Battery State Estimation Method and Battery State Estimation Device

Abstract

To estimate the SOC with high accuracy even if a battery is deteriorated.SOLUTION: Provided is a method for estimating the state of a battery by using a device having a Kalman filter arithmetic unit, a battery equivalent circuit model recording unit, and a system noise covariance calculation unit, and the method includes: a step of, by using measured values from a voltage sensor, a current sensor, and a temperature sensor installed in the battery, and a battery equivalent circuit model recorded in the battery equivalent circuit model recording unit, sequentially estimating, by the Kalman filter arithmetic unit, the charge rate and the capacity deterioration rate of the battery, the current offset of the current sensor, and the internal resistance of the battery set in the battery equivalent circuit model; and a step of changing, by the system noise covariance calculation unit, a system noise covariance value that is a set value used in the Kalman filter arithmetic unit according to deterioration of the battery.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a battery state estimation method and a battery state estimation device.

Background Art

[0002] Generally, the state of charge (SOC) of a battery (cell) is estimated using the charge and discharge current, terminal voltage, and surface temperature of the cell.

[0003] For example, since there is a close relationship between the cell terminal voltage (open circuit voltage (OCV)) when not charging or discharging and the SOC, there is a method of measuring the OCV and converting it to the SOC. There is also a method of obtaining the OCV from the cell terminal voltage (closed circuit voltage (CCV)) during charge and discharge using a cell equivalent circuit model using OCV, resistance, and polarization (parallel connection of resistance and capacitor), and converting it to the SOC. As another method, there is a method of measuring or calculating the OCV, obtaining the initial SOC, and then integrating the charge and discharge current to calculate the SOC.

[0004] In these SOC estimations, if the sensing errors of the current and voltage are large, the error of the SOC becomes large.

[0005] Patent Document 1 discloses an apparatus that detects the charge and discharge current and terminal voltage of a secondary battery, estimates a current offset, corrects the charge and discharge current detected by the current offset, and estimates the charge state of the secondary battery. The apparatus estimates the terminal voltage of the secondary battery using a predetermined measurement model, calculates a first correction value of the estimated current offset using the error between the estimated terminal voltage and the detected terminal voltage, and corrects the estimated current offset using the first correction value. Patent Document 1 also discloses that the predetermined measurement model is based on an equivalent circuit model of the polarization voltage of the secondary battery and uses an extended Kalman filter.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

[0007] In the state-of-charge estimation device for a secondary battery described in Patent Document 1, the case where the battery deteriorates is not considered.

[0008] Normally, when the battery deteriorates, the capacity decreases and the internal resistance of the battery increases, so the values of the resistance of the cell equivalent circuit, polarization, etc. also fluctuate. For this reason, with the conventional method, the estimation accuracy of the SOC becomes low. Therefore, there is room for improvement from the viewpoint of accuracy in estimating the SOC considering the deterioration of the battery during charge and discharge.

[0009] An object of the present invention is When the current is different during charging and discharging of the battery also to estimate the SOC with high accuracy. MEANS FOR SOLVING THE PROBLEMS

[0010] The battery state estimation method of the present invention is a method for estimating the state of a battery using a device having a Kalman filter calculation unit, a battery equivalent circuit model recording unit, and a system noise covariance calculation unit. The Kalman filter calculation unit uses the measured values of a voltage sensor, a current sensor, and a temperature sensor installed in the battery, and the battery equivalent circuit model recorded in the battery equivalent circuit model recording unit, and sequentially estimates the charge rate and capacity deterioration rate of the battery, the current offset of the current sensor, and the internal resistance of the battery set in the battery equivalent circuit model. And a step in which the system noise covariance calculation unit changes the system noise covariance value, which is a set value used by the Kalman filter calculation unit, depending on whether it is used for estimation during charging of the battery or for estimation during discharging of the battery. The system noise covariance value includes the system noise covariance values of the current offset and the internal resistance, and the system noise covariance values of the current offset and the internal resistance are smaller during discharging At times than during charging. EFFECTS OF THE INVENTION

[0011] According to the present invention, When the current is different during charging and discharging of the battery even in [a certain situation], the SOC can be estimated with high accuracy.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0014] (Embodiment 1) FIG. 1 is a configuration diagram showing the battery state estimation device of Embodiment 1.

