Battery degradation state estimation device

Abstract

To provide a battery degradation state estimating device capable of estimating the degradation state of a battery at discretionary timing and improving the accuracy of estimation.SOLUTION: Provided is a battery degradation state estimating device that estimates a degradation state of a battery for traveling that is mounted in a vehicle. The degradation state estimating device comprises: a detection unit for detecting a plurality of state quantities including first, second and third state quantities that represent a use state of the battery; a storage unit for storing a plurality of map data indicating degradation degrees of the battery that corresponds to the plurality of state quantities; and an estimation unit for estimating a degradation change quantity of the battery. The plurality of map data includes first map data in which degradation degrees corresponding to the first and second state quantities of the battery are indicated and second map data in which degradation degrees corresponding to the second and third state quantities of the battery are indicated. The estimation unit estimates the degradation change quantity of the battery on the basis of the plurality of map data and a plurality of weighting coefficients.SELECTED DRAWING: Figure 3A

Description

Technical Field

[0001] The present invention relates to a battery degradation state estimation device that estimates the degradation state of a battery for driving.

Background Art

[0002] Patent Document 1 discloses a device that calculates a life consumption coefficient of a battery for driving by using a plurality of map data. The plurality of map data includes map data showing the relationship between the battery temperature at the time of stopping and the life coefficient, and map data showing the relationship between the battery current and the life coefficient. Further, the plurality of map data includes map data showing the relationship between the battery temperature during driving and the life coefficient, and map data showing the relationship between ΔSOC (State of Charge) and the life coefficient.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The amount of battery degradation (SOH: State of Health) can be easily measured, for example, by charging the battery until it is fully charged. On the other hand, it may be useful to be able to estimate the degradation state of the battery at any timing, such as before charging after the battery has been used. However, there is room for improvement in the estimation accuracy in the conventional estimation methods such as those shown in Patent Document 1.

[0005] An object of the present invention is to provide a battery degradation state estimation device that can estimate the degradation state of a battery at any timing and can improve the estimation accuracy.

Means for Solving the Problems

[0006] (1) One aspect of the present invention Battery degradation state estimation device is a battery deterioration state estimation device for estimating the deterioration state of a driving battery mounted on a vehicle, a detection unit that detects a plurality of state quantities including a first state quantity, a second state quantity, and a third state quantity representing the usage state of the battery, a storage unit that stores a plurality of map data indicating the degree of deterioration of the battery corresponding to the plurality of state quantities, an estimation unit that estimates the deterioration change amount of the battery, and includes the plurality of map data includes first map data indicating the degree of deterioration corresponding to the first state quantity and the second state quantity of the battery, and second map data indicating the degree of deterioration corresponding to the second state quantity and the third state quantity of the battery, the estimation unit estimates the deterioration change amount of the battery based on the plurality of state quantities repeatedly detected by the detection unit, the plurality of map data, Based on this, for each of the individual map data, an integrated value of the degree of degradation extracted from the individual map data is calculated, The plurality of integrated values calculated corresponding to each of the plurality of map data, and a plurality of weighting coefficients that respectively weight the degrees of deterioration of the plurality of map data and Furthermore, the estimation unit Obtains a measured value of the amount of degradation of the battery during the charging process of the battery, Based on the fact that the measured value has been obtained, based on the difference between the measured value obtained in the past and the measured value obtained this time, and the plurality of integrated values, a plurality of weighting coefficients corresponding to the difference and the plurality of integrated values are calculated as coefficient solutions, When estimating the amount of degradation change, based on the coefficient solution calculated in the past, the plurality of weighting coefficients used for estimating the amount of degradation change are estimated to do. (2) Another aspect of the battery degradation state estimation device of the present invention is A battery degradation state estimation device for estimating the degradation state of a driving battery mounted on a vehicle, A detection unit that detects a plurality of state quantities including a first state quantity, a second state quantity, and a third state quantity representing the usage state of the battery, A storage unit that stores a plurality of map data indicating the degree of degradation of the battery corresponding to the plurality of state quantities, An estimation unit that estimates the amount of degradation change of the battery, comprising The plurality of map data includes first map data indicating the degree of degradation corresponding to the first state quantity and the second state quantity of the battery, second map data indicating the degree of degradation corresponding to the second state quantity and the third state quantity of the battery, and third map data indicating the degree of degradation corresponding to the third state quantity and the first state quantity of the battery, The estimation unit estimates the amount of degradation change of the battery based on the plurality of state quantities repeatedly detected by the detection unit, the plurality of map data, and a plurality of weighting coefficients that respectively weight the degree of degradation of the plurality of map data, The plurality of weighting coefficients include a first coefficient that weights the degree of degradation of the first map data, a second coefficient that weights the degree of degradation of the second map data, and a third coefficient that weights the degree of degradation of the third map data, The first state quantity, the second state quantity, and the third state quantity indicate, in any order, the current of the battery, the voltage or SOC of the battery, and the temperature of the battery.

Advantages of the Invention

[0007] According to the deterioration state estimation device according to the present invention, by estimating the amount of deterioration change of the battery by the estimation unit, the amount of deterioration change of the battery can be estimated even after the battery is used and before the charging process. Here, the deterioration of the battery progresses according to a plurality of state quantities representing the usage state of the battery. However, each state quantity does not independently affect the amount of deterioration, and the degree of influence on the amount of deterioration varies depending on the combination of the plurality of state quantities. Therefore, the deterioration state estimation device according to the present invention has first map data indicating the degree of deterioration corresponding to the first state quantity and the second state quantity of the battery, and second map data indicating the degree of deterioration corresponding to the second state quantity and the third state quantity of the battery. Then, the estimation unit estimates the amount of deterioration change using a plurality of map data including the first map data and the second map data. Therefore, it is possible to estimate the amount of deterioration change reflecting the degree of deterioration caused by the combination of the first state quantity and the second state quantity based on the first map data and the degree of deterioration caused by the combination of the second state quantity and the third state quantity based on the second map data. Therefore, the estimation accuracy of the deterioration state of the battery is improved.

