Battery full charge capacity estimation method, battery full charge capacity estimation device, and battery full charge capacity estimation program

Abstract

To estimate the full charge capacity of a battery with relatively high accuracy regardless of the degree of change in the state of charge.SOLUTION: In a battery system 100 managed by a control unit 20 (battery management system), a first estimation unit 46 estimates an estimated value H1 from a value acquired by an acquisition unit 42 and a capacity deterioration model DM1. The acquisition unit 42 acquires information including a voltage value, a temperature value, and a current value. The capacity deterioration model DM1 is a model related to predetermined information and the amount of capacity deterioration of a battery 1. The estimated value H1 is estimated when, for example, an ignition of an electric vehicle is turned on.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a method for estimating a full charge capacity of a battery, a device for estimating a full charge capacity of a battery, and a program for estimating a full charge capacity of a battery. [Background technology]

[0002] Japanese Patent Application Laid-Open Publication No. 2002-243813 discloses a battery capacity degradation calculation device that includes a state-of-charge calculation unit that calculates changes in the state of charge of a secondary battery and a battery capacity calculation unit that calculates the battery capacity of the secondary battery in a degraded state. The degraded battery capacity is calculated from an integrated discharge current value during discharge and changes in the state of charge. The change in the state of charge is calculated from the correlation between the open-circuit voltage and the state of charge and the open-circuit voltage during discharge. Here, the correlation between the open-circuit voltage and the state of charge does not depend on the degraded state of the secondary battery. Therefore, if the correlation is known, it is possible to determine changes in the state of charge of the secondary battery and calculate the degraded battery capacity without, for example, preparing a table of internal resistance degradation of the secondary battery. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-243813 Summary of the Invention [Problem to be solved by the invention]

[0004] Here, Patent Document 1 states that in order to improve the calculation accuracy of the degraded battery capacity, it is advisable to calculate the degraded battery capacity when the change in the state of charge is relatively large. In other words, in order to estimate the full charge capacity of a battery, the change in the state of charge needs to be relatively large.

[0005] The inventors of the present invention wish to estimate the full charge capacity of a battery with relatively high accuracy, regardless of the magnitude of changes in the state of charge. [Means for solving the problem]

[0006] The method for estimating the full charge capacity of a battery disclosed herein is a method for estimating the full charge capacity of a battery managed by a battery management system mounted on an electric vehicle, and includes an acquisition step of acquiring predetermined information about the battery, a capacity degradation model in which the relationship between the predetermined information about the battery and the amount of capacity degradation of the battery is pre-recorded, and a first estimation step of estimating the full charge capacity of the battery based on the predetermined information about the battery acquired by the acquisition step.

[0007] According to this method for estimating the full charge capacity of a battery, the full charge capacity of the battery can be estimated with relatively high accuracy, regardless of the magnitude of changes in the state of charge. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing a battery system 100. As shown in FIG. [Figure 2] FIG. 2 is a graph showing an example of changes in SOC in an electric vehicle equipped with the battery system 100. In FIG. [Figure 3] FIG. 3 is a flowchart for estimating the full charge capacity of the battery 1 according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of a capacity degradation model DM1 that shows the relationship between charge / discharge time and capacity degradation at a predetermined change amount ΔSOC1 of the battery 1. [Figure 5] FIG. 5 is a graph showing the relationship between the open circuit voltage OCV and the SOC. [Figure 6] 10 is a diagram showing a sheet S1 showing the relationship between reliability Re and weighting coefficient W. FIG. [Figure 7A] FIG. 7A is a diagram showing coefficients W1 to W4 for each parameter. [Figure 7B] FIG. 7B is a diagram showing coefficients W5 to W7 for each parameter. [Figure 8]FIG. 8 is a diagram showing an example of a capacity deterioration model DM2 showing the relationship between the standing time at a predetermined temperature and capacity deterioration. [Figure 9] FIG. 9 is a diagram showing a sheet S2 showing the relationship between the reliability Re and the weighting coefficient W according to another embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] An embodiment of the technology disclosed herein will be described below with reference to the drawings. The embodiment described here is, of course, not intended to limit the present invention. The drawings are schematic and do not necessarily reflect the actual product. Furthermore, the same reference numerals are appropriately used for components and parts that perform the same function, and redundant explanations will be omitted where appropriate.

[0010] <Battery System 100> FIG. 1 is a schematic diagram showing a battery system 100. As shown in FIG. 1, the battery system 100 includes a battery 1 and a control unit 20. The battery 1 is connected to an external load (not shown). The control unit 20 manages the charging and discharging of the battery 1. In other words, the control unit 20 is an example of a battery management system according to the present invention. The battery system 100 is, for example, an on-board system for an electric vehicle (battery EV).

[0011] In this specification, the term "battery" refers to an electricity storage device capable of extracting electrical energy. Batteries include secondary batteries that can be repeatedly charged and discharged by the movement of charge carriers between a pair of electrodes (positive and negative electrodes) via an electrolyte, such as lithium-ion secondary batteries. The manner in which the battery is used is not particularly limited. The battery includes a battery pack in which a plurality of batteries (single cells) are electrically connected to one another. In this embodiment, the battery is, for example, a so-called on-board battery that serves as a power source for an electric vehicle. When the battery is used as an on-board battery, the battery is appropriately connected to a charging / discharging device and charged.

[0012] The full charge capacity of the battery 1 decreases over time as the battery 1 is charged and discharged. The full charge capacity is the battery capacity of the battery 1 that is fully discharged after being charged to a maximum charge capacity, that is, a state of charge (SOC) of 100%.