[0015] In this figure, the battery state estimation device 500 is composed of a Kalman filter calculation unit 510, a battery equivalent circuit model recording unit 520, a system noise covariance calculation unit 530, and a battery deterioration detection unit 540. The Kalman filter calculation unit 510 includes a state estimation unit 552 and an error variance calculation unit 554.

[0016] The Kalman filter calculation unit 510 receives measurement values from a voltage sensor 200 that measures the terminal voltage CCV (closed-circuit voltage) of the battery 100 (cell), a current sensor 300 that measures the charge and discharge current I of the battery 100, and a temperature sensor 400 that measures the temperature T of the battery 100. From these inputs, the state estimation unit 552 sequentially estimates the SOC (state of charge), SOH (state of health), the current error offset value Io of the current sensor 300, and the respective gains of the resistance R0 in the battery equivalent circuit model. Also, the error variance calculation unit 554 estimates the error variance when estimating the gains. Note that the battery equivalent circuit model is also referred to as the "cell equivalent circuit model".

[0017] The Kalman filter calculation unit 510 calculates and outputs the SOC and SOH of the battery 100 using the above estimated values.

[0018] FIG. 2 shows the configuration of the state estimation unit 552 of the Kalman filter calculation unit 510 in FIG. 1.

[0019] As shown in FIG. 2, the state estimation unit includes an SOC estimation unit 511, an SOH estimation unit 512, an Io estimation unit 513, a RoG estimation unit 514, a Q estimation unit 515, a CCV estimation unit 516, a Kalman gain estimation unit 521 for Q, a Kalman gain estimation unit 522 for SOC, a Kalman gain estimation unit 523 for Io, and a Kalman gain estimation unit 524 for RoG. Here, Q is the full charge capacity of the cell. RoG is the internal resistance in the equivalent circuit model of the cell.

[0020] I, T, and V are input to the state estimation unit from each sensor. Then, SOC, SOH, Io, and RoG estimated using I, T, and V are output. When making these estimations, predetermined Kalman gains are estimated and used.

[0021] SOC, SOH, Io, and RoG are sequentially calculated and obtained using the following equations (1) to (4).

[0022]

Equation

[0023] [Number]

[0024] [Number]

[0025] [Number]

[0026] Here, Qini is the capacity of the cell when it is new. The unit is Ah. Also, Gsoc is the Kalman gain of SOC, Gq is the Kalman gain of Q, Gio is the Kalman gain of Io, and Grog is the Kalman gain of RoG. These Kalman gains are sequentially calculated by the Kalman filter calculation unit 510 (Fig. 1). Also, t is the sensing period of voltage, current, temperature or the calculation period of SOC, SOH, Io, RoG. For example, if it is every 1 second, t = 1.

[0027] Fig. 3 shows the battery equivalent circuit model used when calculating CCV.

[0028] In the battery equivalent circuit model recording unit 520 of Fig. 1, maps of OCV, the values of resistances R0, R1, R2 constituting the battery equivalent circuit, and the time constants τ1, τ2 of the capacitors are recorded in advance as parameters of the battery equivalent circuit for each SOC and T. Then, battery parameters are estimated from the SOC, T, and RoG input to the CCV estimation unit 516, and CCV is calculated from the input current (I) and current offset (Io).

[0029] The difference ΔCCV between the calculated CCV and the voltage (V) measured by the voltage sensor at the battery terminals is obtained, and the values multiplied by the Kalman gains (Gsoc, Gq, Gio, Grog) using the above equations (1) to (4) are added to SOC, Q, Io, and RoG respectively to update SOC, Q, Io, and RoG.

[0030] The calculation formulas for the Kalman gain G calculated by the Kalman filter calculation unit 510 (Fig. 1) are as shown in the following equations (5) and (6).