[0008] Furthermore, the estimation unit estimates the amount of deterioration change of the battery using a plurality of weighting coefficients for weighting the degrees of deterioration of the plurality of map data. Therefore, the degrees of deterioration of the plurality of map data can be standardized among the individual map data, and the weights of the degrees of deterioration between the plurality of map data can be adjusted by the weighting coefficients. Therefore, it becomes easy to create individual map data.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 3C

Figure 4

Figure 5

Figure 6

Figure 7

Mode for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram showing an example of a vehicle equipped with a battery deterioration state estimation device according to an embodiment of the present invention. FIG. 2 is a block diagram showing the deterioration state estimation device.

[0011] The vehicle 1 in FIG. 1 is, for example, an EV (Electric Vehicle), and includes drive wheels 2, an electric motor 3 that generates driving power output to the drive wheels 2, and a driving battery 4 that stores electric power supplied to the electric motor 3. Further, the vehicle 1 includes an inverter 5 that converts and transmits electric power between the battery 4 and the electric motor 3, a driving operation unit 6 that receives a driving operation by a driver, and a driving control unit 7 that receives an operation signal of the driving operation unit 6 and controls the inverter 5. Further, the vehicle 1 includes a display panel 8 that outputs information to the user and inputs information from the user, and a controller 9 for a user interface that controls the exchange of information with the user via the display panel 8.

[0012] Furthermore, the vehicle 1 includes a detection unit 21 that detects a plurality of state quantities representing the usage state of the battery 4, a battery management unit 22 that manages the battery 4, and an arithmetic device 10 that estimates the deterioration state of the battery 4. The detection unit 21 and the arithmetic device 10 correspond to an example of the deterioration state estimation device 100 of the present embodiment.

[0013] The battery 4 is, for example, a lithium-ion secondary battery, a nickel-metal hydride secondary battery, etc., but as long as it can store the power for running by charging, the type is not particularly limited.

[0014] The inverter 5 drives the electric motor 3 in power running and regenerative running according to the running situation and the driving operation. Typically, when the inverter 5 drives the electric motor 3 in power running, the battery 4 discharges, and when the inverter 5 drives the electric motor 3 in regenerative running, a regenerative current is sent to the battery 4 and the battery 4 is charged. The current value of the battery 4 is represented by a positive value during discharge and a negative value during charging.

[0015] The detection unit 21 constantly and repeatedly detects a plurality of state quantities representing the usage state of the battery 4. The plurality of state quantities include the temperature of the battery 4, the voltage of the battery 4, and the current of the battery 4, and the detection unit 21 includes a temperature sensor 21a, a voltage sensor 21b, and a current sensor 21c. The detection values of the respective state quantities detected by the detection unit 21 are sent to the arithmetic device 10 via the battery management unit 22. The temperature, voltage, and current of the battery 4 correspond to an example of the first state quantity, the second state quantity, and the third state quantity according to the present invention in any order (regardless of order).

[0016] The voltage of the battery 4 detected by the voltage sensor 21b is, in this embodiment, the voltage of a representative one of the plurality of battery cells constituting the battery 4. However, the voltage of the battery 4 detected by the voltage sensor 21b may be the voltage of each of the plurality of battery cells, or may be the total voltage of the battery 4.

[0017] The battery management unit 22 constantly calculates the SOC of the battery 4 during use of the battery 4 by integrating the current value of the battery 4. Data on the initial capacity of the battery 4 is stored in the battery management unit 22. When the battery 4 is being charged, the battery management unit 22 measures the SOH of the battery 4 based on the SOC of the battery 4 at the full charge voltage and the above-mentioned initial capacity.

[0018] The vehicle 1 further includes a charging port 31 for taking in electric power from outside the vehicle 1, and an in-vehicle charger 32 that charges the battery 4 using the electric power taken in from the charging port 31. A charging cable for guiding electric power from a power source outside the vehicle 1 can be connected to the charging port 31. Charging the battery 4 via the charging port 31 is called "plug-in charging".

[0019] As shown in FIG. 1, the arithmetic unit 10 is an ECU (Electronic Control Unit) including a CPU (Central Processing Unit) 10a that performs calculation processing, a RAM (Random Access Memory) 10b in which the CPU 10a expands data, a ROM (Read Only Memory) 10c that stores a control program executed by the CPU 10a, a non-volatile storage device 10d, and an interface 10e that exchanges signals between the CPU 10a and devices outside the arithmetic unit 10. The arithmetic unit 10 may be composed of one ECU, or may be composed of a plurality of ECUs that communicate with each other and cooperate to operate.

[0020] As shown in FIG. 2, the arithmetic unit 10 includes a storage unit 12 that stores a plurality of map data 12a to 12c showing the relationship between a plurality of state quantities detected by the detection unit 21 and the degree of deterioration of the battery 4, and an estimation unit 13 that estimates the deterioration change amount of the battery 4. In FIG. 2, the internal structure of the arithmetic unit 10 is represented by functional blocks. The estimation unit 13 is a functional module realized by the CPU 10a executing a control program. The storage unit 12 is composed of an area of the ROM 10c and / or an area of the storage device 10d. The storage unit 12 further includes a storage area 12d for storing log data for estimating the deterioration change amount of the battery 4. The deterioration change amount of the battery 4 means the difference between the deterioration amount (SOH) of the battery 4 at a certain timing and the deterioration amount of the battery 4 at another certain timing. The deterioration amount at a certain timing means the ratio of the fully charged capacity of the initial battery 4 to the fully charged capacity of the battery 4 at that timing. Hereinafter, the deterioration change amount is also denoted as "ΔSOH".

