Battery management device

Abstract

This invention provides a battery management device capable of accurately estimating the battery's degradation state under various conditions. The battery management device includes a controller for estimating the battery's degradation state. The controller temporarily estimates the battery's degradation state using multiple methods, including a first method, and estimates the battery's degradation state based on values ​​obtained by adding weighting coefficients to the temporary degradation state estimated using the first method and the temporary degradation state estimated using other methods. Furthermore, the first method is a method of estimating the battery's temporary degradation state based on open-circuit voltage and accumulated power; the controller adjusts the weighting coefficients to correspond to the time-varying amount of the open-circuit voltage.

Description

Technical Field

[0001] This invention relates to a battery management device. Background Technology

[0002] Patent document 1 describes a power supply device that estimates the deterioration state of a battery that stores electricity for driving an electric vehicle.

[0003] Existing technical documents Patent documents Patent Document 1: Japanese Patent Application Publication No. 2008-122165 Summary of the Invention

[0004] Technical issues In electric vehicles, it is essential to accurately determine the degradation state of the batteries that store electricity for driving. Among the methods for estimating the degradation state of batteries, there are several estimation methods with different tendencies and characteristics, such as those where the estimation accuracy varies significantly depending on the conditions at the time of estimation, and those where the estimation accuracy does not vary significantly but is difficult to obtain with high precision.

[0005] The purpose of this invention is to provide a battery management device that can accurately estimate the degradation state of a battery under various conditions.

[0006] Technical solution One aspect of the battery management device of the present invention is characterized in that, The battery management device is mounted on an electric vehicle, which has a driving motor for driving the drive wheels and a battery for supplying power to the driving motor. The battery management device includes: A voltage sensor that can detect the open-circuit voltage of the battery; The measuring unit measures the cumulative power of the battery; and The controller, which estimates the degradation state of the battery, The controller temporarily estimates the battery's degradation state using multiple methods, including the first method. The estimated battery degradation state is based on values ​​obtained by adding weighting coefficients to the temporary degradation state estimated using the first method and the temporary degradation state estimated using other methods. The first method is a method of estimating the temporary degradation state of the battery based on the open-circuit voltage and the accumulated power. The controller causes the weighting coefficients to change in accordance with the time variation of the open-circuit voltage.

[0007] Technical effect The first method for estimating the battery's degradation state based on open-circuit voltage and accumulated power is characterized by achieving high estimation accuracy when the open-circuit voltage is stable. According to the present invention, since the aforementioned weighting coefficients are determined based on the time variation of the open-circuit voltage, the battery's degradation state can be estimated with high accuracy under various conditions. Attached Figure Description

[0008] Figure 1 This is a block diagram illustrating an electric vehicle equipped with a battery management device according to an embodiment of the present invention.

[0009] Figure 2 This is a diagram illustrating the method for estimating the degradation state of the first approach.

[0010] Figure 3 This is a diagram illustrating the relational data used in the method for estimating the degradation state in the second approach.

[0011] Figure 4 This is a diagram illustrating the method for estimating the degradation state of the second approach.

[0012] Figure 5 This is a graph showing an example of the change in the open-circuit voltage of a battery after discharge has ended.

[0013] Figure 6 This is a graph showing an example of the change in the open-circuit voltage of a battery at the end of charging.

[0014] Figure 7 This is a flowchart illustrating the sequence of deterioration state estimation processes performed by the controller.

[0015] Figure 8 Is to show execution Figure 7 A timing diagram of an example of voltage changes in a battery during a presumed degradation process.

[0016] Figure 9 This is a flowchart illustrating the sequence of battery data acquisition processes performed by the controller in the variant example.

[0017] Figure 10 This is a flowchart illustrating the sequence of degradation state estimation processes performed by the controller in the variant example.

[0018] Figure 11 This is a diagram illustrating the relational data used in a third-party method for estimating the state of degradation.

[0019] Symbol Explanation 1. Electric vehicles 2 drive wheels 3. Driving motor 4 batteries 5. Converter 6. Driving Operations Unit 6a Steering Unit 6b Acceleration Operation Unit 6c Brake Operation Unit 7. Control Panel 8 Vehicle controller 9. Charger 20 Battery Management Device 21 Voltage Sensor 22 Current Sensor 23 Temperature sensor 25 Controllers 25a Storage Section 25b Cumulative Department 25d1 relational data Detailed Implementation

[0020] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0021] Figure 1 This is a block diagram showing an electric vehicle 1 equipped with a battery management device 20 according to an embodiment of the present invention. The battery management device 20 of this embodiment is mounted on the electric vehicle 1, which includes a drive motor 3 that drives drive wheels 2 and a battery 4 that supplies power to the drive motor 3. Specifically, the electric vehicle 1 includes drive wheels 2, a drive motor 3 that drives drive wheels 2, a battery 4 that stores power supplied to the drive motor 3, a converter 5 that transmits power between the battery 4 and the drive motor 3, a driving control unit 6 and a control panel 7 for driver operation, a vehicle controller 8 for controlling driving, a charger 9 that receives power from outside the vehicle body via a connector 9a to charge the battery 4, and a battery management device 20 for managing the battery 4. The driving control unit 6 includes a steering part 6a such as a steering handle, an acceleration operation part 6b such as an accelerator pedal, and a braking operation part 6c such as a brake pedal. The control panel 7 includes a display that can output the status of the battery 4.

