Battery state estimation system

Abstract

A battery state estimation system (101) comprises: an information acquisition unit (10) that acquires position information (A1) and date and time information (A2) during travel of an electric vehicle (1); a feature calculation unit (30) that calculates integrated current (D) and battery temperature degradation level (E), which are degradation features related to degradation of a battery (2) mounted in the electric vehicle (1), on the basis of the position information (A1) and the date and time information (A2) acquired by the information acquisition unit (10); and a battery degradation estimation unit (60) that estimates the degradation state of the battery (2) on the basis of the integrated current (D) and the battery temperature degradation level (E) calculated by the feature calculation unit (30).

Description

Battery State Estimation System CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on Japanese Application No. 2024-144344, filed on August 26, 2024, the contents of which are incorporated herein by reference.

[0002] The present disclosure relates to a battery state estimation system that estimates a battery state.

[0003] Patent Document 1 below discloses a technology for calculating the degree of battery degradation. This technology calculates the degree of battery degradation using battery information such as the battery current and temperature, and thereby attempts to estimate the state of battery degradation.

[0004] Patent No. 4042475

[0005] In the technology disclosed in Patent Document 1, it is necessary to acquire battery information to estimate the deterioration state of the battery. To acquire the battery information, it is necessary to remove the battery from the electric vehicle, which is time-consuming. Furthermore, it is difficult to acquire battery information using a device such as an aftermarket device that is retrofitted to the electric vehicle. Furthermore, it is necessary to estimate not only the deterioration state of the battery, but also the SOC, which is an index that indicates the battery's state of charge.

[0006] The present disclosure aims to provide a battery state estimation system that is highly convenient.

[0007] One aspect of the present disclosure is a battery state estimation system comprising: an information acquisition unit that acquires non-battery information while an electric vehicle is running; a feature calculation unit that calculates a degradation feature related to degradation of a battery mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and a battery degradation estimation unit that estimates a degradation state of the battery based on the degradation feature calculated by the feature calculation unit.

[0008] Another aspect of the present disclosure is a battery state estimation system including: an information acquisition unit that acquires non-battery information while an electric vehicle is running; a current estimation unit that estimates a current of a battery mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and an SOC estimation unit that estimates an SOC of the battery based on the current estimated by the current estimation unit.

[0009] In the battery state estimation system according to the above embodiment, non-battery information, such as the speed and acceleration of an electric vehicle, is acquired instead of general battery information such as the battery current and voltage. The non-battery information is essentially different from battery information in that it can be acquired relatively easily without removing the battery from the electric vehicle. Then, a deterioration feature related to battery deterioration is calculated based on the non-battery information, and the battery deterioration state is estimated based on the deterioration feature.

[0010] This battery state estimation system can estimate the state of deterioration of a battery based on non-battery information, which is easier to obtain than battery information. It also makes it possible to use equipment such as an add-on device that is retrofitted to an electric vehicle to obtain the non-battery information. Furthermore, because the system can estimate the state of deterioration of a battery by obtaining non-battery information in real time while the electric vehicle is traveling, it is easy to use and does not require procedures such as receiving input about a driving plan from a client before estimating the state of deterioration of the battery.

[0011] According to the battery state estimation system of the above-described another aspect, the current of the battery mounted on the electric vehicle can be estimated based on the non-battery information acquired by the information acquisition unit, and the SOC, which is one of the battery states, can be estimated based on the estimated current. This makes it possible to create an efficient charging plan for the battery based on the SOC estimation result.

[0012] Therefore, according to the above-described aspects, it is possible to provide a battery state estimation system that is highly convenient.

[0013] Note that the symbols in parentheses in the claims indicate the correspondence with the specific means described in the embodiments described below, and do not limit the technical scope of the present disclosure.