[0013] The control unit 20 includes a sensor 30 and a control device 40. The sensor 30 includes a voltage sensor 31, a temperature sensor 32, and a current sensor 33. The control device 40 includes a storage unit 41, an acquisition unit 42, a charge / discharge time measurement unit 43, a charge / discharge time acquisition unit 44, a calculation unit 45, a first estimation unit 46, a second estimation unit 47, a weighted average unit 48, and a coefficient determination unit 49. The control device 40 may be, for example, a computer such as an ECU (Electronic Control Unit) or a circuit board equipped with a microcomputer. The computer performs required functions according to, for example, a predetermined program. Each function of the computer is processed by the computer's arithmetic unit (also referred to as a processor, CPU (Central Processing Unit), or MPU (Micro-Processing Unit)), storage device (memory, hard disk, etc.), and software working together. In this embodiment, the control device 40 is realized by the ECU. A full charge capacity estimation program 40a is installed in the control device 40. The full charge capacity estimation program 40a is a program configured to realize the respective units 41 to 49 of the control device 40. The control device 40 is configured to be able to communicate with the sensor 30.

[0014] Although not shown in the figures, the control device 40 may be one in which multiple control devices work together. For example, if the control device 40 is connected to an external computer via a LAN cable, the Internet, or the like so as to be able to communicate data with the external computer, the processing of the control device 40 may be performed in cooperation with such an external computer. For example, the information or part of the information stored in the control device 40 may be stored in an external computer, or the processing or part of the processing performed by the control device 40 may be performed by an external computer.

[0015] FIG. 2 is a graph GP showing an example of changes in SOC in an electric vehicle equipped with battery system 100. The horizontal axis of graph GP represents time, and the vertical axis of graph GP represents the SOC of battery 1. In graph GP, the region defined by times 0 to t1 is designated region A1. Similarly, in graph GP, the regions defined by times t1 to t2, t2 to t3, and the region from time t3 onward are designated regions A2, A3, and A4, respectively. Region A1 is a region in which the ignition (hereinafter simply referred to as "ignition") of the electric vehicle equipped with battery system 100 is OFF, and the SOC is SOC1. Region A2 is a state in which the ignition is ON after region A1. For example, the electric vehicle is running for the time corresponding to region A2. However, the SOC may increase while the electric vehicle is running. When the electric vehicle is running, battery 1 (see FIG. 1) discharges, and the SOC decreases. Area A3 is an area where the ignition is OFF and the SOC is SOC2, which is lower than SOC1. Area A4 is an area where the ignition is ON at a time later than area A3.

[0016] The voltage sensor 31 of the sensor 30 shown in FIG. 1 is a sensor that detects the voltage value of the battery 1. The voltage sensor 31 detects the voltage value of the battery 1 as an analog signal, for example. The voltage detected by the voltage sensor 31 is the closed circuit voltage CCV. The detected analog signal is converted into a digital signal by an A / D converter (not shown) and output to an acquisition unit 42 (see FIG. 1 ) of the control device 40, which will be described later. The temperature sensor 32 and current sensor 33 of the sensor 30, like the voltage sensor 31, also detect the temperature and current values ​​of the battery 1, respectively. The detected temperature and current values ​​are transmitted to the acquisition unit 42, which will be described later. The sensor 30 detects each value whether the ignition is on or off. The voltage sensor 31, temperature sensor 32, and current sensor 33 detect the voltage value, temperature value, and current value, respectively, at predetermined intervals. The intervals at which the voltage sensor 31, temperature sensor 32, and current sensor 33 perform detection are not particularly limited, but are, for example, approximately 0.001 to 1 second.

[0017] A method for estimating the full charge capacity of the battery 1 by the control unit 20 will be described below, along with the configuration of the control unit 20. Fig. 3 is a flowchart for estimating the full charge capacity of the battery 1 according to the first embodiment. The flow shown in Fig. 3, excluding the storage step S101, is assumed to be performed at time t3 shown in Fig. 2 (when the ignition is turned on at the boundary between area A3 and area A4). However, the timing at which the flow shown in Fig. 3 starts is not limited to time t3.

[0018] The storage step S101 shown in FIG. 3 is a step of storing a capacity degradation model relating to predetermined information about the battery 1 and the amount of capacity degradation of the battery 1. The storage step S101 can be implemented by the storage unit 41 (see FIG. 1) of the control device 40. In this embodiment, the storage unit 41 stores a capacity degradation model DM1 (see FIG. 4). The capacity degradation model DM1 is acquired and stored in advance through testing, simulation, theoretical calculation, etc. For example, the capacity degradation model DM1 is obtained by acquiring each piece of data when the battery system 100 (see FIG. 1) is manufactured.

[0019] FIG. 4 is a diagram showing an example of a capacity degradation model DM1 showing the relationship between charge / discharge time and capacity degradation for a predetermined change amount ΔSOC1 of the battery 1. The change amount ΔSOC1 is the amount of change when the SOC of the battery 1 changes. As described above, the capacity degradation model DM1 is a model relating to predetermined information and the amount of capacity degradation of the battery 1. In this embodiment, the predetermined information includes temperature, SOC, change amount ΔSOC1, and amount of capacity degradation. The temperature and SOC were measured using a known method when the capacity degradation model DM1 was created.

[0020] In the capacity degradation model DM1, the change ΔSOC1 is calculated by dividing the integrated current by the full charge capacity. Therefore, the predetermined information also includes the integrated current and full charge capacity at the time of acquiring each data. The integrated current and full charge capacity at the time of acquiring each data are acquired by a known method.