[0022] Battery 4 is a lithium secondary battery, nickel-metal hydride secondary battery, etc., but is not specifically limited to these types.

[0023] The vehicle controller 8 receives signals from the driving operation unit 6 and controls the converter 5 and the braking device (not shown) to accelerate or decelerate in accordance with driving operations. The vehicle controller 8 is an ECU (Electronic Control Unit) that operates according to a control program. The vehicle controller 8 communicates with the controller 25 of the battery management device 20 and, by cooperating with the controller 25, enables the driving motor 3 to operate in a power or regenerative manner, in addition to driving operations, based on the state of the battery 4.

[0024] The battery management device 20 includes a voltage sensor 21 capable of detecting the open-circuit voltage of the battery 4, an accumulation unit 25b measuring the accumulated power of the battery 4, and a controller 25 for determining the estimated state of energy (SOCE) CE[%]. It should be noted that the voltage sensor 21 measures the voltage between the terminals of the battery 4 and is capable of detecting the open-circuit voltage based on the measurement results when no current flows through the battery 4. Furthermore, the accumulation unit 25b calculates the accumulated power based on the output of the current sensor 22 (described later) and the output of the voltage sensor 21.

[0025] The estimated degradation state CE value is used as display data when the status of battery 4 is displayed on the operation panel 7, and as a parameter in the formula for calculating SOC (state of charge: remaining charge) based on the cumulative charge and discharge values. In addition, the estimated degradation state CE value is used as a parameter for calculating the maintenance period or replacement period of battery 4. It should be noted that the application is not limited to the above examples, and the degradation state CE value can also be used for various other management or control purposes. It should also be noted that other indicators such as SOH (State of Health: health status) can be used to represent the degree of degradation of battery 4.

[0026] The battery management device 20 also includes a current sensor 22 for detecting the current of the battery 4 and a temperature sensor 23 for detecting the temperature of the battery 4. The detection outputs of the voltage sensor 21, the current sensor 22, and the temperature sensor 23 are sent to the controller 25.

[0027] Voltage sensor 21 is a voltmeter that measures the voltage between the terminals of battery 4. It detects the open-circuit voltage by measuring the voltage when battery 4 is not being charged or discharged. It should be noted that the open-circuit voltage is not limited to the voltage when the terminals are strictly open, but also includes the voltage measured when there is a small discharge current or charging current that is capable of measuring the same voltage as when the terminals are open.

[0028] The accumulator 25b calculates the power at each moment based on the outputs of the voltage sensor 21 and the current sensor 22, and accumulates the power. In this embodiment, the accumulator 25b is a function of the controller 25, but the accumulator 25b may also be configured with a different structure than the controller 25.

[0029] The controller 25 is a microcomputer that operates according to a control program and has a storage unit 25a for storing the control program. The controller 25 is an ECU. The controller 25 may also be a BCU (Battery Control Unit) that calculates the input power Win and output power Wout of the battery 4 and communicates with the vehicle controller 8 to manage the input and output of the battery 4. It should be noted that the calculation of the input power Win and output power Wout, as well as the management of the input and output of the battery 4, may also be performed by other control units.

[0030] During the period from the end of driving to the end of the vehicle system, and during the period from the end of charging via charger 9 to the start of driving or the end of the vehicle system, controller 25 performs a presumption process for the degradation state CE[%] of battery 4. In addition, if various presumption start conditions are met, controller 25 may also start and complete the presumption process for the degradation state CE[%] of battery 4.

[0031] The controller 25 uses multiple methods, including the first method (e.g., the first method and the second method), to temporarily estimate the degradation state CE[%] of the battery 4. Furthermore, the controller 25 estimates the degradation state CE[%] of the battery 4 based on the values ​​obtained by applying weighting coefficients w1 and w2 to the temporary degradation state CE1[%] estimated using the first method and the temporary degradation state CE2[%] estimated using other methods (e.g., the second method).

[0032] The following provides a detailed explanation of the temporary estimation of the deterioration state based on the first method, the temporary estimation based on the second method, and the final estimation using weighted coefficients w1 and w2.

[0033] <First Method of Presumption> The first method estimates the temporary degradation state CE1 based on the open-circuit voltage OCV [V] and accumulated charge [Wh] of battery 4. The controller 25 is provided with pre-defined relational data 25d1 showing the correspondence between the stable open-circuit voltage OCV and the state of charge (SOC) [%]. This relational data 25d1 can be tabular data or data representing a function. The open-circuit voltage OCV of battery 4 has a high-precision correlation with the SOC of battery 4 when it is stable. Therefore, the controller 25 can use the relational data 25d1 to calculate the SOC of battery 4 with high precision based on the stable open-circuit voltage OCV.