[0014] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. 1 is a block diagram showing the configuration of a battery state estimation system of a first embodiment, FIG. 2 is a flowchart of a battery state estimation process by the battery state estimation system of the first embodiment, FIG. 3 is a correlation map showing the correlation between the absolute value of acceleration and current by speed, FIG. 4 is a diagram for explaining a process of correcting an outside air temperature to a battery temperature based on an offset amount, FIG. 5 is a diagram showing a first setting pattern, a second setting pattern, and a third setting pattern of the offset amount, FIG. 6 is a diagram showing a fourth setting pattern of the offset amount, FIG. 7 is a frequency distribution diagram showing a frequency distribution of battery temperatures, FIG. 8 is a block diagram showing the configuration of a battery state estimation system of a second embodiment, FIG. 9 is a flowchart of a battery state estimation process by the battery state estimation system of the second embodiment, FIG. 10 is a block diagram showing the configuration of a battery state estimation system of a third embodiment, FIG. 11 is a flowchart of a battery state estimation process by the battery state estimation system of the third embodiment, and FIG. 12 is a diagram showing the configuration of a battery state estimation system of a fourth embodiment. 13 is a flowchart of the battery state estimation process by the battery state estimation system of the fourth embodiment, FIG. 14 is a frequency distribution diagram showing the frequency distribution of the absolute value of acceleration, FIG. 15 is a frequency distribution diagram showing the frequency distribution of the outside air temperature, FIG. 16 is a block diagram showing the configuration of the battery state estimation system of the fifth embodiment, FIG. 17 is a flowchart of the battery state estimation process by the battery state estimation system of the fifth embodiment, and FIG. 18 is a block diagram showing the configuration of the battery state estimation system of the sixth embodiment. 19 is a flowchart of the battery state estimation process by the battery state estimation system of the sixth embodiment, FIG. 20 is a block diagram showing the configuration of the battery state estimation system of the seventh embodiment, FIG. 21 is a flowchart of the battery state estimation process by the battery state estimation system of the seventh embodiment, FIG. 22 is a diagram for explaining another example of a method for calculating the gradient in the battery state estimation process of FIG. 21, FIG. 23 is a correlation diagram showing the correlation between the gradient and the travel distance for the electric vehicle when normal and when loaded, and FIG. 24 is a correlation diagram showing the correlation between the gradient and the travel distance for the electric vehicle when normal and when loaded,FIG. 26 is a flowchart of the battery state estimation process by the battery state estimation system of the eighth embodiment; FIG. 27 is a block diagram of the battery state estimation system of the ninth embodiment; FIG. 28 is a flowchart of the battery state estimation process by the battery state estimation system of the ninth embodiment; FIG. 29 is a diagram for explaining the complementation process for complementing the integrated current in the battery state estimation process of FIG. 28; FIG. 30 is a diagram for explaining the process for calculating the unobtained section when the purchase date of the electric vehicle is unknown in the complementation process of FIG. 29; FIG. 33 is a diagram for explaining the process of comparing the estimated value of SOH with a reference value in FIG. 32 , FIG. 34 is a block diagram showing the configuration of a battery state estimation system of the 11th embodiment, and FIG. 35 is a flowchart of the battery state estimation process of the battery state estimation system of the 11th embodiment.

[0015] The battery state estimation system according to the above-described embodiment will be described in detail below with reference to the drawings.

[0016] (First embodiment) 1. Configuration of electric vehicle 1 The electric vehicle 1 shown in FIG. 1 is a vehicle such as an electric car or a hybrid car. The electric vehicle 1 is equipped with a battery 2 for driving. The electric vehicle 1 is also equipped with a GPS receiver 3. In this embodiment, the GPS receiver 3 is an add-on device that is retrofitted to the electric vehicle 1. The GPS receiver 3 has the function of receiving signals from GPS satellites (artificial satellites) to measure its position and detect accurate date and time information. This function may be realized by a dedicated device, or may be realized by a car navigation system or a drive recorder that has a GPS function.

[0017] The battery 2 is a battery pack including multiple battery modules and a main battery management unit. The battery modules include a battery pack formed by combining multiple battery cells, and a satellite battery management unit. The battery cells are secondary batteries that can be recharged and reused. The main battery management unit stores battery information for the multiple battery modules.

[0018] The functions of the main battery management unit and the satellite battery management unit are executed by a processor. The term "processor" broadly encompasses computer components that perform tasks such as data calculations and conversions, program execution, and control of other devices. The processor may include a CPU (Central Processing Unit) that controls the entire computer, or an MPU (Micro Processing Unit) that integrates some of the CPU's functions.

[0019] 2. Configuration of Battery State Estimation System 101 As shown in FIG. 1 , the battery state estimation system 101 is applied to an electric vehicle 1. This battery state estimation system 101 is a system that estimates the battery state of a battery 2. The battery state includes not only the degradation state of the battery 2 but also the charge state of the battery 2. This battery state estimation system 101 includes, as its components, an information acquisition unit 10, an information storage unit 20, a feature calculation unit 30, a vehicle internal information acquisition unit 50, and a battery degradation estimation unit 60. These multiple components are mounted on a management system installed at a dealer, vehicle inspection shop, repair shop, appraisal shop, or the like for the electric vehicle 1. Furthermore, some of these multiple components may be provided on a data server or cloud external to the system.

[0020] 2-1. Configuration of the information acquisition unit 10 The information acquisition unit 10 has a function of acquiring non-battery information when the electric vehicle 1 is traveling. This information acquisition unit 10 includes a position information acquisition unit 11 and a date and time information acquisition unit 12. The position information acquisition unit 11 has a function of acquiring position information A1 from the GPS receiver 3 on the electric vehicle 1 side. The date and time information acquisition unit 12 has a function of acquiring date and time information A2 during traveling from the GPS receiver 3 on the electric vehicle 1 side. Both the position information A1 and the date and time information A2 are "non-battery information."

