Estimation device

Abstract

An estimation device 1 comprises a current integration unit 11, an SOC estimation unit 12, and an SOH estimation unit 13. The current integration unit 11 uses current values I of a battery 5 measured at a plurality of time points tn during charging / discharging of the battery 5 to calculate an integrated current value ∆Q from measurement start t0 to each of the time points tn. The SOC estimation unit 12 uses the current values I and terminal voltage values V of the battery 5 measured at the plurality of time points tn to calculate an estimated SOC value at each of the time points tn of the measurement. The SOH estimation unit 13 uses three or more, preferably five or more, datasets including the integrated current value ∆Q and the estimated SOC value to estimate the SOH of the battery 5 on the basis of a regression line calculated by a least squares method.

Description

estimation device

[0001] The present invention relates to an estimation device.

[0002] Batteries are used to supply power to vehicle drive sources such as motors. Repeated charging and discharging of batteries gradually deteriorates them, resulting in a decrease in their full charge capacity. The State of Health (SOH) is used as a value indicating the battery's health. While the SOH is a necessary element for appropriate control of battery charging and discharging, it cannot be measured directly. Therefore, a method for estimating the SOH using measurable values ​​from the battery has been proposed (see, for example, Patent Document 1).

[0003] International Publication No. 2014 / 083856

[0004] One method for estimating SOH is a calculation method based on the change in SOC (State of Charge) between two points in time and the current integration value. However, since the change in SOC is calculated using the estimated SOC value, it may be affected by SOC estimation errors. If the SOH estimation accuracy is poor, it may be necessary to install a large battery to ensure a large safety margin in the battery charge / discharge control range, which may affect costs and the layout of on-board equipment. There is a need for an estimation device that can reduce the effect of SOC estimation errors and improve SOH estimation accuracy.

[0005] The present invention provides an estimation device for estimating a state of health of a battery, comprising: an integration value calculation unit that calculates an integrated current value from the start of measurement to each measurement point using current values ​​of the battery measured at multiple points during charging and discharging of the battery; an SOC estimation unit that calculates an estimated SOC value at each measurement point using the current values ​​and terminal voltage values ​​of the battery measured at the multiple points; and a state of health estimation unit that estimates the state of health based on a regression line calculated by the least squares method using three or more data sets including the integrated current values ​​and the estimated SOC values ​​at each measurement point.

[0006] According to the present invention, it is possible to reduce the influence of an error in estimating the SOC and improve the accuracy of estimating the SOH.

[0007] FIG. 1 is a block diagram showing a configuration of an estimation device according to an embodiment; FIG. 2 is a conceptual diagram illustrating the relationship between an integrated current value and an SOC, and an SOH; FIG. 3 is a diagram showing an example of calculation of an SOH; FIG. 4 is a flowchart showing a processing flow of the estimation device while a vehicle is traveling; FIG. 5 is a block diagram showing a configuration of an estimation device according to a first modification; FIG. 6 is a diagram showing an example of calculation of an SOH according to the first modification; FIG. 7 is a block diagram showing a configuration of an estimation device according to a second modification; and FIG. 8 is a flowchart showing a processing flow of the estimation device according to the second modification.

[0008] An estimation device according to an embodiment of the present invention will now be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of an estimation device 1 according to the embodiment. The estimation device 1 can be installed in a vehicle, such as an electric vehicle or a hybrid electric vehicle, that can be driven by power supplied from a battery 5. The battery 5 is a rechargeable secondary battery, and can be, for example, a lithium-ion battery, but other types of batteries 5 may also be used. The battery 5 supplies power to an electric motor that drives the vehicle by discharging, and is charged by regenerative energy from the electric motor when the vehicle is braked. The battery 5 can also be charged by an external charging facility, such as a quick charger or a household outlet.

[0009] As shown in FIG. 1 , a current sensor 6 and a voltage sensor 7 are connected to the battery 5. The current sensor 6 measures the current flowing through the battery 5. The voltage sensor 7 measures the terminal voltage of the battery 5. The current sensor 6 and the voltage sensor 7 measure the current and the terminal voltage at a predetermined sampling period while the vehicle is running (from when the ignition of the vehicle is turned on to when the ignition is turned off). The estimation device 1 is connected to the current sensor 6 and the voltage sensor 7 by wire or wirelessly. The estimation device 1 is connected to the current sensor 6 and the voltage sensor 7 at each time t n The current value I and the terminal voltage value V measured in are input in sequence.