[0034] Figure 2 This is a diagram illustrating the method for estimating the degradation state of the first approach. Figure 2 This is a timing diagram showing an example of the time-varying voltage V when the battery 4 is charged after the electric vehicle 1 has finished driving. In this timing diagram, during the charging period T3, the actual SOC of the battery 4 increases from 21% to 80%. Furthermore, due to the placement periods T2 and T4, stable open-circuit voltages OCV_Lo and OCV_Hi can be measured at the start of charging t1 and at t3 after the end of charging t2, respectively.

[0035] Under these conditions, the controller 25 measures the charging energy during the charging period T3 by accumulating the charging power during the charging period T3. Furthermore, the controller 25 uses relational data 25d1 to convert the open-circuit voltages OCV_Lo and OCV_Hi into State of Charge (SOC). Since the open-circuit voltages OCV_Lo and OCV_Hi are stable values, the SOC calculated from them can represent the actual SOC with a small error. For example, when the actual SOC is 21% and 80%, the calculated SOC is 20.2% and 80.2%, respectively. Therefore, the controller 25 can estimate the increase or decrease in SOC caused by charging with a small error. For example, when the actual increase or decrease in SOC is +59% (=80%-21%), the estimated increase or decrease is +60% (=80.2%-20.2%), etc.

[0036] On the other hand, regarding the cumulative result of charging power, i.e., charging energy, the power can be calculated based on the measurement of voltage and current. By accumulating the calculated power, an accurate value can be obtained. Figure 2 The timing diagram is set to 50kWh.

[0037] The controller 25 can calculate the current fully charged capacity of the battery 4 based on these values ​​as described below. Specifically, based on the energy required to increase the SOC by 60% being 50 kWh, the fully charged capacity of the battery 4, i.e., the energy required to increase the SOC from 0% to 100%, can be calculated as 83.3 kWh (=50 kWh × 100 / 60). Furthermore, the fully charged capacity of the battery 4 when it is new is provided to the controller 25 in advance (set to 100 kWh). Therefore, the controller 25 can calculate the degradation state of the battery 4, CE1 = 83.3 (=83.3 kWh / 100 kWh), based on the above calculation result of the fully charged capacity.

[0038] On the other hand, the open-circuit voltage (OCV) of battery 4 is unstable immediately after charging and discharging, and it takes time for it to stabilize. Therefore, when it is not possible to obtain... Figure 2 Under the conditions of sufficiently long placement periods T2 and T4 as shown, the error of SOC calculated from the open-circuit voltage OCV increases, and the error of the deterioration state CE1 obtained by the first method also increases.

[0039] <Second Method of Prediction> Figure 3 This is a diagram illustrating the relational data used in the estimation method of the degradation state in the second approach. Figure 3 The vertical axis represents the rate of increase of the current internal resistance of battery 4 relative to its internal resistance when it was new. Figure 3 The horizontal axis represents the ratio of the current capacity of battery 4 to its capacity when it was new.

[0040] The second method is to estimate the temporary degradation state CE2 based on the internal resistance R of battery 4. The internal resistance R of battery 4 increases due to degradation. Therefore, as... Figure 3 As shown, the increase in internal resistance R is related to the fully charged capacity. The data indicating how much the internal resistance R increases and how much the fully charged capacity decreases are inherent values ​​of battery 4. Therefore, as... Figure 3 As shown, the manufacturer measures data representing the relationship between the rate of increase of the internal resistance R and the rate of change of the fully charged capacity, and provides this relationship data L2 to the controller 25 in advance. The relationship data L2 is calculated through regression analysis or similar methods and provided to the controller 25 in advance in the form of tabular data or functions. Therefore, the controller 25 can use a second method to calculate the degradation state CE2 of the battery 4 based on the internal resistance R of the battery 4 and the relationship data L2 representing the aforementioned relationship.

[0041] Figure 4 This is a diagram illustrating the method for estimating the degradation state of the second approach. Figure 4 The vertical axis represents the current I of battery 4. Figure 4The horizontal axis represents the voltage V2 applied to the internal resistance R of battery 4 (the voltage obtained by subtracting the voltage drop in the external load from the output voltage V of battery 4).

[0042] The internal resistance R of battery 4 can be calculated based on the current and voltage during charging and discharging of battery 4, as well as the magnitude of the external load relative to battery 4, such as the driving motor 3. However, it is difficult to accurately detect the magnitude of this external load, and furthermore, the external load is relatively large compared to the internal resistance R. Therefore, it is difficult to determine the internal resistance R with a small error. Therefore, as... Figure 4 As shown in the diagram, the controller 25 acquires current I and voltage V2 data at multiple moments during the charging and discharging of battery 4, and performs regression analysis and other analytical processing based on the acquired data to estimate the internal resistance R.

[0043] Then, the controller 25 connects the internal resistance R, which is estimated as described above, with... Figure 3 By comparing the relationship data L2, the rate of change of the fully charged capacity can be calculated, and the temporary degradation state CE2 of battery 4 can be calculated. Since the degradation state CE2 calculated in this way includes errors in the estimation of internal resistance R, etc., although the accuracy becomes relatively low, the factors that cause deviation in accuracy itself are relatively few.