[0021] The term "non-battery information" used here refers to information that is essentially distinct from battery information, where battery information is information directly related to the battery 2, such as the current, voltage, and temperature of the battery 2. While battery information is generally obtained with the battery 2 removed from the electric vehicle 1, non-battery information can be obtained without removing the battery 2. Therefore, non-battery information is easier to obtain than battery information.

[0022] 2-2. Configuration of Information Storage Unit 20 The information storage unit 20 has a function of storing various pieces of information in the battery state estimation system 101 in a readable manner. The location information A1 and the date and time information A2 are stored in the information storage unit 20. Alternatively, the information storage unit 20 may be configured by a memory within the system, or may be configured by a data server or cloud external to the system.

[0023] 2-3. Configuration of feature amount calculation unit 30 The feature amount calculation unit 30 has a function of calculating degradation feature amounts related to degradation of the battery 2 based on the non-battery information acquired by the information acquisition unit 10. This feature amount calculation unit 30 includes a speed calculation unit 31, an acceleration calculation unit 32, a current estimation unit 33, an integrated current calculation unit 34, an outside air temperature estimation unit 35, a battery temperature estimation unit 36, and a battery temperature degradation degree calculation unit 37.

[0024] The speed calculation unit 31 has a function of calculating the speed A3 of the electric vehicle 1 from the position information A1 acquired by the position information acquisition unit 11. The acceleration calculation unit 32 has a function of calculating the acceleration A4 of the electric vehicle 1 from the speed A3 calculated by the speed calculation unit 31. In this way, in this embodiment, both the speed A3 and the acceleration A4 can be calculated using only the position information A1 acquired from the GPS receiver 3. Therefore, there is no need for dedicated sensors for acquiring the speed A3 and the acceleration A4, and it is possible to reduce both the component costs of the sensors and the man-hours required to install the sensors.

[0025] The current estimation unit 33 has a function of estimating the current B of the battery 2 from the speed A3 calculated by the speed calculation unit 31 and the acceleration A4 calculated by the acceleration calculation unit 32. By knowing in advance the correlation between the speed A3, the acceleration A4, and the current B, the current estimation unit 33 can acquire the current B without using a current sensor that is difficult to install later.

[0026] The integrated current calculation unit 34 has a function of calculating an integrated current D by time-integrating the current B estimated by the current estimation unit 33. The integrated current D is a "deterioration feature amount" and "battery information" related to the deterioration of the battery 2. In particular, the integrated current D is the amount of stress on the battery 2 over its lifetime and is an effective feature amount for accurately estimating the state of deterioration.

[0027] The outside air temperature estimator 35 has a function of estimating an outside air temperature A5 based on the location information A1 acquired by the location information acquirer 11 and the date and time information A2 acquired by the date and time information acquirer 12. The outside air temperature estimator 35 acquires the outside air temperature A5 corresponding to the combination of the location information A1 and the date and time information A2 from an external outside air temperature information storage unit 70. The outside air temperature information storage unit 70 is configured by a data server or cloud external to the system.

[0028] In this way, in this embodiment, the outside air temperature A5 can be estimated using the location information A1 and the date and time information A2 acquired from the GPS receiver 3. Therefore, a dedicated sensor for acquiring the outside air temperature A5 is not required, and it is possible to reduce both the component costs of the sensor and the man-hours required to install the sensor.

[0029] The battery temperature estimation unit 36 ​​has a function of estimating the battery temperature C of the battery 2 from the outside air temperature A5 estimated by the outside air temperature estimation unit 35. By knowing in advance the correlation between the outside air temperature A5 and the battery temperature C, the battery temperature estimation unit 36 ​​can acquire the battery temperature C without using a dedicated temperature sensor.

[0030] The battery temperature degradation degree calculation unit 37 has a function of calculating a battery temperature degradation degree E from the battery temperature C estimated by the battery temperature estimation unit 36. The battery temperature degradation degree E is a "degradation feature amount" related to the degradation of the battery 2 and is also "battery information."

[0031] The feature amount calculation unit 30 configured as described above may be configured by a data server or cloud outside the system, as necessary.