[0021] The horizontal axis of the capacity degradation model DM1 represents charge / discharge time. The vertical axis of the capacity degradation model DM1 represents the capacity degradation rate. The capacity degradation rate represents the percentage of the full charge capacity at the time each data point was acquired, with the full charge capacity of Battery 1 at time 0 of charge / discharge time being taken as 100%.

[0022] The capacity degradation model DM1 includes data DT1, DT2, and DT3. In this embodiment, the data DT1 is test data obtained when the battery 1 is charged and discharged with a change amount ΔSOC1 of 80%. The data DT2 is test data obtained when the battery 1 is charged and discharged with a change amount ΔSOC1 of 50%. The data DT3 is test data obtained when the battery 1 is charged and discharged with a change amount ΔSOC1 of 20%. However, the numerical value of the change amount ΔSOC1 in the capacity degradation model DM1 is not limited to these. Furthermore, the number of data included in the capacity degradation model DM1 is not limited to three.

[0023] 4, the capacity degradation model DM1 is data when the temperature of the battery 1 is at temperature T1°C. That is, the data DT1, DT2, and DT3 are data acquired when the temperature of the battery 1 is T1°C when the capacity degradation rate is acquired. Although not shown, the storage unit 41 stores multiple capacity degradation models created from data acquired when the temperature of the battery 1 is a temperature other than T1°C when the capacity degradation rate is acquired. Alternatively, the capacity degradation model for a temperature other than T1°C may be obtained by correcting the capacity degradation model DM1 using a predetermined ratio for the temperature of the battery 1.

[0024] The acquisition step S102 shown in FIG. 3 is a step of acquiring predetermined information about the battery 1. The acquisition step S102 can be implemented by the acquisition unit 42 (see FIG. 1) of the control device 40. The acquisition unit 42 acquires information including voltage, current, and temperature values ​​acquired by the sensor 30 (see FIG. 1). As described above, the sensor 30 detects each value at predetermined time intervals and transmits the detected values. In this embodiment, the charge / discharge time measurement unit 43 (see FIG. 1) of the control device 40 acquires the charge / discharge time CDt. The charge / discharge time measurement unit 43 measures the time during which charging or discharging is performed in the battery system 100. In this embodiment, as shown in FIG. 2, the battery 1 discharges from time t1 to time t2. Therefore, the charge / discharge time measurement unit 43 measures the time from time t1 to time t2 (t2-t1). In this embodiment, the time from time t1 to time t2 is the charge / discharge time CDt. The charge / discharge time measurement unit 43 transmits the charge / discharge time CDt to the acquisition unit 42 (see FIG. 1), and the acquisition unit 42 acquires the charge / discharge time CDt. In this embodiment, the initial full charge capacity Ho of the battery 1 is also acquired. The full charge capacity Ho may be stored in advance in the control device 40, for example.

[0025] The calculation step S103 shown in FIG. 3 is a step of calculating a change amount ΔSOC2 in the SOC of the battery 1 based on the information of the battery 1 acquired by the acquisition unit 42 in the acquisition step S102. The change amount ΔSOC2 is the amount of change in SOC before and after the ignition is turned on. The calculation step S103 can be implemented by the calculation unit 45. In this embodiment, the calculation unit 45 calculates the change amount of SOC from time t1 to t2 (region A2) shown in FIG. 2 as the change amount ΔSOC2. The calculation unit 45 first acquires the voltage value at time t1 from among the voltage values ​​acquired by the acquisition unit 42. In this embodiment, since the ignition has been in the off state for a period before time t1, the voltage at time t1 can be considered to be an open circuit voltage (OCV). The voltage value at time t1 is designated as voltage V1. The acquisition unit 42 also acquires the voltage value at time t3. Between times t2 and t3, the ignition remains in the OFF state, so the voltage value at time t3 can be considered to be the open-circuit voltage OCV. The voltage value at time t3 is designated as voltage V2. If the voltage value detected by voltage sensor 31 is a closed-circuit voltage, the open-circuit voltage may be estimated using the closed-circuit voltage and a conventionally known voltage behavior model.

[0026] The SOC1 and SOC2 (see FIG. 2) of the battery 1 are estimated based on the voltages V1 and V2. SOC1 is the SOC of the battery 1 at time t1. SOC2 is the SOC of the battery 1 at time t3. In this embodiment, the SOC1 and SOC2 of the battery 1 are estimated using an OCV-SOC conversion table (see FIG. 5) stored in advance in the control device 40. The OCV-SOC conversion table may be obtained in advance by testing, simulation, theoretical calculation, etc., and stored in the control device 40.

[0027] FIG. 5 is a graph showing the relationship between the open-circuit voltage OCV and the SOC. In FIG. 5, the relationship between the open-circuit voltage OCV and the SOC is shown graphically. Note that FIG. 5 shows the relationship between the open-circuit voltage OCV and the SOC only in a schematic manner and does not necessarily reflect the actual relationship. In FIG. 5, the open-circuit voltage OCV after charging is shown by a solid line, and the open-circuit voltage OCV after discharging is shown by a dashed line. As shown in FIG. 5, in the OCV-SOC conversion table, the open-circuit voltage OCV is recorded in association with the SOC. In the OCV-SOC conversion table shown in FIG. 5, the relationship between the open-circuit voltage OCV and the SOC after charging differs from the relationship between the open-circuit voltage OCV and the SOC after discharging. The relationship between the open-circuit voltage OCV and the SOC used to estimate the SOC may be appropriately selected depending on whether the acquired current value is a current value acquired during charging or a current value acquired during discharging. Note that the estimation of the SOC of the battery 1 is not limited to this form, and a conventionally known method, such as the IV method, in which the open-circuit voltage is determined from a plot of the current value and the CCV, may also be used. The method for estimating the SOC may be determined depending on the usage pattern of the battery 1. Once the SOC1 and SOC2 are estimated from the voltages V1 and V2 and the OCV-SOC conversion table, the calculation unit 45 calculates the amount of change ΔSOC2. The amount of change ΔSOC2 is calculated as the difference between SOC1 and SOC2.