[0044] <Final estimation using weighting coefficients w1 and w2> Figure 5 This is a graph showing an example of the change in the open-circuit voltage of battery 4 after the discharge ends. Figure 6 This is a graph illustrating an example of the change in the open-circuit voltage of battery 4 at the end of charging. (See graph for example.) Figure 5 and Figure 6 As shown, the open-circuit voltage OCV of battery 4 used in the first method described above is unstable immediately after discharge and immediately after charging, but becomes a stable voltage value over time after the end of discharge or charging. The open-circuit voltage OCV has the following tendency: after discharge, it changes in the direction of increasing value, with the amount of change increasing immediately after discharge and decreasing over time after the end of discharge. Similarly, the open-circuit voltage OCV has the following tendency: after charging, it changes in the direction of decreasing value, with the amount of change increasing immediately after charging and decreasing over time after the end of charging. Therefore, the time change of the open-circuit voltage OCV |Dv / Dt| ( Figure 5 and Figure 6 The values ​​of |Dv1 / Dt| to |Dv6 / Dt| increase immediately after charging and discharging, and decrease over time after the end of charging and discharging. The change over time is represented by absolute values, and the symbol “||” represents absolute values.

[0045] In the first method described above, the open-circuit voltage OCV is converted to SOC using relational data 25d1. The open-circuit voltage OCV of relational data 25d1 is equivalent to the stable open-circuit voltage OCV. Therefore, when converting the SOC using the open-circuit voltage OCV immediately after discharge or charge, the error of the converted SOC becomes larger, and the temporary degradation state CE1 estimated in the first method includes a large error. On the other hand, using the stable open-circuit voltage OCV to estimate the temporary degradation state CE1 using the first method yields a highly accurate value. That is, when using the open-circuit voltage OCV measured immediately after charge / discharge, the accuracy of the temporary degradation state CE1 becomes lower, while when using the open-circuit voltage OCV after a longer period of time since the end of charge / discharge, the accuracy of the temporary degradation state CE1 becomes higher.

[0046] On the other hand, as mentioned above, although the accuracy of the estimated temporary degradation state CE2 in the second method is relatively low, there are fewer factors that cause deviations in accuracy. Therefore, there is no significant difference in accuracy for each estimated degradation state CE2 immediately after discharge, immediately after charging, after a long period of time since the end of discharge, and after a long period of time since the end of charging.

[0047] Therefore, based on the estimated time, the temporary deterioration states CE1 and CE2 are either high precision and relatively low fixed precision, or the temporary deterioration states CE1 and CE2 are either low precision and relatively low fixed precision. In other words, the relationship between the precision values ​​changes.

[0048] Therefore, the controller 25 applies weighting coefficients w1 and w2 to the temporary degradation state CE1 estimated using the first method and the temporary degradation state CE2 estimated using the second method, respectively, to compensate for the changes in the aforementioned accuracy relationship. Then, the controller 25 calculates the final degradation state CE based on the values ​​of the added weighting coefficients w1 and w2. Specifically, the calculation is performed as shown in equation (1).

[0049] CE = w1 × CE1 + w2 × CE2 (1) Where w1 + w2 = 1.

[0050] The controller 25 determines the weighting coefficients w1 and w2 based on the time variation of the open-circuit voltage OCV. For example, when the controller 25 acquires the output of the voltage sensor 21 as the value of the open-circuit voltage OCV using the first method, it acquires the value of the open-circuit voltage OCV at other times within a short time interval (e.g., 2 seconds to 10 seconds). Then, it calculates the time variation of the open-circuit voltage OCV based on the values ​​of the open-circuit voltage OCV acquired at multiple times. More specifically, in the case of performing the estimation process of the first method, the controller 25 acquires the difference Dv between two open-circuit voltages OCV at a predetermined short time interval Dt, and sets Dv / Dt as the time variation of the open-circuit voltage OCV. Then, as shown in the conversion table below, the controller 25 determines the weighting coefficients w1 and w2 corresponding to the time variation Dv / Dt. The first column of the conversion table shows the absolute value of the time variation Dv / Dt. Through the determination of such weighting coefficients w1 and w2, the weighting coefficients w1 and w2 change in accordance with the time variation of the open-circuit voltage OCV.

[0051] Table 1 (Conversion table)

[0052] In the conversion table, the smaller the time variation of the open-circuit voltage OCV, the larger the weighting coefficient w1 of the first method. A small time variation means a small absolute value of Dv / Dt. This indicates that the smaller the time variation of the open-circuit voltage OCV, the closer the open-circuit voltage OCV is to a stable value. Therefore, through the above settings, when it is assumed that the accuracy of the temporary degradation state CE1 estimated using the first method is high, the weighting coefficient w1 of the first method becomes larger, which in turn improves the accuracy of the final estimated result, i.e., the degradation state CE.