[0032] 2-4. Configuration of the vehicle internal information acquisition unit 50 The vehicle internal information acquisition unit 50 is connected to a connection port (not shown) of the diagnostic device 1a mounted on the electric vehicle 1 while the vehicle is stopped via a connection means such as a cable. This allows the vehicle internal information acquisition unit 50 to perform the function of acquiring vehicle internal information J recorded in the diagnostic device 1a from the electric vehicle 1 while the vehicle is stopped. The vehicle internal information acquisition unit 50 is connected to the electric vehicle 1 while the vehicle is stopped, for example, at the time of vehicle inspection or appraisal. The vehicle internal information J includes, for example, accumulated information related to the use of the battery 2 (number of charges, charging time, mileage, etc.). Note that if the vehicle internal information J is not to be acquired, the vehicle internal information acquisition unit 50 may be omitted as necessary.

[0033] 2-5. Configuration of battery degradation estimation unit 60 The battery degradation estimation unit 60 has a function of estimating the degradation state of the battery 2 based on the degradation feature calculated by the feature calculation unit 30. In this case, the degradation feature used is the integrated current D and the battery temperature degradation level E, or, if necessary, the integrated current D and the battery temperature degradation level E plus the vehicle internal information J. This battery degradation estimation unit 60 includes a model generation unit 61 and a degradation calculation unit 62.

[0034] The model generation unit 61 has a function of generating a deterioration model formula for predicting a deterioration level index by machine learning based on the deterioration feature quantities. This deterioration model formula is an equation obtained by linear regression analysis in which the deterioration feature quantities are used as explanatory variables and the true value of the deterioration level index is used as a response variable. As the linear regression analysis, an appropriate algorithm such as lasso regression, ridge regression, or elastic net can be used. The deterioration model formula generated by the model generation unit 61 is stored in the information storage unit 20.

[0035] The deterioration model formula may be updated as appropriate in accordance with the accumulation of various data related to the deterioration of the battery 2. Furthermore, for example, multiple types of deterioration model formulas may be created in accordance with multiple classifications of the battery 2, such as battery capacity, positive electrode material, negative electrode material, etc.

[0036] The deterioration calculation unit 62 has a function of calculating a deterioration level index of the battery 2 using the deterioration model equation generated by the model generation unit 61. This deterioration calculation unit 62 can calculate a deterioration level index of the battery 2. The calculation result by the deterioration calculation unit 62 is output to the output unit 81 of the terminal device 80. The term "output" as used here broadly includes not only display output by screen display or data output, but also output by printing, audio, etc. The terminal device 80 is typically a desktop or notebook personal computer (PC), a tablet terminal, a mobile terminal, or the like. The terminal device 80 may also be a car navigation system attached to the electric vehicle 1. In this case, information for instructing the occupant of the electric vehicle 1 on how to drive may be output to the output unit 81.

[0037] 3. Battery State Estimation Process Next, the battery state estimation process performed by the battery state estimation system 101 will be described with reference to Figures 1 to 7. The battery state estimation process may be performed only once, or may be performed multiple times. This battery state estimation process is typically performed appropriately at times such as when the electric vehicle 1 undergoes vehicle inspection or appraisal, or when the battery 2 is being charged. In this battery state estimation process, the first step S101 to the tenth step S110 of the flowchart shown in Figure 2 are performed sequentially. One or more steps may be added to these steps as necessary, or multiple steps may be combined as appropriate.

[0038] The first step S101 is executed by the position information acquisition unit 11 and the date and time information acquisition unit 12 in FIG. 1. In this first step S101, position information A1 and date and time information A2 are acquired from the GPS receiver 3. The second step S102 is executed by the speed calculation unit 31 in FIG. 1. In this second step S102, a speed A3 is calculated by time-differentiating the position information A1 acquired in the first step S101. The third step S103 is executed by the acceleration calculation unit 32 in FIG. 1. In this third step S103, an acceleration A4 is calculated by time-differentiating the speed A3 calculated in the second step S102.

[0039] The fourth step S104 is executed by the current estimation unit 33 in FIG. 1. In this fourth step S104, the current B is estimated using the two-dimensional correlation map shown in FIG. 3. This correlation map shows the correlation between the absolute value of acceleration A4 and the current B for each speed, and is created in advance and stored in the information storage unit 20. This correlation map shows that the value of current B increases as the acceleration A4 increases and as the speed A3 increases. By applying the speed A3 calculated in the second step S102 and the acceleration A4 calculated in the third step S103 to the correlation map, the current B can be uniquely estimated.

[0040] The correlation map is not limited to the one shown in FIG. 3 . For example, a three-dimensional correlation map obtained by adding another parameter to the correlation map of FIG. 3 may be used. The gradient of the electric vehicle 1 may be used as the other parameter. The "gradient" here may be information indicating the gradient of the electric vehicle 1 itself (vehicle gradient) or information indicating the gradient of the road on which the electric vehicle 1 travels (road gradient). The vehicle gradient and the road gradient are substantially the same. For example, the gradient can be calculated using various GPS information received by the GPS receiver 3. Furthermore, instead of the correlation map, the current B may be estimated using a model formula that calculates the current B from the speed A3, acceleration A4, and gradient.