[0021] <Map Data> Figs. 3A to 3C respectively show examples of the IV map data, TI map data, and TV map data stored in the storage unit. Among the plurality of map data stored in the storage unit 12, there are included IV map data 12a indicating the degree of deterioration according to the voltage and current of the battery 4, and TI map data 12b indicating the degree of deterioration according to the current and temperature of the battery 4. Further, among the plurality of map data stored in the storage unit 12, there is included TV map data 12c indicating the degree of deterioration according to the voltage and temperature of the battery 4. Any two of the three map data 12a to 12c correspond to an example of the first map data and the second map data according to the present invention, and the remaining one corresponds to an example of the third map data according to the present invention. In the present embodiment, the case of having three map data 12a to 12c will be described. However, the arithmetic device 10 may have only two of the above map data, or other map data may be added.

[0022] As shown in Fig. 3A, in the IV map data 12a, a voltage value is associated with each row, a current value is associated with each column, and the degree of deterioration of the battery 4 when a voltage value and a current value associated with the row and column of the relevant column occur is shown in each cell. In Fig. 3A, the current values associated with each column of the IV map data 12a indicate single-point values such as, for example, "0A" and "100A", but these mean intervals of current values having a width such as "-25A or more and less than 25A" and "25A or more and less than 150A". Similarly, the voltage values associated with each row of the IV map data 12a mean intervals having a width. In Fig. 3A, the IV map data 12a is data of 5 rows and 5 columns, but it may have more rows and columns and data in which each interval of current and each interval of voltage are more subdivided.

[0023] The degree of degradation shown in the IV map data 12a represents the degree of degradation per unit time. The degree of degradation only needs to relatively show the magnitude relationship within the IV map data 12a, and its value may be normalized. The normalization can be any normalization, such as normalization where the maximum value and the minimum value become respective predetermined values, or normalization where the average value of the degrees of degradation in all columns becomes a predetermined value.

[0024] The IV map data 12a can be created, for example, by obtaining the amount of degradation per unit time through tests or simulations under various usage conditions with different currents and voltages. Note that the amount of degradation of the battery 4 is also affected by other state quantities (such as temperature) in addition to current and voltage. Therefore, when creating the IV map data 12a, for other state quantities, one representative value can be selected for testing or simulation, and the degree of degradation of the IV map data 12a can be determined from the results of the test or simulation. Alternatively, multiple representative values can be selected for other state quantities, tests or simulations can be performed with each representative value, and the degree of degradation of the IV map data 12a can be determined from the statistical quantity (such as the average value) of the results.

[0025] In the example of FIG. 3A, the degree of degradation is low when both the voltage and the current are at intermediate values (3.7V, 0A), and the degree of degradation increases as it moves away from the intermediate value in both the positive and negative directions. However, this is a simplified example. Depending on the structure or characteristics of the battery 4, etc., the IV map data 12a may include a gradient of the degree of degradation different from that in FIG. 3A, and there may also be a case where the degree of degradation sharply increases with a specific combination of voltage value and current value.

[0026] The TI map data 12b in FIG. 3B and the TV map data 12c in FIG. 3C are the same as the IV map data 12a except that the state quantities of interest are exchanged for temperature and current, or temperature and voltage.

[0027] <Processing of the estimation unit> FIG. 4 is a time chart for explaining the processing of the estimation unit. The estimation unit 13 performs processing for counting the integrated values A to C of the degree of deterioration, processing for obtaining coefficient solutions of the weighting coefficients a to c of the degree of deterioration, and estimation processing of ΔSOH of the battery 4.

[0028] The counting process of the integrated values A to C is repeatedly executed constantly at a short cycle. The process of obtaining the coefficient solutions of the weighting coefficients a to c is executed when the amount of deterioration of the battery 4 is measured, such as when the plug-in charging is completed. The ΔSOH estimation process is executed based on an estimation request at an arbitrary timing. Subsequently, these processes will be described in detail.

[0029] <Counting Process of Integrated Values A to C> FIG. 5 is a data chart for explaining the counting process of the integrated values A to C executed by the estimation unit. The integrated values A to C are values obtained by integrating the degrees of deterioration extracted from the map data 12a to 12c. The integration of the degree of deterioration is executed for each of the individual map data 12a to 12c as follows.

[0030] That is, the detection unit 21 constantly detects a plurality of state quantities of the battery 4 every predetermined sampling period (for example, 0.1 ms to 100 ms, etc.), and sends the detection values of the plurality of state quantities to the estimation unit 13 via the battery management unit 22. When receiving the detection values of the plurality of state quantities, the estimation unit 13 extracts the degree of deterioration corresponding to the plurality of state quantities from the plurality of map data 12a to 12c. Then, the extracted degree of deterioration is added to the integrated values A to C calculated in the previous cycle. Through such processing, a value obtained by integrating the degree of deterioration of the IV map data 12a is stored in the storage area of the integrated value A. Similarly, a value obtained by integrating the degree of deterioration of the TI map data 12b is stored in the storage area of the integrated value B, and a value obtained by integrating the degree of deterioration of the TV map data 12c is stored in the storage area of the integrated value C.

[0031] The data chart in FIG. 5 is a chart arranging data of a plurality of processing cycles related to the counting process of integrated values A to C in time series. One processing cycle corresponds to one sampling period of the detection unit 21. In the specific example of FIG. 5, for example, at the cycle number “01800102”, the detection unit 21 detects a current value = -50 [A], a voltage value = 3.7 [V], and a temperature = -40 [° C]. Referring to the IV map data 12a, the TI map data 12b, and the TV map data 12c in FIGS. 3A to 3C, the degrees of deterioration corresponding to these detected values are “1”, “2”, and “2” respectively, and the estimation unit 13 extracts these degrees of deterioration. Then, the estimation unit 13 adds the extracted degrees of deterioration to the integrated value A “02722103”, the integrated value B “02250124”, and the integrated value C “02920164” at the previous cycle number. As a result, the updated integrated value A “02722104”, the integrated value B “02250126”, and the integrated value C “02920166” are counted. Such counting processes of the integrated values A to C are continuously executed at all times.