[0010] The estimation device 1 estimates the state of health (SOH) of the battery 5 in real time using the current value I and terminal voltage value V of the battery 5 input while the vehicle is traveling. The SOH indicates the ratio (FCC / FCC0) of the full charge capacity FCC of the battery 5 when it is deteriorated to the full charge capacity of the battery 5 when it is new (hereinafter referred to as the "initial full charge capacity FCC0"), and serves as an index for determining the deterioration state of the battery 5. The estimation device 1 can be configured, for example, by an ECU (Electronic Control Unit) provided in the vehicle. Although not shown, the ECU includes a processor such as a CPU and memories such as a ROM and a RAM. The memory stores various programs executed by the estimation device 1, and the processor executes the programs to realize the functional configuration shown in FIG. 1.

[0011] Although detailed description will be omitted, the ECU may perform various processes using the SOH in addition to estimating the SOH. For example, the ECU may control charging and discharging of the battery 5 based on the SOH, or may calculate the vehicle's mileage from the SOH and control vehicle operation. Alternatively, the ECU may output the estimated SOH to an external device, and an external control device may perform processing using the SOH. Alternatively, the estimation device 1 may be a computer provided outside the vehicle. In this case, the estimation device 1 provided outside the vehicle may, for example, communicate with an ECU provided in the vehicle to acquire measurements from the current sensor 6 and the voltage sensor 7.

[0012] 1, the estimation device 1 includes, as functional components, a current integrator 11, an SOC estimator 12, and an SOH estimator 13 (state of health estimator). The estimation device 1 also includes a storage unit 14 configured from a memory. The storage unit 14 stores data required for processing by each functional component, and also temporarily stores the processing results of each functional component.

[0013] The current integrator 11 measures the current at multiple points t n The current integration unit 11 calculates the current integration value ΔQ using the current value I of the current sensor 6.n The current integration unit 11 calculates the current integration value ΔQ by integrating the current value I up to the new time point t n The calculation is performed each time the current value I is input, and the calculated current integrated value ΔQ is stored in the storage unit 14.

[0014] The SOC estimation unit 12 estimates the SOC (State of Charge), which is the charging rate of the battery 5, while the vehicle is running and the battery 5 is being charged and discharged. The SOC estimation unit 12 can estimate the SOC by a known estimation method using the current value I measured by the current sensor 6 and the terminal voltage value V measured by the voltage sensor 7. One known estimation method is, for example, an estimation method using the overvoltage η of the battery 5. The SOC estimation unit 12 estimates the overvoltage η of the battery 5 by an estimation method using an equivalent circuit model such as a Kalman filter, using the current value I and the terminal voltage value V. The SOC estimation unit 12 calculates the open circuit voltage (OCV) of the battery by subtracting the overvoltage η from the terminal voltage value V. The SOC estimation unit 12 obtains an estimated SOC value by converting the open circuit voltage to SOC based on known SOC-OCV characteristics. The SOC estimation unit 12 obtains an estimated SOC value from the current sensor 6 and the voltage sensor 7 at a new time t n The estimation is performed each time a measured value of the SOC is input, and the SOC estimated value, which is the estimation result, is temporarily stored in the storage unit 14.

[0015] As shown in FIG. 1, the storage unit 14 stores the current integration value ΔQ calculated by the current integration unit 11 and the SOC estimation value estimated by the SOC estimation unit 12 at the measurement time t n The SOH estimation unit 13 stores the data as a single data set DS linked by the multiple time points t n The SOH estimation unit 13 estimates the SOH using the data set DS of three or more, preferably five or more, points in time t nThe SOH estimation unit 13 estimates the SOH using the data set DS in the data set DS. That is, when at least three, and preferably five or more, data sets DS are accumulated in the storage unit 14, the SOH estimation unit 13 acquires the accumulated data set DS and starts estimating the SOH. After estimating the initial SOH, the SOH estimation unit 13 estimates the SOH each time a new data set DS is stored in the storage unit 14. Specifically, the SOH estimation unit 13 calculates a regression line by the least squares method using the current integrated value ΔQ and the SOC estimated value of the data set DS. The SOH estimation unit 13 calculates the SOH estimated value by dividing the slope a of the regression line by the initial full charge capacity FCC0 of the battery 5.