[0041] The fifth step S105 is executed by the integrated current calculation unit 34 in FIG. 1. In this fifth step S105, the integrated current D of the battery 2 is calculated by time-integrating the current B estimated in the fourth step S104. At this time, it is preferable to calculate the integrated current D for both charging and discharging the battery 2. This makes it possible to accurately estimate the degradation state of the battery 2. Note that in this fifth step S105, the integrated current D can be calculated for at least one of charging and discharging the battery 2.

[0042] The sixth step S106 is executed by the outside air temperature estimation unit 35 in Fig. 1. In this sixth step S106, the outside air temperature estimation unit 35 estimates the outside air temperature A5 using the position information A1 and date and time information A2 acquired in the first step S101. The outside air temperature A5 corresponding to the combination of the position information A1 and date and time information A2 is acquired from the outside air temperature information accumulation unit 70. In other words, if the position and date and time when the electric vehicle 1 was traveling are known, it is possible to find an outside air temperature A5 under the same conditions from past outside air temperature information accumulated in advance in the outside air temperature information accumulation unit 70.

[0043] The seventh step S107 is executed by the battery temperature estimation unit 36 ​​in FIG. 1. In this seventh step S107, the battery temperature C of the battery 2 is estimated from the outside air temperature A5 estimated in the sixth step S106. As shown in FIG. 4, in this seventh step S107, the outside air temperature A5 is corrected based on the offset amount ΔC to obtain the battery temperature C. The battery temperature C is a subtraction value obtained by subtracting the offset amount ΔC from the outside air temperature A5. The offset amount ΔC can be set to an appropriate value based on various setting patterns.

[0044] For example, a first setting pattern P1, a second setting pattern P2, and a third setting pattern P3 as shown in Fig. 5 can be adopted. The first setting pattern P1 is a setting pattern in which the offset amount ΔC is set to a fixed value regardless of the outside air temperature A5. The second setting pattern P2 and the third setting pattern P3 are setting patterns in which the offset amount ΔC is set to a different value depending on the outside air temperature A5.

[0045] Here, the second setting pattern P2 is a pattern in which the offset amount ΔC is set to a smaller value as the outside air temperature A5 increases. Fig. 5 illustrates a pattern in which the offset amount ΔC is linearly reduced as the outside air temperature A5 increases. Although not specifically illustrated, an alternative pattern may be adopted in which the offset amount ΔC is reduced in a curved or stepwise manner as the outside air temperature A5 increases. Furthermore, the offset amount ΔC may be set to a fixed value without being reduced when the outside air temperature A5 exceeds a predetermined value.

[0046] The third setting pattern P3 is a pattern in which the offset amount ΔC is set to a larger value as the outside air temperature A5 increases. Fig. 5 illustrates a pattern in which the offset amount ΔC increases linearly as the outside air temperature A5 increases. Although not specifically illustrated, an alternative pattern may be adopted in which the offset amount ΔC increases in a curved or stepwise manner as the outside air temperature A5 increases. Furthermore, the offset amount ΔC may be set to a fixed value without increasing once the outside air temperature A5 exceeds a predetermined value.

[0047] Furthermore, a fourth setting pattern P4 as shown in FIG. 6 can be employed. The fourth setting pattern P4 is a pattern in which the offset amount ΔC is set to a different value depending on the current B of the battery 2. When this fourth setting pattern P4 is employed, the current B estimated in the fourth step S104 is also used to estimate the battery temperature C in the seventh step S107 (see the dashed-dotted arrows in FIGS. 1 and 2). In connection with the fourth setting pattern P4, a pattern may be employed in which the offset amount ΔC is set to a different value depending on the integrated current of the battery 2, and when the integrated current reaches a threshold value, the offset amount ΔC is thereafter set to a fixed value.

[0048] In addition, instead of correcting the outside air temperature A5 to the battery temperature C based on the offset amount ΔC, the outside air temperature A5 may be corrected to the battery temperature C using mapping data or machine learning created based on past performance.

[0049] Returning to FIG. 2 , the eighth step S108 is executed by the battery temperature degradation degree calculation unit 37 in FIG. 1 . In this eighth step S108, the battery temperature degradation degree E is calculated from the battery temperature C estimated in the seventh step S107. In this eighth step S108, a frequency distribution chart (histogram) M1 showing the frequency distribution of the battery temperature C is used, as shown in FIG. 7 . The battery temperature degradation degree E is determined by adding up the values ​​obtained by multiplying each frequency N1 to N9 in this frequency distribution chart M1 by the individually set degradation contribution coefficients α1 to α9. That is, the calculation formula expressed by the following formula (1) is used.