[0053] It should be noted that the relationship between the time variation of the open-circuit voltage OCV, Dv / Dt, and the weighting coefficients w1 and w2 described above is an example. An appropriate relationship can be established through experimentation or simulation to improve the accuracy of the final degradation state, CE. In the above conversion table, in any case, the weighting coefficient w1 is greater than or equal to the weighting coefficient w2, but this relationship is not limited to this.

[0054] For reference Figure 2As explained, when estimating the temporary degradation state CE1 using the first method, the controller 25 uses two open-circuit voltages, OCV: the one acquired at the start of power accumulation and the one acquired at the end of power accumulation. Therefore, it is conceivable that the accuracy of the two open-circuit voltages OCV may differ. However, in the above example, this is based on the premise that the open-circuit voltage OCV acquired by the controller 25 at the start of power accumulation is highly accurate. This premise can be ensured by the controller 25 selecting a state where the battery 4 has been in a dormant state for a relatively long time as the start of power accumulation. Alternatively, this premise can be ensured by the controller 25 resetting the accumulated power value during the interval between charging and discharging, once the open-circuit voltage OCV is detected to be stable.

[0055] It should be noted that, not limited to the above examples, the controller 25 can also calculate the time change of the open-circuit voltage OCV at both the start of the accumulation of charging and discharging power and at the end of the accumulation, and determine the weighting coefficients w1 and w2 based on the time change at these two times. Next, taking the case where the time change of the open-circuit voltage OCV at the start of accumulation is Dv1 / Dt1 and the time change of the open-circuit voltage OCV at the end of accumulation is Dv2 / Dt2 as examples, the determination methods of the first and second examples are shown. Here, as predetermined shorter times Dt1 and Dt2, different signs are applied at the start and end of accumulation, but these values ​​can be the same or set to different values.

[0056] As shown in equation (2) below, the first example sets the average of the time changes Dv1 / Dt1 and Dv2 / Dt2 of the two as the comprehensive time change Dv0 / Dt. On the other hand, a method for determining the conversion table of weighting coefficients w1 and w2 corresponding to the comprehensive time change Dv0 / Dt is prepared in advance. The conversion table is constructed in the same way as the above "conversion table" and changed to a conversion table corresponding to the comprehensive time change Dv0 / Dt.

[0057] Dv0 / Dt=(|Dv1 / Dt1|+|Dv2 / Dt2|) / 2 (2) Then, the weighting coefficients w1 and w2 can be determined by the same process as that used with the conversion table described above.

[0058] The second example is a method for determining the weighting coefficients w1 and w2 by using a function derived from the time change at the start of the accumulation, Dv1 / Dt, and the time change at the end of the accumulation, Dv2 / Dt, as independent variables. That is, the lower the value of |Dv1 / Dt| of the temporary deterioration state CE1 estimated using the first method, the higher the reliability, and the lower the value of |Dv2 / Dt|, the higher the reliability. A function that reflects this level of reliability in the weighting coefficient w1 can be used. Examples of such functions include equations (3) and (4).

[0059] w1=|Dv1 / Dt|×(1-|Dv1 / Dt| / (|Dv1 / Dt|+|Dv2 / Dt|))+|Dv2 / Dt| (3) w2=1-w1 (4) In the second example, let Dt1 = Dt2, and let them be represented by the same notation Dt.

[0060] <Presumed Deterioration Condition Treatment> Figure 7 This is a flowchart illustrating the sequence of deterioration state estimation processes performed by the controller. Figure 8 Is to show execution Figure 7 A timing diagram illustrating an example of voltage changes in a battery during a presumed degradation process. It should be noted that... Figure 8 The timing diagram shows that battery 4 is mainly in a period of heavy discharge, but the voltage change of battery 4 is stable during charging. Therefore, it is possible to estimate the degradation state CE without discharging, but to estimate the degradation state CE during charging.

[0061] In the degradation state estimation process, the controller 25 determines whether a voltage change (i.e., an increase or decrease) or a reversal of the voltage change direction has occurred (step S1). If the result is negative, the controller 25 repeatedly executes the determination process of step S1. Through the determination process of step S1, for example, it is possible to determine if... Figure 8 The time t11 indicates when battery 4 transitions from a state of not charging or discharging to the state of starting to discharge due to driving, etc. If the result of step S1 is yes, then controller 25 causes the process to proceed to the next step.

[0062] If the process continues, the controller 25 repeatedly performs steps S2 to S4 until the voltage change direction reverses and the current stops. Step S2 is the process of calculating the cumulative current of battery 4. Step S3 is the process of determining the reversal of the voltage change direction, and step S4 is the process of determining the cessation of the current.

[0063] Through this cyclic process, the current of battery 4 is accumulated until the state of battery 4 reaches a state that can be presumed to be a deterioration state CE. It should be noted that the accumulated current calculation result in step S2 is combined with the voltage value data and accumulated, and the controller 25 can calculate the accumulated power based on this data.

[0064] The judgments in steps S2 and S3, for example, can determine... Figure 8 The time t12 indicates when battery 4 stops discharging from the discharging state. Similarly, the judgments in steps S2 and S3 can determine when battery 4 stops charging from the charging state. In this case, the current stops and the direction of voltage change is reversed.