[0028] The first estimation step S104 shown in FIG. 3 is a step of estimating the full charge capacity of the battery 1 based on a capacity degradation model DM1 in which a relationship between predetermined information about the battery 1 and the amount of capacity degradation of the battery 1 is pre-recorded, and the predetermined information about the battery 1 acquired in the acquisition step S102. The first estimation step S104 can be implemented by a first estimation unit 46 (see FIG. 1). In this embodiment, the full charge capacity of the battery 1 is estimated using, in addition to the information acquired by the acquisition unit 42, the charge / discharge time CDt measured by the charge / discharge time measurement unit 43 (see FIG. 1) and the initial full charge capacity Ho of the battery 1. The first estimation unit 46 selects the ΔSOC that matches the change ΔSOC1 from among the data DT1 to DT3 of the capacity degradation model DM1 shown in FIG. 4. For example, if the change ΔSOC1 is 80%, the change ΔSOC1 matches the ΔSOC in the data DT1. At this time, the first estimating unit 46 acquires the capacity deterioration rate for the value of the charge / discharge time CDt acquired by the acquiring unit 42 from the data DT1. As shown in FIG. 4, in the data DT1, the capacity deterioration rate Rd corresponds to the value of the charge / discharge time CDt. The first estimating unit 46 estimates the full charge capacity of the battery 1 by multiplying the initial full charge capacity Ho of the battery 1 by the capacity deterioration rate Rd. The full charge capacity estimated by the first estimating unit 46 is set as an estimated value H1. Note that, of the ΔSOCs contained in the data DT1 to DT3, the one closest to the change amount ΔSOC1 may be selected.

[0029] The second estimation step S105 shown in Fig. 3 is a step of estimating the full charge capacity of the battery 1 based on a discharge current integrated value ΣA1, which is an integrated value of the discharge current of the battery 1, and an amount of change ΔSOC2 in the SOC of the battery 1. The second estimation step S105 can be realized by the second estimation unit 47 (see Fig. 1). In this embodiment, the second estimation unit 47 estimates the full charge capacity of the battery 1 based on the following equation (1): H2 = (ΣA1 / ΔSOC2) × 100 (1) An estimated value H2 of the full charge capacity of the battery 1 is obtained based on the above. Here, the integrated discharge current value ΣA1 is a value obtained by integrating the current values ​​from time t1 to t2 among the current values ​​acquired by the current sensor 33 (see FIG. 1). In calculating equation (1), the integrated discharge current value ΣA1 is also calculated by the second estimation unit 47.

[0030] The weighted averaging step S106 is a step of estimating the full charge capacity of the battery 1 by multiplying the estimated value H1 of the full charge capacity of the battery 1 estimated in the first estimation step S104 by a predetermined weighting coefficient, and adding and averaging the result of multiplying the full charge capacity of the battery 1 estimated in the second estimation step by a predetermined weighting coefficient. The weighted averaging step S106 can be implemented by the weighted averaging unit 48 (see FIG. 1). In this embodiment, the weighted averaging unit 48 performs calculations using the estimated values ​​H1 and H2 and a weighting coefficient W. Details of the weighting coefficient W will be described later. If the estimated value of the full charge capacity calculated by the weighted averaging unit 48 is Hx, the weighted averaging unit 48 calculates Hx by the following equation (2): Hx = H1 × (1 - W) + H2 × W (2) The estimated value Hx is calculated based on the above. In this embodiment, the estimated value H2 is multiplied by the weighting coefficient W and the estimated value H1 is multiplied by (1-W), but this is not limiting. The weighting coefficient W may also be multiplied by the estimated value H1. In this way, the estimated value Hx is calculated.

[0031] The weighting coefficient W is calculated from the reliability Re shown in Fig. 6. Fig. 6 is a diagram showing a sheet S1 showing the relationship between the reliability Re and the weighting coefficient W. The reliability Re is an index showing how reliable the estimated value H2 is. As shown in Fig. 6, the value of the weighting coefficient W corresponds to the range of the numerical value of the reliability Re. The sheet S1 is assumed to be stored in advance in the control device 40, for example.

[0032] Here, the reliability Re will be explained. The reliability Re is calculated using the following formula (3): Re=(W1×W2×W3×W4×W5×W6×W7)×100 (3) The coefficients W1 to W7 are calculated based on the above. Each coefficient W1 to W7 is set to a value between 0 and 1. The coefficients W1 to W7 are determined by the coefficient determination unit 49 of the control device 40 (see FIG. 1). Since each coefficient W1 to W7 is a value between 0 and 1, the reliability Re can take a value between 0% and 100%. However, the reliability Re is not limited to being expressed as a percentage. The reliability Re may, for example, be expressed as a ratio using a value between 0 and 1. As shown in FIG. 6, in this embodiment, a weighting coefficient W is associated with the numerical value of the reliability Re in increments of 10%. Each coefficient W1 to W7 is assigned a possible value depending on the value of each parameter. FIG. 7A is a diagram showing coefficients W1 to W4 for each parameter. FIG. 7B is a diagram showing coefficients W5 to W7 for each parameter. Note that the graphs shown in FIGS. 7A and 7B are merely examples, and the numerical values ​​of the coefficients W1 to W7 are not limited to these. Furthermore, each coefficient W1 to W7 may be determined in the form of a table in which the coefficient corresponding to each parameter value is determined. The coefficients W1 to W7 will be explained below.