[0016] FIG. 2 illustrates the relationship between the current integrated value ΔQ, the SOC, and the SOH. FIG. 2 shows a state in which the same amount of current flows from time t0 to time t6 in both a new battery 5 and a deteriorated battery 5. (a) of FIG. 2 is a graph showing the change in the current integrated value ΔQ, and (b) of FIG. 2 is a graph showing the change in the SOC. (c) of FIG. 2 is a distribution diagram in which the current integrated value ΔQ from time t0 to time t6 is plotted on the y-axis (vertical axis) and the SOC is plotted on the x-axis (horizontal axis). Note that in FIG. 2, the data for the new battery 5 is shown with a dashed line, and the data for the deteriorated battery 5 is shown with a solid line. The SOC in FIG. 2 indicates a true value with no estimation error.

[0017] As shown in Figures 2(a) and 2(b), the SOC of battery 5 increases as the current integrated value ΔQ from time t0 to time t6 increases. Here, as battery 5 deteriorates, the full charge capacity FCC gradually decreases from the initial full charge capacity FCC0. Therefore, when the same amount of current flows, the SOC increases more quickly when battery 5 is deteriorated (solid line in Figure 2(b)) than when it is new (dashed line in Figure 2(a)). In other words, when battery 5 is deteriorated, the change in SOC within the same time period is greater than when it is new. That is, the change in SOC of battery 5 corresponds to the SOH (FCC / FCC0) of battery 5.

[0018] 2C shows a straight line connecting the plotted points of the current integrated value ΔQ and SOC when the battery 5 is new and when it is deteriorated. As shown in FIG. 2C, the slope a of the line differs when the battery 5 is new (dashed line) and when it is deteriorated (solid line). The slope a of the line depends on the SOH (FCC / FCC0) of the battery 5. In other words, the SOH can be estimated by determining the slope a of the line plotting the current integrated value ΔQ and SOC.

[0019] In this embodiment, the SOH is estimated using a data set DS of three or more points, preferably five or more points. However, there is a conventional method of calculating the SOH using a data set DS of two points (t0, t1) according to the following equations (1) and (2). Since SOC(t0) and SOC(t1) are calculated using estimated values, there is an error with respect to the true value. In this conventional method, since only two SOC estimates are used, the error per one point can have a significant impact on the accuracy of the SOH estimation.

[0020] On the other hand, in this embodiment, the SOH estimator 13 estimates the SOH using a data set DS of multiple points (three or more points, preferably five or more points). It is assumed that the error of the estimated SOC value relative to the true value varies randomly between positive and negative. In this case, by using a data set DS of multiple points, the estimation errors of each point are canceled out as a whole, thereby reducing the impact on the accuracy of the SOH estimation.

[0021] Specifically, the SOH estimator 13 can estimate the SOH with reduced estimation error by calculating a regression line by the least squares method using the current integrated value ΔQ and the SOC estimated value of the data set DS. The regression line calculated by the least squares method approximates the line shown in FIG. 2C.

[0022] The regression line obtained by simple regression analysis using two variables x (estimated SOC value) and y (integrated current value ΔQ) is expressed by the following equation (3): y=ax+b (equation (3))

[0023] When the equation (3) is expressed as a matrix, the following equations (4) to (7) are obtained. A set of coefficients A[a, b] is calculated by the following equation (8): A=inv(Phi^T×Phi)×Phi^T×Y (8) where inv denotes an inverse matrix, and ^T denotes a transpose.

[0024] 3 is a diagram showing an example of SOH calculation. As shown in FIG. 3, the SOH estimator 13 creates a matrix Phi using the estimated SOC values ​​of the data set DS. The SOH estimator 13 creates an n-row, 2-column (n = the number of data sets DS) matrix Phi, with the estimated SOC values ​​entered in the first column and 1 entered in the second column. The SOH estimator 13 also creates Y using the current integrated value ΔQ of the data set DS. The SOH estimator 13 calculates a set of coefficients A[a, b] by performing the matrix calculation of equation (8) using the created matrices Phi and Y. The SOH estimator 13 acquires the first element of the coefficient A as the slope a of the regression line.

[0025] The slope a of the regression line is expressed by the following equation (9): a=FCC0×SOH (9) The SOH estimation unit 13 can calculate the estimated SOH value of the battery 5 by dividing the acquired slope a by the initial full charge capacity FCC0.