[0065] As a result, if the loop processing of steps S2 to S3 is terminated, the controller 25 determines whether the amount of accumulated current (the amount of accumulated period or the amount of change in accumulated value) in step S2 is above the accumulated amount threshold representing a sufficient amount to presuppose the degradation state CE of the battery 4 (step S5). Then, if yes, the controller 25 causes the processing to proceed to the next step; but if no, the controller 25 causes the processing to return to step S1, and the processing is repeated from step S1 onwards.

[0066] If the accumulated current is sufficient and the process proceeds to the next step, the controller 25 acquires the output of the voltage sensor 21 as the first open-circuit voltage OCV (step S6). Afterward, the controller 25 waits for a short time (step S7), and then acquires the output of the voltage sensor 21 as the second open-circuit voltage OCV (step S8). Then, the controller 25 determines whether a reversal of the voltage change direction has occurred (step S9). If not, the process returns to step S6 and repeats the process from step S6 onward. By repeating steps S6 to S9, the controller can acquire voltage according to the situation. Figure 5 and Figure 6 The multiple times Dv1 / Dt~Dv6 / Dt.

[0067] On the other hand, if the result is yes in step S9, it means that OCV measurement cannot be performed, so the controller 25 proceeds to the next step. Then, based on the first open-circuit voltage OCV and the second open-circuit voltage OCV obtained in the cyclic processing of steps S6 to S9, the time change amount Dv / Dt of the open-circuit voltage OCV is calculated (step S10). Then, as described above, the controller 25 determines the weighting coefficients w1 and w2 corresponding to the time change amount Dv / Dt (step S11). Furthermore, the controller 25 estimates the degradation state CE[%] of the battery 4 based on the values ​​obtained by adding weighting coefficients w1 and w2 to the temporary degradation state CE1[%] estimated using the first method and the temporary degradation state CE2[%] estimated using the second method, respectively (step S12).

[0068] Then, the value of the degradation state CE held by the controller 25 is updated using the value calculated in step S12 (step S13), ending one degradation state estimation process. Then, the controller 25 restarts the next degradation state estimation process at an appropriate time. If the controller 25 requests a degradation state CE value from another ECU, it transfers the registered degradation state CE value to the requesting ECU as needed.

[0069] The aforementioned degradation state estimation process is stored in a non-transitory computer-readable medium, such as the storage unit 25a of the controller 25. The controller 25 may also be configured to read and execute a program stored on a portable non-transitory recording medium. The portable non-transitory storage medium may also store at least one of the aforementioned degradation state estimation processes.

[0070] <Presumed Deterioration Status Treatment for Modified Examples> Figure 9 This is a flowchart illustrating the sequence of data acquisition processing of the battery 4 performed by the controller 25 in a modified example. The data acquisition processing is a process that is always performed when the controller 25 is activated. The controller 25 periodically acquires the outputs of the voltage sensor 21 and the current sensor 22 of the battery 4 (step S21), and performs cumulative charging and discharging power processing based on these outputs (step S22). Furthermore, it determines whether the condition for stable OCV of the battery 4 is met (step S23), and if it is determined to be stable, it stores the value of the voltage sensor 21 at this time as OCV (step S24). The condition in step S23 can appropriately apply conditions that ensure stable OCV, such as the battery 4 neither charging nor discharging for a time exceeding a threshold time. Then, the controller 25 repeatedly executes the above-described processes S21 to S24.

[0071] Figure 10This is a flowchart illustrating the sequence of degradation state estimation processing performed by controller 25 in a modified example. If degradation state estimation processing begins when controller 25 is started, controller 25 repeatedly performs a process to determine whether a preset estimation request has occurred (step S31). An estimation request is, for example, when the voltage of battery 4 becomes a preset change pattern, and the amount of current accumulation (or power accumulation) since the last acquisition of open-circuit voltage OCV exceeds a threshold (a threshold indicating a sufficient amount). The preset change pattern is a pattern where the voltage change direction is reversed, etc. It should be noted that various requests can be applied as preset estimation requests. For example, it is also possible to apply a request operation by the user to terminate the system of electric vehicle 1 after the electric vehicle 1 has finished driving or charging, or a request operation by the user via the operation panel 7 to display items including the degradation state of battery 4 after the electric vehicle 1 has finished driving or charging. In addition, estimation requests can also occur under various conditions during the period when the charging and discharging of battery 4 is stopped.

[0072] As a result, if the determination in step S31 is yes, the controller 25 confirms that the charging and discharging of the battery 4 has stopped (step S32). If it has stopped, the controller acquires the output of the voltage sensor 21 as the first open-circuit voltage OCV (step S33). Furthermore, the controller 25 waits for a short predetermined time (step S34), and then acquires the output of the voltage sensor 21 again as the second open-circuit voltage OCV (step S35).

[0073] Then, the controller 25 uses the first open-circuit voltage OCV to calculate the temporary degradation state CE1 using a first method (step S36). In addition, the controller 25 uses a second method to calculate the temporary degradation state CE2 (step S37).