[0032] Note that the integrated values A to C may be configured such that the values are reset based on the occurrence or end of a predetermined event, and the integrated values A to C immediately before the reset are recorded together with the event information.

[0033] Since the values of the degrees of deterioration shown in the map data 12a to 12c are standardized as described above, the amount of deterioration of the battery 4 cannot be represented only by the values of the degrees of deterioration. Therefore, by multiplying the values of the degrees of deterioration respectively shown in the plurality of map data 12a to 12c by the weighting coefficients a to c, the values after multiplication are converted into values corresponding to the amount of deterioration of the battery 4, and the degrees of deterioration are made comparable between the different map data 12a to 12c. The weighting coefficients a to c correspond to an example of the first coefficient, the second coefficient, and the third coefficient according to the present invention in an arbitrary order (order is not limited).

[0034] Therefore, the estimation unit 13 multiplies the integrated values A to C of the degradation degrees of the plurality of map data 12a to 12c by the weighting coefficients a to c, respectively, and performs data processing such that the sum value approximates the ΔSOH of the battery 4. That is, when using the three map data 12a to 12c, the estimation unit 13 performs data processing assuming that the following equation (1) holds. ΔSOH = A·a + B·b + C·c ··· (1) Here, ΔSOH indicates the ΔSOH from an arbitrary first timing to an arbitrary second timing, and the integrated values A to C indicate the integrated values of the degradation degrees from the above first timing to the above second timing.

[0035] When using more map data, ΔSOH may be expressed by the following relational expression. That is, in this case, the estimation unit 13 multiplies the integrated value of the degradation degree of each map data by the weighting coefficient corresponding to the degradation degree of each map data, and uses an equation in which the sum of the multiplied values for all map data is equal to the ΔSOH of the battery 4.

[0036] The estimation unit 13 constantly counts the integrated values A to C. Therefore, if the values of the weighting coefficients a to c are determined, the estimation unit 13 can obtain an estimated value of the ΔSOH of the battery 4 using equation (1).

[0037] On the other hand, the degradation degrees shown in the plurality of map data 12a to 12c may gradually change the weights acting on the degradation amount of the battery 4 depending on the usage method of the battery 4 up to that point or the aging of the battery 4. Therefore, the estimation unit 13 estimates and sets the weighting coefficients a to c so as to conform to the state of the battery 4, rather than setting the weighting coefficients a to c as fixed values.

[0038] <Process for obtaining the coefficient solutions of the weighting coefficients a to c> In vehicle 1, the amount of degradation (SOH) of battery 4 is measured by plug-in charging. Therefore, ΔSOH can be calculated from the measured value of the amount of degradation at the previous plug-in charging and the measured value of the amount of degradation this time. Furthermore, the estimation unit 13 constantly counts the integrated values A to C. Therefore, the estimation unit 13 obtains an equation with the weighting coefficients a to c as unknowns by applying the calculated ΔSOH and the counted integrated values A to C to Equation (1). Each time the amount of degradation of battery 4 is measured by plug-in charging, the estimation unit 13 acquires the above equation and records it.

[0039] FIG. 6 shows a data chart for explaining the data processing executed by the estimation unit 13 for each plug-in charging. The data chart is a chart in which the data handled by the estimation unit 13 at the completion of each plug-in charging is arranged in time series. "k" of the integrated values A to C means "×1000", and values less than three digits of the integrated values A to C are shown after rounding down. "n" of the coefficient solution means "×10 -9 ", and values less than three digits of the coefficient solution are shown after rounding down.

[0040] As described above, the amount of degradation of battery 4 is measured at the completion of each plug-in charging. The estimation unit 13 stores the measured value of the amount of degradation each time the measurement is performed. Then, at the completion of the plug-in charging, the estimation unit 13 calculates the difference between the measured value of the amount of degradation at the previous time and the measured value of the amount of degradation this time, and obtains ΔSOH of battery 4 from the previous time to this time. Then, the estimation unit 13 obtains one equation with the weighting coefficients a to c as unknowns by applying the integrated values A to C counted from the previous time to this time and the above ΔSOH to Equation (1). The equations in each row of FIG. 6 are obtained by applying the ΔSOH and the integrated values A to C in the same row to Equation (1).

[0041] Furthermore, upon completion of each plugin charging, the estimation unit 13 calculates solutions of the weighting coefficients a to c (hereinafter referred to as coefficient solutions) using the obtained equation. The coefficient solutions may be obtained, for example, by numerical calculation. That is, the estimation unit 13 may allocate various numerical values to each of the weighting coefficients a to c in small increments and search for a combination of values that best satisfies the equation. The range of values that the weighting coefficients a to c can take is predetermined, for example, as "0 < a < 1, 0 < b < 1, 0 < c < 1". When the coefficient solutions cannot be determined by only one equation, the estimation unit 13 may obtain the coefficient solutions of the weighting coefficients a to c by the same numerical calculation using a plurality of equations close to that point in time.