[0026] The SOH estimating unit 13 temporarily stores the SOH estimated value calculated while the vehicle is traveling in the storage unit 14. When the vehicle stops traveling, the SOH estimating unit 13 stores the last estimated SOH value (SOH last ) stored in the storage unit 14 as the estimated SOH value at the end of the current trip. The SOH estimator 13 can perform weighted averaging as an example of the smoothing process. The SOH estimator 13 smooths the estimated SOH value (SOH pre ) and weighted average processing can be performed using the following equation (10): SOH estimated value at the end of the current trip = SOH last ×W+SOH pre ×(1−W) Equation (10) W is a weighting factor and is a positive number less than 1.

[0027] 4 is a flowchart showing the flow of processing by the estimation device 1 while the vehicle is running. As described above, when the vehicle starts running, the current sensor 6 and the voltage sensor 7 measure the current and terminal voltage of the battery 5 at a predetermined sampling period. As shown in FIG. 4, while the vehicle is running, the estimation device 1 receives the current and terminal voltage signals from the current sensor 6 and the voltage sensor 7 at each measurement time t n The measured values ​​(current value I, terminal voltage value V) measured at the measurement time t are input (step S01: Yes). When the measured values ​​are input, the current integrator 11 calculates the current integrated value ΔQ, and the SOC estimator 12 estimates the SOC (step S02). The current integrated value ΔQ and the estimated SOC are calculated at the measurement time t n The SOH estimation unit 13 temporarily stores the SOH estimated value in the storage unit 14 as a data set DS linked by the current value I and the terminal voltage value V. When the number of data sets DS stored in the storage unit 14 reaches three or more (step S03: Yes), the SOH estimation unit 13 acquires the data set DS and starts estimating the SOH (step S04). The SOH estimation unit 13 temporarily stores the SOH estimated value in the storage unit 14. If the vehicle has not finished traveling (step S05: No), the SOH estimation unit 13 returns to step S01, and when new measured values ​​(current value I, terminal voltage value V) are input, performs the processes of steps S02 to S04. When the vehicle has finished traveling (step S05: Yes), the SOH estimation unit 13 acquires the last estimated SOH value SOH last (step S06), and stores the result as an estimated SOH value at the end of the current trip in the storage unit 14. Note that in step S03, the SOH estimation unit 13 may start estimating the SOH when the number of data sets DS stored in the storage unit 14 reaches five or more.

[0028] As described above, the estimation device 1 according to the embodiment includes, for example, the following configuration: (1) The estimation device 1 estimates the SOH (state of health) of the battery 5. The estimation device 1 includes a current integrator 11, an SOC estimator 12, and an SOH estimator 13 (state of health estimator). The current integrator 11 estimates the SOH (state of health) of the battery 5 at multiple times t n Using the current value I of the battery 5 measured at time t0, nThe SOC estimation unit 12 calculates the current integrated value ΔQ up to a plurality of times t n Using the current value I and terminal voltage value V of the battery 5 measured at each measurement time t n The SOH estimation unit 13 estimates the SOH of the battery 5 based on a regression line calculated by the least squares method using three or more, preferably five or more, data sets DS including the current integrated value ΔQ and the SOC estimate value. Specifically, the SOH estimation unit 13 can calculate the SOH estimate value by dividing the slope a of the regression line calculated using the data set DS by the initial full charge capacity FCC0 of the battery 5.

[0029] Conventionally, there is a method for calculating the SOH of a battery 5 based on the change in SOC at two points in time (t0, t1) and the current integrated value ΔQ. However, since the SOC is an estimated value, estimation errors occur. When only two SOC estimates are used, the estimation error per point can have a significant impact on the accuracy of the SOH estimation. If the SOH estimation accuracy is poor, it becomes necessary to provide a large battery 5 in order to ensure a large safety margin in the control range of charging and discharging the battery 5. This can affect the installation cost of the battery 5 and the layout of the on-board equipment including the battery 5.

[0030] In this embodiment, the SOH estimation unit 13 estimates the SOH by regression analysis using three or more (preferably five or more) data sets DS (current integrated value ΔQ and SOC estimated value). As shown in FIG. 2B, since it is assumed that the error of the SOC estimated value with respect to the true value changes randomly between positive and negative, the SOH is estimated by using a large number of measurement points t n By using the data set DS, the estimation error of the SOC at each point is canceled out. This allows the estimation device 1 to reduce the influence of the estimation error of the SOC and improve the estimation accuracy of the SOH.

