electrode shift.

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