[0050] E=α1×N1+α2×N2+α3×N3+α4×N4+α5×N5 +α6×N6+α7×N7+α8×N8+α9×N9…(1)

[0051] Note that, although the frequency distribution diagram M1 in FIG. 7 illustrates an example in which there are nine battery temperature C intervals, the number is not limited to this. Furthermore, the deterioration contribution coefficients α1 to α9 may be constant values ​​or may be different values. For example, when considering that one cause of battery deterioration is use in a high-temperature environment, it is preferable to set the deterioration contribution coefficients so that the values ​​increase as the battery temperature C increases. Furthermore, when a specific interval in which battery deterioration is severe is identified, it is preferable to set the deterioration contribution coefficient corresponding to that interval to a value greater than the deterioration contribution coefficients of other intervals. This enables accurate estimation of the deterioration state of the battery 2.

[0052] Returning to Fig. 2, the ninth step S109 is executed as necessary by the vehicle interior information acquisition unit 50 in Fig. 1. In this ninth step S109, the vehicle interior information J is acquired from the diagnostic device 1a mounted on the electric vehicle 1.

[0053] The tenth step S110 is executed by the deterioration calculation unit 62 in FIG. 1. In this tenth step S110, the integrated current D calculated in the fifth step S105, the battery temperature deterioration level E calculated in the eighth step S108, and, if necessary, the vehicle internal information J acquired in the ninth step S109 are substituted into a deterioration model equation generated in advance. As a result, a deterioration level index representing the deterioration state of the battery 2 is calculated. This deterioration level index is output to the output unit 81 as necessary.

[0054] In this tenth step S110, for example, a single degradation model equation can be used, which uses both the integrated current D and the battery temperature degradation level E as explanatory variables, or three of the integrated current D, the battery temperature degradation level E, and the vehicle internal information J as explanatory variables, and uses the true value of the SOH (State of Health), which is one of the degradation level indicators of the battery 2, as the objective variable. The SOH of the battery 2 is calculated using this degradation model equation. Note that a degradation level indicator other than the SOH may also be calculated using a degradation model equation related to the degradation level indicator. Alternatively, a degradation level indicator may be calculated using two degradation model equations each using the integrated current D and the battery temperature degradation level E as explanatory variables, and then another degradation level indicator may be calculated from the two degradation level indicators.

[0055] 4. Effects The battery state estimation system 101 of the first embodiment acquires non-battery information rather than general battery information such as the current and voltage of the battery 2. The non-battery information is essentially different from the battery information in that it can be acquired relatively easily without removing the battery 2 from the electric vehicle 1. Then, a deterioration feature quantity related to the deterioration of the battery 2 is calculated based on this non-battery information, and the deterioration state of the battery 2 is estimated based on this deterioration feature quantity.

[0056] According to this battery state estimation system 101, it is possible to estimate the degradation state of the battery 2 based on non-battery information, which is easier to obtain than battery information. Also, it is possible to use a GPS receiver 3, which is an add-on device that is retrofitted to the electric vehicle 1, to obtain the non-battery information. Furthermore, because it is possible to obtain non-battery information in real time while the electric vehicle 1 is traveling and estimate the degradation state of the battery 2, there is no need for a procedure such as receiving input related to a drive plan from a client in advance before estimating the degradation state of the battery 2, making it easy to use.

[0057] Therefore, according to the first embodiment, it is possible to provide a battery state estimation system 101 that is highly convenient.

[0058] In a modification particularly related to the first embodiment, the degradation state of the battery 2 may be estimated based on only one of the integrated current D and the battery temperature degradation degree E, or the degradation state of the battery 2 may be estimated based on one of the integrated current D and the battery temperature degradation degree E and the vehicle internal information J. Also, the battery temperature estimation unit 36 ​​may be omitted, and instead, a temperature sensor capable of measuring the temperature in the vicinity of the battery 2 may be used to directly and accurately obtain the battery temperature C.

[0059] Hereinafter, other embodiments related to the above-described embodiment 1 will be described with reference to the drawings. In the other embodiments, the same elements as those in embodiment 1 are denoted by the same reference numerals, and the description of the same elements will be omitted.

[0060] 8 differs from the battery state estimation system 101 of the first embodiment in that an outside air temperature A5 is acquired using an outside air temperature sensor 4 attached to the electric vehicle 1. In accordance with this difference, the configurations of an information acquisition unit 10A and a feature amount calculation unit 30A differ from those of the information acquisition unit 10 and the feature amount calculation unit 30 of the first embodiment. In this embodiment, the outside air temperature sensor 4 is an add-on device that is retrofitted to the electric vehicle 1, similar to the GPS receiver 3.