[0031] (4) The battery 5 supplies power for driving the vehicle. The SOH estimation unit 13 starts estimating the SOH when at least three, preferably five, data sets DS are acquired after the vehicle starts traveling, and thereafter estimates the SOH each time a new data set DS is acquired.

[0032] As described above, the estimation device 1 of this embodiment improves the accuracy of SOH estimation by using a large number of data sets DS. Therefore, the SOH estimation unit 13 can ensure the accuracy of SOH estimation by acquiring at least three, and preferably five, data sets DS before starting SOH estimation. Furthermore, as time passes since the vehicle starts traveling, a larger number of data sets become available, allowing the estimation device 1 to perform SOH estimation with higher accuracy.

[0033] (5) The estimation device 1 includes a storage unit 14 that stores an estimated SOH value for each vehicle run of the battery 5. The SOH estimation unit 13 stores the last estimated SOH value (SOH last ) is calculated by multiplying the SOH estimated value (SOH pre ) and stores the smoothed value in the storage unit 14.

[0034] By storing the smoothed SOH estimated value in the storage unit 14, the rationality of the SOH statistical data can be improved. In addition, when the vehicle travels next time, the SOH estimated value at the end of the previous travel (SOH pre ) can be utilized for controlling the charging and discharging of the battery 5 and for controlling the driving of the vehicle.

[0035] [Modification 1] Fig. 5 is a block diagram showing the configuration of an estimation device 1A according to Modification 1. As shown in Fig. 5, the estimation device 1A according to Modification 1 includes a timer 15 in addition to the functional configuration described in the embodiment. The timer 15 measures the time from the measurement start time t0 of the current sensor 6 and the voltage sensor 7 to each measurement time t n The timer 15 measures the elapsed time T from the measurement time t n That is, in the first modification, the measurement time t nThe SOH estimation unit 13 stores a data set DS configured from the current integrated value ΔQ, the SOC estimated value, and the elapsed time T linked to the SOH. The SOH estimation unit 13 estimates the SOH using the data set DS configured from these three elements. As in the embodiment, the SOH estimation unit 13 can perform estimation using three or more data sets DS, preferably five or more data sets DS.

[0036] In the first modification, the measurement error I err In order to take into account the above, the elapsed time T is added to the elements of the SOH estimation. As shown in FIG. 5, the current value I measured by the current sensor 6 is the true value of the current flowing through the battery 5 with a measurement error I err The current integrated value ΔQ is the sum of the current values ​​I measured by the current sensor 6. Therefore, as shown in the following equation (12), from the measurement start time t0 to the measurement time t n The larger the value of the elapsed time T until err The influence of err × T) becomes larger. Then, as shown in the following equation (11), the measurement error I err The influence of also affects the accuracy of SOH estimation.

[0037] Therefore, by adding the elapsed time T to the elements of the SOH estimation, the measurement error I err This reduces the influence of the above and improves the accuracy of estimating the SOH. Specifically, the SOH estimation unit 13 performs multiple regression analysis using a regression model in which the estimated SOC value, the elapsed time T, and the integrated current value ΔQ are variables x1, x2, and y, respectively. The multiple regression model is expressed by the following equation (13).

[0038] As in the embodiment, the SOH estimator 13 performs a matrix operation using the data set DS to determine coefficient 1, coefficient 2, and the intercept in the above equation (13). Coefficient 1 corresponds to the slope of the regression line. The SOH estimator 13 can calculate the estimated SOH value by dividing coefficient 1 by the initial full charge capacity FCC0 of the battery 5.

[0039] FIG. 6 is a diagram showing an example of SOH calculation according to Modification 1. FIG. 6 shows the data set DS of FIG. 3 to which elapsed time T has been added. As shown in FIG. 6 , in Modification 1, the SOH estimator 13 creates a matrix Phi using the estimated SOC values ​​and elapsed time T of the data set DS. The SOH estimator 13 creates an n-row, 3-column (n = the number of data sets DS) matrix Phi, with the estimated SOC values ​​entered in the first column, the elapsed time T entered in the second column, and 1 entered in the third column. The SOH estimator 13 also creates Y using the current integrated value ΔQ of the data set DS. As in the embodiment, the SOH estimator 13 can calculate coefficient 1, coefficient 2, and the intercept in equation (13) by performing the matrix calculation of equation (8) using the created matrices Phi and Y.