[0033] FIG. 7A(a) is a graph showing the relationship between the temperature of the battery 1 and the coefficient W1. Here, the temperature of the battery 1 refers to the temperature of the battery 1 at the time of full charge capacity estimation. In this embodiment, it refers to the temperature of the battery 1 at time t3 (see FIG. 2). The temperature of the battery 1 is detected by the temperature sensor 32 (see FIG. 1) and acquired by the acquisition unit 42 (see FIG. 1). As shown in FIG. 7A(a), the value of the coefficient W1 increases in proportion to the temperature of the battery 1. In this embodiment, when the temperature of the battery 1 is 20°C or higher, the value of the coefficient W1 is constant at 1. The coefficient determination unit 49 (see FIG. 1) determines the value of the coefficient W1 from the temperature of the battery 1 acquired by the acquisition unit 42 and the graph of FIG. 7A(a).

[0034] FIG. 7A(b) is a graph showing the relationship between the amount of change in SOC and the coefficient W2. The amount of change in SOC in FIG. 7A(b) is synonymous with the amount of change ΔSOC2 calculated by the calculation unit 45 (see FIG. 1) in the calculation step S103 (see FIG. 3). In this embodiment, as shown in FIG. 7A(b), the greater the absolute value of the amount of change in SOC, the greater the value of the coefficient W2. More specifically, when the amount of change in SOC is less than −10%, the smaller the amount of change in SOC, the greater the coefficient W2. Furthermore, when the amount of change in SOC is greater than 10%, the greater the amount of change in SOC, the greater the coefficient W2. When the amount of change in SOC is −40% or less or 40% or more, the value of the coefficient W2 is constant at 1. When the amount of change in SOC is between −10% and 10%, the value of the coefficient W2 is 0. The coefficient determination unit 49 (see FIG. 1) determines the value of the coefficient W2 from the value of the change amount ΔSOC2 calculated by the calculation unit 45 and the graph of FIG. 7A(b). Note that the shape of the graph shown in FIG. 7A(b) is not limited to this. The relationship between the change amount of SOC and the coefficient W2 may be such that, for example, the smaller the value of the change amount of SOC, the larger the value of the coefficient W2.

[0035] FIG. 7A(c) is a graph showing the relationship between the charge / discharge time and the coefficient W3. As shown in FIG. 7A(c), the longer the charge / discharge time, the smaller the coefficient W3. When the charge / discharge time is 300 seconds or more, the value of the coefficient W3 is constant at 0. In this embodiment, the charge / discharge time CDt is measured by the charge / discharge time measurement unit 43 (see FIG. 1). The charge / discharge time CDt is acquired by the acquisition unit 42 in the acquisition step S102. The coefficient determination unit 49 (see FIG. 1) determines the coefficient W3 from the charge / discharge time CDt acquired by the acquisition unit 42 and the graph of FIG. 7A(c).

[0036] FIG. 7A(d) is a graph showing the relationship between the OCV-SOC before the start of charging / discharging and the coefficient W4. The OCV-SOC before the start of charging / discharging is an SOC estimated from the OCV acquired before the start of charging / discharging of the battery 1. In this embodiment, the OCV acquired before the start of charging / discharging is the OCV at time t1 (see FIG. 2). The SOC estimated from the OCV at time t1 is SOC1 (see FIG. 2). The OCV-SOC at time t1 is acquired by the calculation unit 45 in the calculation step S103. As shown in FIG. 7A(d), in this embodiment, the coefficient W4 increases as the OCV-SOC at time t1 increases. The coefficient determination unit 49 (see FIG. 1) determines the coefficient W4 from the OCV-SOC before the start of charging (SOC1) and the graph of FIG. 7A(d).

[0037] FIG. 7B(e) is a graph showing the relationship between the OCV-SOC at the end of charging and discharging and the coefficient W5. The OCV-SOC at the end of charging and discharging is the SOC estimated from the OCV acquired at the end of charging and discharging of the battery 1. In this embodiment, the OCV acquired at the end of charging and discharging of the battery 1 is the OCV at time t2 (see FIG. 2). The SOC estimated from the OCV at time t2 is SOC2 (see FIG. 2). The OCV-SOC at time t2 is acquired by the calculation unit 45 in the calculation step S103. As shown in FIG. 7B(e), in this embodiment, the coefficient W5 increases as the OCV-SOC at time t2 increases. The coefficient determination unit 49 (see FIG. 1) determines the coefficient W5 from the OCV-SOC (SOC2) at the end of charging and discharging and the graph of FIG. 7B(e).

[0038] FIG. 7B(f) is a graph showing the relationship between the amount of change in the integrated current value and the coefficient W6. The amount of change in the integrated current value is the amount of change in the integrated current value when the battery 1 is charged or discharged. In this embodiment, the amount of change in the integrated current value is expressed as a ratio to the integrated discharge current value when the battery 1 is fully charged (SOC 100%). The integrated discharge current value when the battery 1 is fully charged is assumed to be stored in advance in the control device 40. In this embodiment, the integrated discharge current value ΣA1 when the battery 1 is charged or discharged is calculated in the second estimation step S105. As shown in FIG. 7B(f), in this embodiment, when the amount of change in the integrated current is less than 10%, the value of the coefficient W6 is 0, and when the amount of change in the integrated current is 10% or more, the value of the coefficient W6 is 1. The coefficient determination unit 49 (see FIG. 1) determines the coefficient W6 from the discharge current integrated value ΣA1 calculated in the second estimation step S105, the discharge current integrated value when the battery 1 is in a fully charged state, and the graph of FIG. 7B(f).