[0061] The information acquisition unit 10A includes an outside air temperature acquisition unit 15 in addition to the position information acquisition unit 11. The outside air temperature acquisition unit 15 has a function of acquiring an outside air temperature A5 outside the electric vehicle 1 from the outside air temperature sensor 4 on the side of the electric vehicle 1. The outside air temperature A5 is "non-battery information."

[0062] The feature amount calculation unit 30A has a configuration in which the outside air temperature estimation unit 35 is omitted from the feature amount calculation unit 30 (see FIG. 1 ) of the first embodiment. This is because the outside air temperature A5 can be acquired on the information acquisition unit 10A side, and therefore there is no need to estimate the outside air temperature A5 again.

[0063] The other configurations are the same as those in the first embodiment.

[0064] In the battery state estimation process by the battery state estimation system 102, the first step S201 to the tenth step S210 in the flowchart shown in Fig. 9 are executed in sequence. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated.

[0065] The first step S201 is executed by the location information acquisition unit 11 in FIG. 8. In this first step S201, location information A1 is acquired from the GPS receiver 3. The second step S202 to the fifth step S205 are substantially the same as the second step S102 to the fifth step S105 (see FIG. 2) of the first embodiment. The sixth step S206 is executed by the outside air temperature acquisition unit 15 in FIG. 8. In this sixth step S206, an outside air temperature A5 is acquired from the outside air temperature sensor 4. The seventh step S207 to the tenth step S210 are substantially the same as the seventh step S107 to the tenth step S110 (see FIG. 2) of the first embodiment.

[0066] According to the second embodiment, the outside air temperature sensor 4 can be used to directly and accurately detect the outside air temperature A5. This is particularly effective when the outside air temperature A5 changes rapidly. As a result, the accuracy of estimating the degradation state of the battery 2 can be improved. Furthermore, it becomes possible to use the GPS receiver 3 and the outside air temperature sensor 4, which are retrofit devices that are retrofitted to the electric vehicle 1, to acquire non-battery information.

[0067] In a modification particularly related to the second embodiment, the degradation state of the battery 2 may be estimated based on only one of the integrated current D and the battery temperature degradation degree E, or the degradation state of the battery 2 may be estimated based on one of the integrated current D and the battery temperature degradation degree E and the vehicle internal information J. Also, the battery temperature estimation unit 36 ​​may be omitted, and instead, a temperature sensor capable of measuring the temperature in the vicinity of the battery 2 may be used to directly and accurately obtain the battery temperature C.

[0068] In addition, the same effects as those in the first embodiment are achieved.

[0069] 10 differs from the battery state estimation system 102 of the second embodiment in that the battery state estimation system 103 of the third embodiment acquires non-battery information using a speed sensor 5 and an acceleration sensor 6 attached to the electric vehicle 1 instead of the GPS receiver 3. In accordance with this difference, the configurations of the information acquisition unit 10B and the feature amount calculation unit 30B differ from those of the information acquisition unit 10A and the feature amount calculation unit 30A of the second embodiment. In this embodiment, the speed sensor 5 and the acceleration sensor 6, like the outside air temperature sensor 4, are add-on devices that are retrofitted to the electric vehicle 1.

[0070] The information acquisition unit 10B includes a speed acquisition unit 13 and an acceleration acquisition unit 14 in addition to an outside air temperature acquisition unit 15. The speed acquisition unit 13 has a function of acquiring a speed A3 of the electric vehicle 1 from a speed sensor 5 on the side of the electric vehicle 1. The acceleration acquisition unit 14 has a function of acquiring an acceleration A4 of the electric vehicle 1 from an acceleration sensor 6 on the side of the electric vehicle 1. Both the speed A3 and the acceleration A4 are "non-battery information."

[0071] The feature amount calculation unit 30B has a configuration in which the speed calculation unit 31 and the acceleration calculation unit 32 are omitted from the feature amount calculation unit 30A (see FIG. 8 ) of the second embodiment. This is because the speed A3 and the acceleration A4 can be acquired on the information acquisition unit 10B side, and therefore there is no need to calculate the speed A3 and the acceleration A4 anew.

[0072] The other configurations are the same as those in the second embodiment.

[0073] In the battery state estimation process by the battery state estimation system 103, the first step S301 to the ninth step S309 in the flowchart shown in Fig. 11 are sequentially executed. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated.