[0074] Next, the controller 25 calculates the time change of the OCV based on the first OCV and the second OCV (step S38), and then determines the weighting coefficients w1 and w2 corresponding to the time change (step S39). Then, the controller 25 calculates the final degradation state CE based on the values ​​obtained by adding weighting coefficients w1 and w2 to the temporary degradation states CE1 and CE2 respectively (step S40).

[0075] Then, the controller 25 transfers the calculated degradation state CE value to the request source of the presumed request determined in step S31 (step S41). Through the processing in step S41, for example, if the user terminates the electric vehicle 1 system after driving or charging, the controller 25 registers the calculated degradation state CE into the management data before the system terminates. Additionally, if the user displays the degradation state of the battery 4 after driving or charging, the controller 25 transfers the calculated degradation state CE to the display processing. Thus, the calculated degradation state CE is displayed on the display section of the operation panel 7, etc.

[0076] In the degradation state estimation process of the modified example, there are cases where a degradation state CE estimation request is generated when not much time has passed since the end of charging and discharging of battery 4, and the degradation state CE1 is calculated using the first method based on the unstable open-circuit voltage OCV. On the other hand, there are cases where the above estimation request is generated when a long time has passed since the end of charging and discharging of battery 4, and the degradation state CE1 is calculated using the first method based on the stable open-circuit voltage OCV. Comparing these cases, the magnitude of the error included in the degradation state CE1 calculated using the first method is different. However, by using the degradation state CE2 estimated using other methods in step S37 and the final degradation state CE calculated using weighted coefficients w1 and w2 in step S39, it is possible to suppress the case where the final estimation result includes a large error.

[0077] At least one of the data acquisition processing program and the degradation state estimation processing program described above is stored in a non-transitory storage medium such as the storage unit 25a of the controller 25. The controller 25 may also be configured to read the program stored in a portable non-transitory recording medium and execute the program. The portable non-transitory storage medium described above may also store at least one of the data acquisition processing program and the degradation state estimation processing program described above.

[0078] In the above embodiment, the controller 25 estimates the temporary degradation states CE1 and CE2 using the first method and the second method, respectively. However, the controller 25 may further estimate the temporary degradation state CE3 using other methods, and calculate the final degradation state CE based on the values ​​obtained by adding weighting coefficients w1 to w3 to the three temporary degradation states CE1 to CE3. The aforementioned other methods may also include two or more methods, calculating the final degradation state CE based on the values ​​obtained by adding weighting coefficients w1 to four or more temporary degradation states. Alternatively, even when using the first method and the second method to estimate the degradation states CE1 and CE2, the second method is not limited to the above method, and other methods may be used. Next, as an example of these other methods, a third-party method for estimating the degradation state is shown.

[0079] Figure 11 This is a diagram illustrating the relational data used in the third-party method for estimating the degradation state. The third method estimates the temporary degradation state CE3 based on the heat generated by battery 4. As the degradation of battery 4 progresses, the heat generated increases with the increase in internal resistance, even when the same discharge current or the same charging current is flowing. Figure 11 The chart shows the heat generated by battery 4 under conditions of continuous current flow over a certain period of time. For example... Figure 11 As shown in the chart, the higher the current value, the greater the heat generation, and the more advanced the degradation, the greater the heat generation.

[0080] Pre-providing controller 25 with, for example Figure 11 The chart shows the relationship between current, degradation state, and heat generation. This relationship data can be in the form of a data table, a function, etc., but the data format is not limited as long as the degradation state can be determined from the current and heat generation.

[0081] When using the third method to estimate the degradation state CE3, the controller 25 measures the time and current value (current value at each time point) when the current flowing through the battery 4 is constant during the operation of the electric vehicle 1. Then, the controller 25 calculates the averaged current value over a predetermined time period based on the measurement results. The controller 25 also calculates the heat generated by the battery 4 during this period. If the battery 4 is neither cooled nor heated, the heat generated can be calculated based on the temperature rise and heat capacity of the battery 4; if the battery 4 is cooled or heated, the heat generated can be calculated based on the amount of cooling or heating, the temperature change of the battery 4, and the heat capacity. Then, by substituting the aforementioned averaged current value and heat generated into the relational data, the controller 25 can determine the temporary degradation state CE3.

[0082] As described above, in the battery management device 20 of this embodiment, the controller 25 estimates a temporary degradation state CE1 based on the open-circuit voltage and accumulated power of the battery 4 using a estimation method of the first method. Furthermore, the controller 25 estimates the degradation state CE based on the values ​​obtained by adding weighting coefficients w1 and w2 to the aforementioned temporary degradation state CE1 and a temporary degradation state CE2 estimated using other methods. In addition, the controller 25 adjusts the weighting coefficients w1 and w2 in a way that corresponds to the time variation of the open-circuit voltage OCV of the battery 4. Therefore, when the accuracy of the temporary degradation state CE1 is high, such as when a stable open-circuit voltage OCV can be obtained, the controller 25 can determine a final degradation state CE that increases the influence of the temporary degradation state CE1. On the other hand, when the accuracy of the temporary degradation state CE1 is low, such as when the first estimation method is applied when the open-circuit voltage OCV is unstable, the controller 25 can determine a final degradation state CE that reduces the influence of the temporary degradation state CE1. Therefore, from situations where a stable open-circuit voltage OCV can be obtained to situations where a stable open-circuit voltage OCV is difficult to obtain, the controller 25 can estimate the deterioration state CE with high overall accuracy under various conditions.