[0039] Fig. 7B(g) is a graph showing the relationship between the current full charge capacity value and the coefficient W7. Here, the current full charge capacity value is the estimated value H2 estimated by the second estimating unit 47. As shown in Fig. 7B(g), in this embodiment, when the estimated value H2 is equal to or greater than 1 and equal to or less than 5, the value of the coefficient W7 is 1. When the estimated value H2 is less than 1 or greater than 5, the value of the coefficient W7 is 0. The coefficient determining unit 49 (see Fig. 1) determines the coefficient W7 from the estimated value H2 estimated by the second estimating unit 47 and the graph of Fig. 7B(g).

[0040] The weighted average unit 48 (see FIG. 1) calculates the reliability Re based on equation (3). The coefficients W1 to W7 in equation (3) are determined by the coefficient determination unit 49, as described above. The weighted average unit 48 references sheet S1 (see FIG. 6) and acquires the weighting coefficient W corresponding to the calculated reliability Re. After acquiring the weighting coefficient W, the weighted average unit 48 calculates the estimated value Hx of the full charge capacity based on equation (2). Note that if the reliability Re cannot be calculated, for example, if a numerical value required to calculate the reliability Re cannot be acquired due to a malfunction of the sensor 30 (see FIG. 1), the weighting coefficient W is set to 0. In this case, according to equation (2), the estimated value H2 is not used in calculating the estimated value Hx, and the estimated value H1 becomes equal to the estimated value Hx.

[0041] When estimating the full charge capacity of a battery, if the change in the state of charge is relatively small, the error in detecting the voltage value, etc., becomes relatively large compared to the change in the state of charge. In this case, the estimated value of the full charge capacity of the battery calculated using the change in the state of charge becomes relatively low in accuracy. Therefore, by estimating the full charge capacity only when the change in the state of charge is relatively large, a relatively accurate estimation result can be obtained. However, according to the knowledge of the present inventors, there may be cases where the change in the state of charge is so great that the full charge capacity estimation is not performed for a relatively long period of time. For example, this may occur when the electric vehicle equipped with the battery is driven for a relatively short period of time. In such cases, because the full charge capacity estimation is not performed for a relatively long period of time, there is a possibility that the last estimated full charge capacity of the battery may differ from the actual full charge capacity of the battery.

[0042] According to the method for estimating the full charge capacity of the battery 1 of this embodiment, in the battery system 100 managed by the control unit 20 (battery management system), the first estimator 46 estimates the estimated value H1 using the value acquired by the acquisition unit 42 and the capacity degradation model DM1. The capacity degradation model DM1 is a pre-recorded model of the amount of capacity degradation of the battery 1. Therefore, the estimated value H1 can be acquired regardless of the magnitude of the change amount ΔSOC1. In this embodiment, the estimation by the first estimator 46 is performed at time t3, that is, when the ignition of the electric vehicle equipped with the battery system 100 is turned on. Therefore, the estimation by the first estimator 46 is performed relatively frequently. This prevents the full charge capacity from being estimated for a relatively long time. This prevents the estimated value H1 from deviating from the actual full charge capacity of the battery 1.

[0043] According to the method for estimating the full charge capacity of the battery 1 of this embodiment, in the second estimation step S105, the second estimation unit 47 estimates the full charge capacity. The estimated value H2 of the full charge capacity is calculated using equation (1) based on the integrated discharge current value ΣA1 and the change amount ΔSOC2. Then, in the weighted averaging step S106, the estimated values ​​H1 and H2 are weighted and averaged as shown in equation (2). Here, the estimated value H2 is a value obtained by estimating the full charge capacity using the change amount ΔSOC2. When the change amount ΔSOC2 is relatively small, the estimation accuracy of the estimated value H2 becomes relatively low. On the other hand, because the estimated value H1 is based on the capacity degradation model DM1, the estimation accuracy of the estimated value H1 does not depend on the change amount ΔSOC1. Therefore, by weighting the estimated value H1, whose estimation accuracy does not depend on the magnitude of the change amount ΔSOC1, and the estimated value H2, whose estimation accuracy depends on the change amount ΔSOC2, to estimate the estimated value Hx, the estimation accuracy of the estimated value Hx can be made relatively high.

[0044] According to the method for estimating the full charge capacity of the battery 1 of this embodiment, in the weighted averaging step S106, the larger the absolute value of the change amount ΔSOC2 estimated in the second estimation step S105, the larger the value of the coefficient W2. Therefore, the larger the absolute value of the change amount ΔSOC2, the larger the weighting of the estimated value H2. The estimated value H2 is an estimated value that includes a value acquired by the sensor 30. Therefore, the estimated value H2 is a value estimated based on the actual measurement value of the information related to the battery 1. Therefore, when the absolute value of the change amount ΔSOC2 is relatively large, the weighting of the estimated value H2 based on the actual measurement value of the battery 1 can be increased. In other words, the estimation accuracy of the estimated value Hx can be relatively high.

[0045] The above-described embodiment is merely an example of the full charge capacity estimation of a battery disclosed herein, and the technology disclosed herein can be embodied in various other forms.