[0042] Note that the method for obtaining the coefficient solutions of the weighting coefficients a to c is not limited to the above method, and various methods may be applicable. For example, a method using a machine-learned prediction model may be applicable. In this case, in a plurality of vehicles having the battery 4 of the same structure, the same map data 12a to 12c is used, and a measurement value of the deterioration amount of the battery 4, an equation of the weighting coefficients a to c, and a dataset of the coefficient solutions obtained as described above are collected. Then, the collected dataset is given to the prediction model as teacher data for machine learning. The prediction model is a model that outputs the coefficient solutions of the weighting coefficients a to c that match these by inputting the deterioration amount of the battery 4 or the deterioration amount of the battery 4 and each parameter of the above equation. Then, the estimation unit 13 may be configured to input the measurement value of the deterioration amount obtained at the completion of the plugin charging or the measurement value and each parameter of the above equation to the above prediction model, and obtain the coefficient solutions of the weighting coefficients a to c from the prediction model. The prediction model may be provided in the arithmetic unit 10 or may be provided in a predetermined server computer separated from the vehicle 1. When the prediction model is provided in the server computer, the estimation unit 13 may communicate with the prediction model to obtain the coefficient solutions of the weighting coefficients a to c.

[0043] Once the estimation unit 13 obtains the coefficient solutions of the weighting coefficients a to c, it records the coefficient solutions in the storage area 12d. The estimation unit 13 may simultaneously store the deterioration amount measurement value in the storage area 12d. The above recording may be performed in association with the measurement value of the deterioration amount at that time, event information indicating the number of plugin charging completions, date and time information, and the like.

[0044] Note that even when the battery 4 is charged to less than full charge, for example, SOC 80%, the battery management unit 22 may be able to estimate the deterioration amount (SOH) of the battery 4 from the relationship between the charge amount and the charge voltage. Therefore, the number of plugin chargings in FIG. 6 may include the number of plugin chargings other than full charge. Also, the battery management unit 22 may be able to measure the deterioration amount (SOH) of the battery 4 even when the battery 4 is charged by regenerative power. Therefore, the number of plugin chargings in FIG. 6 is changed to the number of times the deterioration amount of the battery 4 is measured, and the number may include the time when the deterioration amount of the battery 4 is measured by charging with regenerative power.

[0045] <Estimation Process of ΔSOH of Battery> The estimation unit 13 constantly counts the integrated values A to C. Therefore, once the values of the weighting coefficients a to c are determined, the estimation unit 13 can obtain the estimated value of ΔSOH of the battery 4 using Equation (1). On the other hand, the coefficient solutions obtained from the above equations include errors. Therefore, the estimation unit 13 does not estimate ΔSOH using the coefficient solutions of the weighting coefficients a to c obtained last, but first estimates the current appropriate weighting coefficients a to c from a plurality of sets of coefficient solutions obtained in the past. Then, the estimation unit 13 estimates ΔSOH of the battery 4 from the estimated weighting coefficients a to c and the integrated values A to C.

[0046] The estimation of the weighting coefficients a to c is performed as follows. That is, a plurality of sets of coefficient solutions obtained at different timings should be distributed in the vicinity of the appropriate weighting coefficients a to c. Therefore, the estimation unit 13 calculates a predetermined statistic of each weighting coefficient a to c from a plurality of sets of coefficient solutions respectively obtained at the completion of plug-in charging for a plurality of times in the past close to the current time, and uses that value as the estimated value of the weighting coefficients a to c. The above-mentioned predetermined statistic may be any quantity that represents a value close to the true value from a plurality of values including errors. For example, the mode can be applied. Also, an average value or the like may be applied to the above-mentioned predetermined statistic. One weighting coefficient a may be calculated using only the coefficient solution of one weighting coefficient a among the plurality of sets of coefficient solutions, or may be calculated using the coefficient solutions of all the weighting coefficients a to c. The same applies to the weighting coefficient b and the weighting coefficient c.

[0047] Note that the estimation unit 13 may estimate the weighting coefficients a to c by the same calculation as above using not only a plurality of sets of coefficient solutions obtained by the own vehicle 1 (the vehicle 1 equipped with the estimation unit 13), but also a plurality of sets of coefficient solutions obtained by other vehicles having the battery 4 of the same structure. When using the coefficient solutions obtained by other vehicles, the coefficient solutions obtained by the other vehicle may be used when the degree of deterioration of the battery of the other vehicle is equivalent to the degree of deterioration of the battery 4 of the own vehicle 1. By such a selection, it is possible to perform appropriate estimation of the weighting coefficients a to c by excluding the influence of changes in the weighting coefficients a to c over a long period. When using the coefficient solutions obtained by other vehicles, each vehicle is configured to transmit the coefficient solution, the measured value of the deterioration amount of the battery 4 at that time, and information that can identify the structure of the battery 4 to a predetermined server computer each time the coefficient solution is obtained. Then, when the estimation unit 13 of a certain vehicle estimates the weighting coefficients a to c, the estimation unit 13 sends information on the deterioration amount of the battery 4 measured in the past close to the current time to the server computer via communication, and may download the coefficient solutions of other vehicles from the server computer. The downloaded coefficient solutions may be the coefficient solutions obtained when the deterioration amount of the battery 4 of the other vehicle is close to the transmitted deterioration amount.

[0048] When there is a ΔSOH estimation request for the battery 4, the estimation unit 13 estimates the weighting coefficients a to c as described above. Then, by applying the estimated values of the weighting coefficients a to c and the integrated values A to C counted during the period corresponding to the ΔSOH estimation request to Equation (1), the ΔSOH of the battery 4 for that period is calculated. And the obtained value is used as the estimated value of ΔSOH (deterioration change amount) for that period.

[0049] <Method of Using the Estimated ΔSOH> The ΔSOH for an arbitrary period estimated as described above can be effectively used as follows. For example, when the vehicle 1 is used for car sharing or a rental car, the estimation unit 13 may estimate the ΔSOH for the period from the start of the rental to the end of the rental of the vehicle 1. By such estimation, even if plug-in charging is not performed at the time of returning the vehicle 1, the ΔSOH of the battery 4 that occurred during the rental of the vehicle 1 can be estimated, and the ΔSOH can be reflected in the rental fee.