[0031]

Number

[0032]

Number

[0033] In the formula, P^(k) is the prior error covariance, P(k) is the posterior error covariance, A and b are matrices indicating the state, C is a matrix indicating the observed value, and σ w 2 is the observation noise covariance, and σ v 2 is the system noise covariance. Note that prior and posterior mean before (before) and after (after) using the "latest data", respectively.

[0034] The Kalman gain for each state is sequentially calculated together with the error covariance. Also, the system noise covariance is calculated by the system noise covariance calculation unit 530 in Fig. 1 for each state estimation value of SOC, Q, Io, and RoG. The observation noise covariance is the covariance value when measuring the battery terminal voltage with the voltage sensor 200 (Fig. 1), and is a set value used in the Kalman filter calculation unit 510.

[0035] In this way, the Kalman filter calculation unit 510 sequentially estimates SOC, SOH, Io, and RoG.

[0036] However, as the battery deteriorates, the parameters in the battery equivalent circuit model of FIG. 3 vary, resulting in a decrease in the estimation accuracy of SOC and SOH.

[0037] To prevent this, the deterioration of the battery is detected, and the parameters of the calculation formula are set so as to change the system noise covariance value of the system noise covariance calculation unit 530 in FIG. 1. In other words, the system noise covariance calculation unit 530 changes the system noise covariance value, which is the set value used in the Kalman filter operation unit 510, according to the deterioration of the battery.

[0038] From the above formulas (5) and (6), it can be seen that if the system noise covariance value changes, the Kalman gain G changes.

[0039] Specifically, in the battery deterioration detection unit 540, the SOH estimated by the Kalman filter operation unit 510 is used. SOH is usually 100% for a new battery, and the value decreases to 90%, 80% as it deteriorates. Therefore, the system noise covariance value is changed when a predetermined SOH is reached. Regarding the system noise covariance value, in particular, by increasing the system noise covariance values of Io and RoG that affect the battery equivalent circuit model, the Kalman gain becomes large. Using the Kalman gain thus obtained, the parameters of the battery equivalent circuit model are corrected (changed). As a result, even when the battery deteriorates, it is possible to accurately estimate SOC and SOH.

[0040] In summary, the deterioration of the battery 100 is represented by a decrease in the capacity (or SOH) of the battery 100, an increase in the actual internal resistance of the battery 100, or a value corresponding to the actual usage time of the battery 100. When any of these values changes by a predetermined amount with respect to its initial value, the system noise covariance calculation unit 530 increases the system noise covariance value.

[0041] Note that the frequency of changing the system noise covariance value may be changed only once within a predetermined period. However, from the viewpoint of improving the accuracy of the estimated value, the system covariance value may be changed stepwise a plurality of times as the SOH decreases. In the present embodiment, an example of detecting battery deterioration using the SOH estimated by the Kalman filter calculation unit 510 is shown, but the present invention is not limited thereto, and battery deterioration may be detected by other methods. For example, the fluctuation of the charge / discharge current and the fluctuation of the voltage may be measured, the internal resistance of the battery may be calculated, and the deterioration of the battery may be detected by the increase of the internal resistance. Further, the actual usage time of the battery may be counted, and the deterioration of the battery may be detected by the usage time. Here, the usage time means the discharge time or the charge time of the battery, or the sum of the discharge time and the charge time.

[0042] In summary, the deterioration of the battery is represented by a decrease in the capacity of the battery, an increase in the actual internal resistance of the battery, or a value corresponding to the actual usage time of the battery. When any of these values changes by a predetermined amount with respect to its initial value, the system noise covariance calculation unit 530 increases the system noise covariance value.

[0043] FIG. 4 is a flowchart showing the battery state estimation method of the present embodiment.

[0044] In this figure, the Kalman filter calculation unit receives the measured values of the voltage sensor, current sensor, and temperature sensor installed in the battery (step S11). Then, the Kalman filter calculation unit sequentially estimates the SOC and SOH of the battery, Io of the current sensor, and RoG of the battery set in the battery equivalent circuit model using these measured values and the battery equivalent circuit model recorded in the battery equivalent circuit model recording unit (step S12).