[0040] As described above, the estimation device 1A according to the first modification includes, for example, the following configuration: (2) The estimation device 1A performs the following measurement from the measurement start time t0 to each time point t n The SOH estimation unit 13 includes a timer 15 for measuring the elapsed time T from the time t n The SOH is estimated based on a regression line calculated by the least squares method using a data set DS consisting of the current integrated value ΔQ, the SOC estimated value, and the elapsed time T in the above equation.

[0041] The current integrated value ΔQ is calculated from the measurement value (current value I) of the current sensor 6 provided in the battery 5, so the measurement error I err affects the accuracy of the SOH estimation. err The influence of the current integrated value ΔQ on the measurement start time t0 to the measurement time t n Since there is a correlation between the elapsed time T and the SOH estimation, the measurement error I err The influence of the above can be reduced, thereby improving the accuracy of SOH estimation.

[0042] [Modification 2] Fig. 7 is a block diagram showing the configuration of an estimation device 1B according to Modification 2. As described in the embodiment, the SOC estimation unit 12 (overvoltage estimation unit) estimates the overvoltage η of the battery 5 in the process of estimating the SOC. As shown in Fig. 7, in Modification 2, the SOC estimation unit 12 estimates the estimated overvoltage η at a measurement time tn The overvoltage η is stored in the storage unit 14 in association with the data set DS configured from elements for estimating the SOH.

[0043] The SOH estimator 13 acquires the data set DS stored in the memory 14 and estimates the SOH, as in the embodiment. However, in the second modification, the SOH estimator 13 estimates the SOH by acquiring the data set DS stored in the memory 14 at the measurement time t n The data set DS is excluded from the estimation. The SOC estimation unit 12 estimates the overvoltage η to calculate the estimated SOC value, but if the absolute value of the overvoltage η is large, an estimation error in the overvoltage η is likely to occur. As a result, an estimation error is likely to occur in the SOC as well. Therefore, it is possible to determine whether the data set DS is suitable for use in SOH estimation based on the magnitude of the overvoltage η of the battery 5.

[0044] The SOH estimation unit 13 estimates the SOH at each measurement time t n Before performing SOH estimation using the data set DS, the overvoltage η is compared with a threshold value TH. The threshold value TH is a reference value for determining the magnitude of the overvoltage η, and can be appropriately set by testing, simulation, or the like. The SOH estimation unit 13 compares the overvoltage η with a threshold value TH at a certain measurement time t n If the overvoltage η exceeds the threshold TH, the measurement time t n This allows the SOH estimator 13 to perform SOH estimation while excluding data that may cause a decrease in the accuracy of the SOH estimation.

[0045] Specifically, the SOH estimation unit 13 estimates the overvoltage η at the measurement time t n In the subsequent SOH estimations, the SOH is not estimated at the measurement time t n The SOH estimation unit 13 may delete the data set DS excluded from the estimation target from the storage unit 14, or may assign a flag to the data set DS indicating that it has been excluded from the estimation target.

[0046] 8 is a flowchart showing the flow of processing by the estimation device 1B according to the modified example 2. As shown in FIG. 8, while the vehicle is traveling, the estimation device 1B receives t n The measured values ​​(current value I, terminal voltage value V) are input (step S11: Yes). When the measured values ​​are input, the current integrator 11 calculates a current integrated value ΔQ, and the SOC estimator 12 estimates the overvoltage η and estimates the SOC using the overvoltage η (step S12). The current integrated value ΔQ and the estimated SOC are calculated at the measurement time t n The overvoltage η is stored in the storage unit 14 as a data set DS linked by the measurement time t n The SOH estimator 13 compares the overvoltage η with the threshold value TH (step S13). If the overvoltage η exceeds the threshold value TH (step S13: Yes), the SOH estimator 13 estimates the overvoltage η at the measurement time t n The SOH estimator 13 then excludes this data set DS from the estimation target (step S14), and returns to step S11. If the overvoltage η is equal to or less than the threshold value TH (step S13: No), the SOH estimator 13 proceeds to step S15. As in the embodiment, if the number of data sets DS that have not been excluded from the estimation target reaches three or more (step S15: Yes), the SOH estimator 13 estimates the SOH (step S16). Alternatively, the SOH estimator 13 may estimate the SOH if the number of data sets DS that have not been excluded from the estimation target reaches five or more. The processing in steps S17 and S18 is the same as steps S05 and S06 in the embodiment (see FIG. 4), and therefore description thereof will be omitted.