[0050] Also, during the use of the vehicle 1, the estimation unit 13 may estimate the ΔSOH constantly or in response to a user operation or the like, and present (for example, display output) the current deterioration amount (SOH) of the battery 4 to the user. The current deterioration amount of the battery 4 can be obtained by adding the above-estimated ΔSOH to the deterioration amount measured in the past. By such estimation and presentation of information, the user can seamlessly or at a desired timing check the deterioration amount of the battery 4.

[0051] Further, the estimation unit 13 may present (for example, display and output) the estimated weighting coefficients a to c to the user. Since the degrees of deterioration of the plurality of map data 12a to 12c are standardized, the values of the weighting coefficients a to c can indicate which of the degrees of deterioration of the map data 12a to 12c has a great influence on the deterioration amount of the battery 4. That is, if the weighting coefficient a is larger than the weighting coefficients b and c, the user can grasp that the deviation from the standard values of the current and voltage greatly affects the deterioration of the battery 4. Also, if the weighting coefficient b is larger than the weighting coefficients a and c, the user can expect that the deviation from the standard values of the temperature and current greatly affects the deterioration of the battery 4. Further, if the weighting coefficients a and c are large and the weighting coefficient b is small, the user can expect that the deviation of the voltage, which is a common parameter of the map data 12a and 12c, from the standard value greatly affects the deterioration of the battery 4. Then, based on the above prediction, the user can adopt a usage method (for example, maintaining the charging rate within a certain range so that the voltage is close to the standard value) to suppress the deterioration of the battery 4 and can suppress the deterioration of the battery 4.

[0052] <Control Process of Estimation Unit> Subsequently, the control process of the estimation unit 13 that realizes the above-described estimation of ΔSOH will be described. FIG. 7 is a flowchart showing an example of the control process executed by the estimation unit. The estimation unit 13 constantly repeats the control process of FIG. 7. When the control process is started, first, the estimation unit 13 receives the detected values of a plurality of state quantities (current, voltage, temperature) representing the usage state of the battery 4 from the battery management unit 22 (step S1). The detected values are the values detected by the detection unit 21.

[0053] Upon receiving the detected values of the plurality of state quantities, the estimation unit 13 collates the detected values of the state quantities with the plurality of map data 12a to 12c and extracts the corresponding degree of deterioration of the battery 4 (step S2). Then, the degrees of deterioration extracted are respectively added to the above-described integrated values A to C (see FIG. 5), and the integrated values A to C are updated (step S3).

[0054] Next, the estimation unit 13 determines whether a predetermined event has occurred (step S4). If the result is NO, the process returns to step S1. That is, during the period when step S4 is NO, the processes of steps S1 to S3 are repeatedly executed at a predetermined control cycle (for example, 0.1 ms to 100 ms).

[0055] On the other hand, if the determination result in step S4 is YES, the integrated values A to C at that time together with the information indicating the event are recorded as a log in the storage area 12d (step S5). Here, plug-in charging is adopted as a predetermined event. Note that the recording of the information indicating the event and the integrated values A to C may be performed not only at the time of occurrence of the event but also at the time of completion of the event. Further, the estimation unit 13 may record the information indicating the event and the integrated values A to C at the time of occurrence and completion of other events. As other events, vehicle rental, etc. can be applied. In this case, the information indicating the event and the integrated values A to C may be recorded at the time of notification of the start of rental and at the time of notification of the end of rental. Alternatively, the estimation unit 13 may record the integrated values A to C updated every predetermined control cycle or every predetermined time as a log in the storage area 12d. The integrated values A to C may be reset after the recording in step S5.

[0056] Thereafter, the estimation unit 13 determines whether a predetermined event has been completed (step S6). If not, the determination in step S6 is repeated. If completed, the process proceeds to the next step. Note that even during the period when the determination in step S6 is repeatedly executed, the processes of steps S1 to S3 may be repeatedly executed at a predetermined control cycle.

[0057] If the determination result in step S6 is YES, the estimation unit 13 receives the measured value of the degradation amount of the battery 4 from the battery management unit 22 and records it as a log in the storage area 12d (step S7). If the plug-in charging is completed with a full charge, the battery management unit 22 obtains the ratio of the charging capacity at the full charge to the initial full charge capacity of the battery 4 as the measured value of the degradation amount. Further, even when the plug-in charging is completed with less than a full charge, the battery management unit 22 estimates the full charge capacity and the degradation amount of the battery 4 based on the relationship between the charge amount in the plug-in charging and the voltage of the battery 4, and obtains the estimated degradation amount as the measured value.

[0058] Furthermore, the estimation unit 13 obtains an equation (see FIG. 6) for obtaining the weighting coefficients a to c based on the measured value of the degradation amount received in step S7 and the integrated values A to C, and records it as a log in the storage area 12d (step S8). Specifically, the estimation unit 13 obtains the above equation based on the measured value of the degradation amount, the integrated values A to C at that time, the measured value of the degradation amount recorded at the completion of the previous plug-in charging, and the difference between the integrated values A to C at that time.

[0059] Furthermore, the estimation unit 13 calculates the coefficient solutions of the weighting coefficients a to c using the equation in step S8 or a plurality of equations from the ones recorded in the past that are closer to the current time (step S9). In step S9, the estimation unit 13, for example, within the possible range such as "0 < a < 1, 0 < b < 1, 0 < c < 1", assigns several numerical values to each of the weighting coefficients a to c in small increments, and may search for the combination of values that best satisfies the equation through numerical processing. Once the coefficient solutions are obtained, the estimation unit 13 records the coefficient solutions in the storage area 12d together with the recording of the above events (step S10).

[0060] Next, the estimation unit 13 determines whether there is a ΔSOH estimation request for the battery 4 (step S11). The estimation request is not particularly limited. For example, it is sent from the controller 9 responsible for the user interface of the vehicle 1 to the arithmetic unit 10. The estimation request is made by specifying the ΔSOH from any start timing to any end timing. The end timing may be fixed at the current time.