[0045] The system noise covariance calculation unit changes the system noise covariance value, which is a set value used by the Kalman filter calculation unit, according to the deterioration of the battery (step S13).

[0046] (Embodiment 2) In Embodiment 1, the condition for changing the system noise covariance value is based on the deterioration of the battery. However, in Embodiment 2, the system noise covariance value is switched based on the charging and discharging of the battery.

[0047] FIG. 5 is a configuration diagram showing the battery state estimation device of the present embodiment.

[0048] In this figure, the battery state estimation device 500 does not have the battery deterioration detection unit 540 of FIG. 1. Instead, the system noise covariance calculation unit 530 is configured to change the system noise covariance value depending on whether it is used for estimation during charging of the battery 100 or during discharge. In other words, the system noise covariance calculation unit 530 is configured to set different system noise covariance values depending on whether it is used for estimation during charging of the battery 100 or during discharge.

[0049] In home appliances, electric vehicles, etc. driven by a battery, usually the discharge current is larger than the charging current. Therefore, from the above formula (1), the influence of the current offset Io becomes smaller during discharge. Also, the SOC error becomes smaller when the Kalman gain is made smaller.

[0050] Specifically, during discharge, the system noise covariance values of Io and RoG are made smaller compared to during charging. Thereby, it becomes possible to estimate SOC and SOH with high accuracy.

Description of Signs

[0051] 100: Battery, 200: Voltage sensor, 300: Current sensor, 400: Temperature sensor, 500: Battery state estimation device, 510: Kalman filter calculation unit, 511: SOC estimation unit, 512: SOH estimation unit, 513: Io estimation unit, 514: RoG estimation unit, 515: Q estimation unit, 516: CCV estimation unit, 520: Battery equivalent circuit model recording unit, 521: Kalman gain estimation unit for Q, 522: Kalman gain estimation unit for SOC, 523: Kalman gain estimation unit for Io, 524: Kalman gain estimation unit for RoG, 530: System noise covariance calculation unit, 540: Battery deterioration detection unit, 552: State estimation unit, 554: Error variance calculation unit.

Claims

1. A method for estimating the state of a battery using an apparatus having a Kalman filter arithmetic unit, a battery equivalent circuit model recording unit, and a system noise covariance calculation unit, comprising: a step in which the Kalman filter arithmetic unit sequentially estimates the state of charge and capacity degradation rate of the battery, the current offset of the current sensor, and the internal resistance of the battery set in the battery equivalent circuit model, using measurement values from a voltage sensor, a current sensor, and a temperature sensor installed in the battery, and the battery equivalent circuit model recorded in the battery equivalent circuit model recording unit; a step in which the system noise covariance calculation unit changes the system noise covariance value, which is a set value used by the Kalman filter arithmetic unit, depending on whether it is used for estimation during charging of the battery or during discharging of the battery; wherein the system noise covariance value includes the system noise covariance values of the current offset and the internal resistance; and wherein the system noise covariance values of the current offset and the internal resistance are made smaller during discharging than during charging. A method for estimating the state of a battery.

2. An apparatus for estimating the state of a battery, having a Kalman filter arithmetic unit, a battery equivalent circuit model recording unit, and a system noise covariance calculation unit, wherein: the Kalman filter arithmetic unit sequentially estimates the state of charge and capacity degradation rate of the battery, the current offset of the current sensor, and the internal resistance of the battery set in the battery equivalent circuit model, using measurement values from a voltage sensor, a current sensor, and a temperature sensor installed in the battery, and the battery equivalent circuit model recorded in the battery equivalent circuit model recording unit; the system noise covariance calculation unit changes the system noise covariance value, which is a set value used by the Kalman filter arithmetic unit, depending on whether it is used for estimation during charging of the battery or during discharging of the battery; wherein the system noise covariance value includes the system noise covariance values of the current offset and the internal resistance; and wherein the system noise covariance values of the current offset and the internal resistance are made smaller during discharging than during charging. An apparatus for estimating the state of a battery.

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