[0046] In the above-described embodiment, the capacity degradation model DM1 represents the relationship between charge / discharge time and capacity degradation at a predetermined change amount ΔSOC1, but is not limited to this. FIG. 8 is a diagram showing an example of a capacity degradation model DM2 representing the relationship between storage time and capacity degradation at a predetermined temperature. The vertical axis of the capacity degradation model DM2 is the same as that of the capacity degradation model DM1 (see FIG. 4). The horizontal axis of the capacity degradation model DM2 represents the storage time when the battery 1 is stored at a predetermined temperature. That is, the storage time is included as predetermined information in the capacity degradation model DM2. The capacity degradation model DM2 includes data DT4, DT5, and DT6. The data DT4, DT5, and DT6 represent data when the battery 1 is stored at an SOC of 80%, 50%, and 20%, respectively. However, the SOC values ​​in the capacity degradation model DM2 are not limited to these. Furthermore, the number of data included in the capacity degradation model DM2 is not limited to three. Furthermore, the capacity degradation model DM1 represents data when the temperature of the battery 1 is at temperature T1°C. As in the above-described embodiment, the storage unit 41 may store a capacity deterioration model for the battery 1 at a temperature other than temperature T1° C.

[0047] When the capacity degradation model DM2 is used, for example, the acquisition unit 42 (see FIG. 1) acquires the unused time, which is the time the battery 1 is left unused without being charged or discharged. The unused time is, for example, the time from time t2 to t3 (area A3) shown in FIG. 2. The first estimation unit 46 (see FIG. 1) acquires the capacity degradation rate using the unused time acquired by the acquisition unit 42 and the capacity degradation model DM2. Note that the capacity degradation model can be created not only from the capacity degradation models DM1 and DM2 but also from various other information related to the battery 1.

[0048] In the above-described embodiment, in the sheet S1 shown in Fig. 6, the weighting coefficient W is associated with the numerical value of the reliability Re in increments of 10%, but this is not limited to this. Fig. 9 is a diagram showing a sheet S2 according to another embodiment, which shows the relationship between the reliability Re and the weighting coefficient W. As shown in Fig. 9, the weighting coefficient W may be 1 when the reliability Re is 50% or more, and 0 when the reliability Re is 50% or more. That is, according to formula (2), when the reliability Re is 50% or more, the estimated value Hx may be equal to the estimated value H2, and when the reliability Re is less than 50%, the estimated value Hx may be equal to the estimated value H1.

[0049] The full charge capacity estimation program 40a of this embodiment may be stored in, for example, a non-transitory computer readable medium. The program may also be supplied to a computer through such a non-transitory computer readable medium. Examples of non-transitory computer readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), CD-ROMs (Read Only Memory), etc.

[0050] As described above, this specification includes the disclosures set forth in the following sections.

[0051] Section 1: A method for estimating a full charge capacity of a battery managed by a battery management system mounted on an electric vehicle, comprising: an acquiring step of acquiring predetermined information about the battery; A method for estimating the full charge capacity of a battery, comprising: a first estimation step of estimating the full charge capacity of the battery based on a capacity degradation model in which a relationship between predetermined information about the battery and the amount of capacity degradation of the battery is recorded in advance, and the predetermined information about the battery acquired by the acquisition step.

[0052] Section 2: a second estimation step of estimating a full charge capacity of the battery based on an integrated discharge current value, which is an integrated value of a discharge current of the battery, and an amount of change in SOC of the battery; Item 2. The method for estimating a full charge capacity of a battery according to item 1, further comprising a weighted averaging step of estimating a full charge capacity of the battery by multiplying the full charge capacity of the battery estimated in the first estimation step by a predetermined weighting coefficient and adding and averaging the full charge capacity of the battery estimated in the second estimation step by a predetermined weighting coefficient.

[0053] Section 3: Item 3. The method for estimating a full charge capacity of a battery according to Item 2, wherein the weighted averaging step increases the weight of the weighting coefficient of the estimated value estimated by the second estimation step as the absolute value of the change in the SOC of the battery in the second estimation step increases.

[0054] Section 4: A device for estimating a full charge capacity of a battery managed by a battery management system mounted on an electric vehicle, comprising: A sensor, a control device; The sensor A voltage sensor; A temperature sensor; a current sensor; Equipped with The control device a storage unit that stores a capacity degradation model relating to predetermined information about the battery and an amount of capacity degradation of the battery; an acquisition unit that acquires information including a voltage value, a current value, and a temperature value of the battery acquired by the sensor; a first estimation unit that estimates a full charge capacity of the battery based on the information acquired by the acquisition unit and the capacity degradation model; A battery full charge capacity estimation device comprising:

[0055] Section 5: The control device a second estimation unit that estimates a full charge capacity of the battery based on an integrated discharge current value that is an integrated value of a discharge current of the battery and an amount of change in SOC of the battery; a weighted average unit that estimates the full charge capacity of the battery by adding and averaging a value obtained by multiplying the full charge capacity of the battery estimated by the first estimation unit by a predetermined weighting coefficient and a value obtained by multiplying the full charge capacity of the battery estimated by the second estimation unit by a predetermined weighting coefficient; Item 5. The battery full charge capacity estimation device according to item 4, comprising:

[0056] Item 6: Item 6. The battery full charge capacity estimation device according to item 5, wherein the weighted average unit increases the weight of the weighting coefficient of the estimated value estimated by the second estimation unit as the absolute value of the change in SOC of the battery in the second estimation unit increases.

[0057] Section 7: A battery full charge capacity estimation program managed by a battery management system mounted on an electric vehicle, a storage unit that stores a capacity degradation model relating to predetermined information about the battery and an amount of capacity degradation of the battery; an acquisition unit that acquires information including a voltage value, a current value, and a temperature value of the battery acquired by a sensor; a first estimation unit that estimates a full charge capacity of the battery based on the information acquired by the acquisition unit and the capacity degradation model; A battery full charge capacity estimation program configured to cause a computer to realize the above.