[0047] As described above, the estimation device 1B according to the second modification has, for example, the following configuration: (4) The battery 5 supplies electric power for driving the vehicle. The estimation device 1B measures the electric power at each measurement time t n The SOC estimation unit 12 (overvoltage estimation unit) estimates the overvoltage η of the battery 5 using the terminal voltage value V of the battery 5 measured at time t n The SOH is estimated by excluding the data set DS.

[0048] In a state where the overvoltage η of the battery 5 is large, it becomes difficult to perform highly accurate SOH estimation. n The overvoltage η is stored in the storage unit 14. The SOH estimation unit 13 compares the overvoltage η with the threshold value TH, and if it exceeds the threshold value TH, the SOH estimation unit 13 estimates the overvoltage η at the measurement time t n The data set DS is excluded from the SOH estimation target. The SOH estimation unit 13 can maintain the accuracy of the SOH estimation by estimating the SOH while excluding data that may cause a decrease in the accuracy of the SOH estimation.

[0049] The aspect in which the SOH estimator 13 of Modification 2 compares the overvoltage η with the threshold value TH may be applied to Modification 1 (see FIG. 5 ). In the embodiment and modifications, the SOH estimator 13 performs SOH estimation using the data set DS covering the entire period from the measurement start time t0 of the current sensor 6 and the voltage sensor 7 while the vehicle is traveling. However, this is not limiting. For example, the SOH estimator 13 may divide the elapsed time from the measurement start time t0 into regular intervals and estimate the SOH based on the data set DS acquired at each regular interval. When the sampling period of the current sensor 6 and the voltage sensor 7 is short or when the vehicle is traveling for a long time, the number of data sets DS may increase. However, by having the SOH estimator 13 divide the data into regular intervals and perform SOH estimation, the computational load can be reduced even in such cases. Furthermore, the data set DS for which SOH estimation has been completed can be deleted from the storage unit 14, thereby reducing the load on the data capacity of the storage unit 14.

[0050] 1, 1A, 1B: Estimation device, 5: Battery, 6: Current sensor, 7: Voltage sensor, 11: Current integration unit, 12: SOC estimation unit (overvoltage estimation unit), 13: SOH estimation unit (state of health estimation unit), 14: Storage unit, 15: Timer unit

Claims

1. An estimation device for estimating the state of health of a battery, comprising: a current integration unit that calculates an accumulated current value from the start of measurement to each measurement point using current values ​​of the battery measured at multiple points during charging and discharging of the battery; an SOC estimation unit that calculates an estimated SOC value at each measurement point using the current values ​​and terminal voltage values ​​of the battery measured at the multiple points; and a state of health estimation unit that estimates the state of health based on a regression line calculated by the least squares method using three or more data sets including the accumulated current values ​​and the estimated SOC values ​​at each measurement point.

2. An estimation device according to claim 1, further comprising a timing unit that measures the elapsed time from the start of the measurement to each of the measurement points, and wherein the health state estimation unit estimates the health state based on a regression line calculated by the least squares method using a data set including the current integration value, the SOC estimation value, and the elapsed time at each of the measurement points.

3. An estimation device according to claim 1 or 2, wherein the battery supplies power for driving a vehicle, and the estimation device comprises an overvoltage estimation unit that estimates an overvoltage of the battery using the current value and terminal voltage value of the battery measured at each measurement point during charging and discharging of the battery, and the health estimation unit estimates the health by excluding the data set at the measurement point at which the overvoltage exceeded a threshold.

4. An estimation device according to claim 1 or 2, wherein the battery supplies power for driving the vehicle, and the health state estimation unit starts estimating the health state when at least three sets of data have been acquired after the vehicle has started traveling, and thereafter estimates the health state each time a new set of data is acquired.

5. An estimation device according to claim 4, further comprising a memory unit that stores an estimated value of the battery's health for each run of the vehicle, wherein the health estimation unit smooths the last estimated value of the battery's health at the end of the run of the vehicle using the estimated value of the battery's health stored in the memory unit during the previous run, and stores the smoothed value in the memory unit.

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