[0083] Furthermore, according to the battery management device 20 of this embodiment, the method of estimating the temporary degradation state CE2 based on the internal resistance of the battery 4 is used as a second estimation method. This estimation method can obtain estimation accuracy that is not heavily dependent on the stability of the open-circuit voltage OCV. Therefore, the degradation state CE2 can compensate for the reduced accuracy of the temporary degradation state CE1 estimated using the first method, and the controller 25 can determine the degradation state CE with high accuracy within the appropriate range.

[0084] Furthermore, according to the battery management device 20 of this embodiment, the controller 25 acquires the open-circuit voltage OCV for estimating the first mode. The controller 25 also varies the weighting coefficients w1 and w2 in a manner corresponding to the time variation of the open-circuit voltage when acquiring the OCV. Therefore, the controller 25 can utilize a portion of the process for acquiring the open-circuit voltage OCV to achieve higher processing efficiency. Moreover, the time variation of the open-circuit voltage OCV effectively represents the stability of the open-circuit voltage OCV used in the estimation of the first mode. Therefore, the controller 25 can determine the weighting coefficients w1 and w2 that accurately reflect the temporary degradation state CE1. Thus, the controller 25 can determine the degradation state CE with high accuracy within the appropriate range.

[0085] It should be noted that if a considerable amount of time has passed since the acquisition time of the open-circuit voltage OCV obtained for the estimation in the first mode, the controller 25 may also measure the time change of the open-circuit voltage OCV at a time different from the acquisition time. Then, the controller 25 may also calculate the weighting coefficients w1 and w2 based on this time change.

[0086] Furthermore, according to the battery management device 20 of this embodiment, for the controller 25, the smaller the time variation of the open-circuit voltage OCV, the larger the weighting coefficient w1 added to the temporary degradation state CE1 estimated using the first method. Therefore, the controller 25 can determine the degradation state CE with higher accuracy of the temporary degradation state CE1, which further increases the influence of that value. Thus, the controller 25 can determine the degradation state CE with high accuracy within the appropriate range.

[0087] Furthermore, according to the battery management device 20 of this embodiment, the controller 25 has relational data 25d1 representing the correspondence between the open-circuit voltage OCV of the battery 4 and the SOC (state of charge) of the battery 4. Then, in the first mode, the controller 25 estimates the temporary degradation state CE1 of the battery 4 based on the open-circuit voltage OCV, the relational data 25d1, and the accumulated power. With this configuration, the controller 25 can quickly determine the temporary degradation state CE1 with less load.

[0088] The embodiments of the present invention have been described above. However, the present invention is not limited to the embodiments described above. For example, in the embodiments described above, a second method and a third-party estimation method were described as methods for estimating a temporary deterioration state using a method different from the first method. However, various other methods described above can also be applied. As other methods described above, it may be a method in which the tendency of the estimation accuracy to change is different from that of the first method. In addition, the details shown in the embodiments can be appropriately changed without departing from the spirit of the invention.

[0089] Industrial availability This invention can be used in battery management devices.

Claims

1. A battery management device, characterized in that, Equipped in an electric vehicle, the electric vehicle having a drive motor for driving drive wheels and a battery for supplying power to the drive motor, the battery management device includes: A voltage sensor that can detect the open-circuit voltage of the battery; The accumulator unit measures the cumulative power of the battery; and The controller, which estimates the degradation state of the battery, The controller uses multiple methods, including the first method, to temporarily estimate the battery's degradation state. The estimated battery degradation state is based on values ​​obtained by adding weighting coefficients to the temporary degradation state estimated using the first method and the temporary degradation state estimated using other methods. The first method is a method of estimating the temporary degradation state of the battery based on the open-circuit voltage and the accumulated power. The controller causes the weighting coefficients to change in accordance with the time variation of the open-circuit voltage.

2. The battery management device according to claim 1, characterized in that, The multiple methods include the first method and the second method. The second method is to estimate the temporary deterioration state of the battery based on its internal resistance.

3. The battery management device according to claim 1, characterized in that, The controller causes the weighting coefficients to change in correspondence with the time variation of the open-circuit voltage when the open-circuit voltage is obtained in the estimation used in the first mode.

4. The battery management device according to claim 1, characterized in that, For the controller, the smaller the time variation, the larger the weighting coefficient added to the temporary deterioration state estimated using the first method.

5. The battery management device according to claim 1, characterized in that, The controller has relational data representing the correspondence between the open-circuit voltage and the battery's charge margin. In the first approach, the temporary degradation state of the battery is estimated based on the open-circuit voltage, the relational data, and the cumulative power.

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