[0061] As a result of the determination in step S11, if the answer is NO, the estimation unit 13 ends the control process for one control cycle. On the other hand, if the answer is YES, the estimation unit 13 estimates the ΔSOH of the battery 4 from the start timing indicated in the estimation request to the current time or from the start timing indicated in the estimation request to the end timing (step S12). In step S12, the estimation unit 13 extracts a plurality of coefficient solutions close to the current time from the logarithms of the coefficient solutions of the weighting coefficients a to c, and adopts the most frequent value as the estimated values of the weighting coefficients a to c. Then, by applying the integrated values A to C from the start timing to the end timing specified in the estimation request and the above-mentioned estimated weighting coefficients a to c to the above formula (1), the estimation unit 13 calculates the ΔSOH of the battery 4.

[0062] When the ΔSOH is estimated, the estimation unit 13 transmits the calculation result to the requesting controller 9 (step S13). The controller may output the received estimated value of ΔSOH to the display panel 8 of the vehicle 1. Then, one control process ends.

[0063] In the flowchart of FIG. 7, under the condition that a predetermined event is completed (YES in step S6), the estimation unit 13 executes step S11 for determining the ΔSOH estimation request. However, the estimation unit 13 may receive a ΔSOH estimation request at another timing and execute the processes of steps S12 and S13. For example, the estimation unit 13 may be configured to repeatedly execute the processes of steps S11 to S13 in a process different from the processes of steps S1 to S11 and in parallel with the said processes.

[0064] In the control process of FIG. 7, by repeatedly executing steps S1 to S3, the counting process of the above-described integrated values A to C is realized. Further, by executing steps S7 to S10, the equations and coefficient solutions of the weighting coefficients a to c at the time of measuring the deterioration amount are obtained.

[0065] The program of the above-described control process is stored in a non-transitory computer-readable medium such as the ROM 10c of the arithmetic unit 10. The arithmetic unit 10 may be configured to read a program stored in a portable non-transitory recording medium and execute the program. The above portable non-transitory storage medium may store the program of the above-described control process.

[0066] As described above, according to the deterioration state estimation device 100 of the present embodiment, the estimation unit 13 estimates the ΔSOH of the battery based on the plurality of state amounts of the battery 4 repeatedly detected by the detection unit 21, the plurality of map data 12a to 12c, and the plurality of weighting coefficients a to c. The deterioration of the battery 4 progresses according to a plurality of state amounts (for example, current, voltage, temperature) representing the usage state of the battery 4. However, each state amount does not independently affect the deterioration amount, and the degree of influence on the deterioration amount varies depending on the combination of the plurality of state amounts. Therefore, the deterioration state estimation device 100 of the present embodiment includes, in the plurality of map data 12a to 12c, map data indicating the degree of deterioration according to the first state amount and the second state amount, and map data indicating the degree of deterioration according to the second state amount and the third state amount. With such map data, it is possible to obtain the degree of deterioration reflecting the deterioration exerted on the battery 4 by the combination of the first state amount and the second state amount and the deterioration exerted on the battery 4 by the combination of the second state amount and the third state amount. Therefore, the estimation accuracy of the ΔSOH of the battery 4 can be improved.

[0067] Furthermore, according to the degradation state estimation device 100 of the present embodiment, the estimation unit 13 obtains the degrees of degradation extracted from the plurality of map data 12a to 12c based on the detection results of the plurality of state quantities of the battery 4 by the detection unit 21, and counts the integrated values A to C. Then, based on the integrated values A to C and the weighting coefficients a to c, the ΔSOH of the battery 4 is estimated. In this way, by integrating the degrees of degradation of the map data 12a to 12c based on the detection by the detection unit 21, the integrated values A to C reflect the degrees of degradation reflecting the usage state of the battery 4 at each time point. Therefore, the estimation accuracy of the ΔSOH of the battery 4 can be further improved.

[0068] Furthermore, according to the degradation state estimation device 100 of the present embodiment, each time a measured value of the degradation amount of the battery 4 is obtained, the estimation unit 13 calculates the coefficient solutions of the weighting coefficients a to c from the equations of the weighting coefficients a to c obtained based on the integrated values A to C and the measured value of the degradation amount. Furthermore, the estimation unit 13 estimates the weighting coefficients a to c used for the estimation of ΔSOH based on the coefficient solutions calculated in the past. Even when a plurality of factors of the battery 4 (for example, combinations of current, voltage, and temperature) affect the degradation amount of the battery, the weights of the degrees to which the plurality of factors affect the degradation amount may gradually change. Therefore, in the present embodiment, by estimating the weighting coefficients a to c as described above, it is possible to appropriately set the weighting coefficients a to c in response to the case where the weights of the degrees to which the plurality of factors affect the degradation amount gradually change. Therefore, the estimation accuracy of the ΔSOH of the battery 4 can be further improved.

[0069] Furthermore, according to the degradation state estimation device 100 of the present embodiment, based on the coefficient solutions of the plurality of sets of weighting coefficients a to c calculated in the past, the weighting coefficients a to c used for the estimation of ΔSOH are estimated. Alternatively, in addition to the coefficient solutions of the weighting coefficients a to c calculated in other vehicles, the weighting coefficients a to c used for the estimation of ΔSOH are estimated. Therefore, even if the coefficient solutions calculated each time include a relatively large error, it is possible to calculate the weighting coefficients a to c with a small error, and thus the estimation accuracy of the ΔSOH of the battery 4 can be further improved.

[0070] Furthermore, according to the degradation state estimation device 100 of the present embodiment, the plurality of map data 12a to 12c includes map data indicating the degree of degradation corresponding to the third state quantity and the first state quantity. Therefore, it is possible to perform the estimation process of ΔSOH that reflects the influence of the degree of degradation exerted on the battery 4 by the combination of the third state quantity and the first state quantity. Therefore, the estimation accuracy of ΔSOH of the battery 4 can be further improved.