[0058] Section 8: a second estimation unit that estimates a full charge capacity of the battery based on an integrated discharge current value that is an integrated value of a discharge current of the battery and an amount of change in SOC of the battery; Item 8. The battery full charge capacity estimation program according to Item 7, further configured to cause a computer to implement a weighted averaging unit that estimates the full charge capacity of the battery by adding and averaging the estimated value estimated by the first estimation unit multiplied by a predetermined weighting coefficient and the estimated value estimated by the second estimation unit multiplied by a predetermined weighting coefficient.

[0059] Section 9: Item 9. The battery full charge capacity estimation program according to Item 8, wherein the weighted average unit increases the weight of the weighting coefficient of the estimated value estimated by the second estimation unit as the absolute value of the change in SOC of the battery in the second estimation unit increases. [Explanation of symbols]

[0060] 1 battery 20 Control unit (battery management system) 30 sensors 31 Voltage sensor 32 Temperature Sensor 33 Current Sensor 40 Control device 40a Full charge capacity estimation program 41 Storage section 42 Acquisition Department 43 Charge / discharge time measurement unit 44 Charge / discharge time acquisition section 45 Calculation section 46 1st estimation part 47 Second estimation part 48 Weighted average part 49 Coefficient determination unit 100 Battery System A1,A2,A3,A4 area CDt charge / discharge time DM1, DM2 capacity degradation model DT1~DT6 data GP graph H1,H2 estimates Ho Full charge capacity Hx estimate Rd capacity deterioration rate Reliability S1 Seat S101 Memory process S102 Acquisition process S103 Calculation process S104 1st estimation process S105 Second estimation process S106 Weighted average process S2 Seat T1 temperature V1, V2 voltage W weighting factor W1~W7 coefficients t1~t3 hours Changes in ΔSOC1 and ΔSOC2 ΣA1 Discharge current integrated value

Claims

1. A method for estimating a full charge capacity of a battery managed by a battery management system mounted on an electric vehicle, comprising: an acquiring step of acquiring predetermined information about the battery; a first estimation step of estimating the full charge capacity of the battery based on a capacity degradation model in which a relationship between predetermined information about the battery and an amount of capacity degradation of the battery is recorded in advance, and the predetermined information about the battery acquired by the acquisition step.

2. a second estimation step of estimating a full charge capacity of the battery based on an integrated discharge current value, which is an integrated value of a discharge current of the battery, and an amount of change in SOC of the battery; 2. The method for estimating a full charge capacity of a battery according to claim 1, further comprising a weighted averaging step of estimating the full charge capacity of the battery by multiplying the full charge capacity of the battery estimated in the first estimation step by a predetermined weighting coefficient and adding and averaging the full charge capacity of the battery estimated in the second estimation step by a predetermined weighting coefficient.

3. 3. The method for estimating a full charge capacity of a battery according to claim 2, wherein the weighted averaging step increases the weight of the weighting coefficient of the estimated value estimated by the second estimation step as the absolute value of the change in the SOC of the battery in the second estimation step increases.

4. A device for estimating a full charge capacity of a battery managed by a battery management system mounted on an electric vehicle, comprising: A sensor, a control device; The sensor A voltage sensor; A temperature sensor; a current sensor; Equipped with The control device a storage unit that stores a capacity degradation model relating to predetermined information about the battery and an amount of capacity degradation of the battery; an acquisition unit that acquires information including a voltage value, a current value, and a temperature value of the battery acquired by the sensor; a first estimation unit that estimates a full charge capacity of the battery based on the information acquired by the acquisition unit and the capacity degradation model; A battery full charge capacity estimation device comprising:

5. The control device a second estimation unit that estimates a full charge capacity of the battery based on an integrated discharge current value that is an integrated value of a discharge current of the battery and an amount of change in SOC of the battery; a weighted average unit that estimates the full charge capacity of the battery by adding and averaging a value obtained by multiplying the full charge capacity of the battery estimated by the first estimation unit by a predetermined weighting coefficient and a value obtained by multiplying the full charge capacity of the battery estimated by the second estimation unit by a predetermined weighting coefficient; The battery full charge capacity estimation device according to claim 4, further comprising:

6. 6. The battery full charge capacity estimation device according to claim 5, wherein the weighted average unit increases the weight of the weighting coefficient of the estimated value estimated by the second estimation unit as the absolute value of the change in the SOC of the battery in the second estimation unit increases.

7. A battery full charge capacity estimation program managed by a battery management system mounted on an electric vehicle, a storage unit that stores a capacity degradation model relating to predetermined information about the battery and an amount of capacity degradation of the battery; an acquisition unit that acquires information including a voltage value, a current value, and a temperature value of the battery acquired by a sensor; a first estimation unit that estimates a full charge capacity of the battery based on the information acquired by the acquisition unit and the capacity degradation model; A battery full charge capacity estimation program configured to cause a computer to realize the above.

8. a second estimation unit that estimates a full charge capacity of the battery based on an integrated discharge current value that is an integrated value of a discharge current of the battery and an amount of change in SOC of the battery; 8. The battery full charge capacity estimation program according to claim 7, further configured to cause a computer to realize a weighted averaging unit that estimates the full charge capacity of the battery by adding and averaging the estimated value estimated by the first estimation unit multiplied by a predetermined weighting coefficient and the estimated value estimated by the second estimation unit multiplied by a predetermined weighting coefficient.

9. 9. The battery full charge capacity estimation program according to claim 8, wherein the weighted average unit increases the weight of the weighting coefficient of the estimated value estimated by the second estimation unit as the absolute value of the change in the SOC of the battery in the second estimation unit increases.

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