[0071] The embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments. For example, in the above embodiment, as a plurality of state quantities indicating the usage state of the battery 4, the current, voltage, and temperature of the battery 4 are shown as an example, but it is not limited to this example. For example, the SOC of the battery 4 may be added to the plurality of state quantities, or the voltage and SOC may be replaced. Further, in the above embodiment, an example is shown in which the plurality of map data is the IV map data 12a, the TI map data 12b, and the TV map data 12c, but the plurality of map data is not limited to this. For example, map data indicating the degree of degradation corresponding to one state quantity may be added to the plurality of map data, or map data indicating the degree of degradation corresponding to three or more state quantities may be added. Also, among the map data 12a to 12c, any one of them may be omitted, and the plurality of map data may include only two types of map data indicating the degree of degradation corresponding to two state quantities. In addition, the details shown in the embodiments can be appropriately changed without departing from the spirit of the invention.

Explanation of Signs

[0072] 1 Vehicle 2 Driving Wheels 3 Electric Motor 4 Battery 5 Inverter 8 Display Panel 9 Controller 10 Arithmetic Unit 12 Storage Unit 12a IV Map Data 12b TI Map Data 12c TV Map Data 12d Storage Area 13 Estimation unit 21 Detection unit 21a Temperature sensor 21b Voltage sensor 21c Current sensor 22 Battery management unit 31 Charging port 32 On-vehicle charger 100 Deterioration state estimation device

Claims

1. A battery degradation state estimation device for estimating the degradation state of a driving battery mounted on a vehicle, comprising: a detection unit that detects a plurality of state quantities including a first state quantity, a second state quantity, and a third state quantity representing the usage state of the battery; a storage unit that stores a plurality of map data indicating the degree of degradation of the battery corresponding to the plurality of state quantities; an estimation unit that estimates the amount of change in battery degradation; and comprising: The plurality of map data includes first map data indicating the degree of degradation corresponding to the first state quantity and the second state quantity of the battery, and second map data indicating the degree of degradation corresponding to the second state quantity and the third state quantity of the battery; The estimation unit: Based on the plurality of state quantities repeatedly detected by the detection unit and the plurality of map data, for each of the individual map data, calculates an integrated value of the degree of degradation extracted from the individual map data; Estimates the amount of change in battery degradation based on the plurality of integrated values calculated corresponding to the plurality of map data respectively and a plurality of weighting coefficients that weight the degree of degradation of the plurality of map data respectively; Furthermore, the estimation unit: Obtains a measured value of the amount of battery degradation during the charging process of the battery; Based on the acquisition of the measured value, calculates, as a coefficient solution, a plurality of weighting coefficients corresponding to the difference and the plurality of integrated values based on the difference between the previously acquired measured value and the currently acquired measured value and the plurality of integrated values; When estimating the amount of change in degradation, based on the coefficient solution calculated in the past, estimates the plurality of weighting coefficients used for estimating the amount of change in degradation. A battery degradation state estimation device characterized by this.

2. A battery degradation state estimation device for estimating the degradation state of a driving battery mounted on a vehicle, comprising: a detection unit that detects a plurality of state quantities including a first state quantity, a second state quantity, and a third state quantity representing the usage state of the battery; a storage unit that stores a plurality of map data indicating the degree of degradation of the battery corresponding to the plurality of state quantities; an estimation unit that estimates the amount of change in battery degradation; and comprising: The plurality of map data includes first map data indicating the degree of degradation corresponding to the first state quantity and the second state quantity of the battery, second map data indicating the degree of degradation corresponding to the second state quantity and the third state quantity of the battery, and third map data indicating the degree of degradation corresponding to the third state quantity and the first state quantity of the battery. The estimation unit estimates the amount of deterioration change of the battery based on the plurality of state quantities repeatedly detected by the detection unit, the plurality of map data, and a plurality of weighting coefficients that respectively weight the degrees of deterioration of the plurality of map data. The plurality of weighting coefficients include a first coefficient that weights the degree of deterioration of the first map data, a second coefficient that weights the degree of deterioration of the second map data, and a third coefficient that weights the degree of deterioration of the third map data. The first state quantity, the second state quantity, and the third state quantity indicate, in any order, the current of the battery, the voltage or SOC of the battery, and the temperature of the battery, respectively. A battery deterioration state estimation device characterized by this.

3. The estimation unit Based on the plurality of map data and the plurality of state quantities repeatedly detected by the detection unit, for each of the individual map data, calculates an integrated value of the degree of deterioration extracted from the individual map data. The battery deterioration state estimation device according to claim 2, wherein the amount of deterioration change of the battery is estimated based on a plurality of integrated values calculated corresponding to the plurality of map data respectively and the plurality of weighting coefficients.

4. The estimation unit Obtains a measured value of the amount of deterioration of the battery during the charging process of the battery. Based on the fact that the measured value has been obtained, calculates, as a coefficient solution, a plurality of weighting coefficients corresponding to the difference and the plurality of integrated values based on the difference between the measured value obtained in the past and the measured value obtained this time and the plurality of integrated values. And When estimating the amount of deterioration change, the plurality of weighting coefficients used for estimating the amount of deterioration change are estimated based on the coefficient solution calculated in the past. The battery deterioration state estimation device according to claim 3, characterized by this.

5. The estimation unit Estimates the plurality of weighting coefficients used for estimating the amount of deterioration change based on a plurality of sets of the coefficient solutions. The plurality of sets of coefficient solutions include any plurality of the coefficient solutions calculated for a plurality of times based on the acquisition of the measured values a plurality of times in the past, and the coefficient solutions calculated for a plurality of times based on the acquisition of the measured values a plurality of times in other vehicles. The battery deterioration state estimation device according to claim 1 or claim 4, characterized by this.

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