[0074] The first step S301 is executed by the speed acquisition unit 13 in Fig. 10. In this first step S301, a speed A3 is acquired from the speed sensor 5. The second step S302 is executed by the acceleration acquisition unit 14 in Fig. 10. In this second step S302, an acceleration A4 is acquired from the acceleration sensor 6. The third step S303 to the ninth step S309 are substantially the same as the fourth step S204 to the tenth step S210 (see Fig. 9) of the second embodiment.

[0075] According to the third embodiment, the speed A3 and acceleration A4 can be directly and accurately detected using the speed sensor 5 and acceleration sensor 6. As a result, it is possible to improve the accuracy of estimating the degradation state of the battery 2. Furthermore, it becomes possible to use the outside air temperature sensor 4, the speed sensor 5, and the acceleration sensor 6, which are retrofit devices that are retrofitted to the electric vehicle 1, to acquire non-battery information.

[0076] In addition, the same effects as those in the second embodiment are achieved.

[0077] In a modification particularly related to the third embodiment, the degradation state of the battery 2 may be estimated based on only one of the integrated current D and the battery temperature degradation degree E, or the degradation state of the battery 2 may be estimated based on one of the integrated current D and the battery temperature degradation degree E and the vehicle internal information J. Also, the acceleration sensor 6 may be omitted, and instead the acceleration A4 may be calculated by time-differentiating the speed A3 acquired from the speed sensor 5. Also, the battery temperature estimation unit 36 ​​may be omitted, and instead a temperature sensor capable of measuring the temperature near the battery 2 may be used to directly and accurately acquire the battery temperature C.

[0078] 12 differs from the battery degradation estimation system 101 of the first embodiment in that the degradation feature quantity related to the degradation of the battery 2 is non-battery information. That is, in the first embodiment, the degradation feature quantity is the integrated current D and the battery temperature degradation degree E, which are battery information. In addition, in accordance with this difference, the configuration of the feature quantity calculation unit 30C differs from that of the feature quantity calculation unit 30 of the first embodiment. On the other hand, the configuration of the information acquisition unit 10C is the same as that of the information acquisition unit 10 of the first embodiment.

[0079] The feature quantity calculation unit 30C includes a speed calculation unit 31, an acceleration calculation unit 32, and an outside air temperature estimation unit 35, which are the same components as the feature quantity calculation unit 30 of the first embodiment (see FIG. 1 ), as well as a traveling distance calculation unit 38, an acceleration degradation degree calculation unit 39, and an outside air temperature degradation degree calculation unit 40.

[0080] The traveling distance calculation unit 38 has a function of calculating the traveling distance F of the electric vehicle 1 from the speed A3 calculated by the speed calculation unit 31. The acceleration degradation degree calculation unit 39 has a function of calculating the acceleration degradation degree G from the acceleration A4 calculated by the acceleration calculation unit 32. The outside air temperature degradation degree calculation unit 40 has a function of calculating the outside air temperature degradation degree H from the outside air temperature A5 estimated by the outside air temperature estimation unit 35. The traveling distance F, the acceleration degradation degree G, and the outside air temperature degradation degree H are all "degradation feature amounts" related to degradation of the battery 2, and are "non-battery information."

[0081] The other configurations are the same as those in the first embodiment.

[0082] In the battery state estimation process by the battery state estimation system 104, the first step S401 to the ninth step S409 in the flowchart shown in Fig. 13 are executed in sequence. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated.

[0083] The first step S401 to the third step S403, the sixth step S406, and the eighth step S408 are substantially the same as the first step S101 to the third step S103, the sixth step S106, and the ninth step S109 (see FIG. 2) of the first embodiment.

[0084] The fourth step S404 is executed by the travel distance calculation unit 38 shown in Fig. 12. In this fourth step S404, the travel distance F is calculated by integrating the speed A3 calculated in the second step S402 with respect to time.

[0085] The fifth step S405 is executed by the acceleration degradation degree calculation unit 39 in FIG. 12. In this fifth step S405, an acceleration degradation degree G is calculated from the acceleration A4 calculated in the third step S403. In this fifth step S405, a frequency distribution chart (histogram) M2 showing the frequency distribution of the absolute value of the acceleration A4 is used, as shown in FIG. 14. The acceleration degradation degree G is determined by adding up the values ​​obtained by multiplying each of the frequencies N11 to N19 in this frequency distribution chart M2 by the individually set degradation contribution coefficients β1 to β9. That is, the calculation formula expressed by the following formula (2) is used.

[0086] G=β1×N11+β2×N12+β3×N13+β4×N14+β5×N15 +β6×N16+β7×N17+β8×N18+β9×N19…(2)

[0087] Note that, although the frequency distribution diagram M2 in FIG. 14 illustrates an example in which there are nine intervals of the absolute value of acceleration A4, the number is not limited to this. Furthermore, the deterioration contribution coefficients β1 to β9 may be constant values ​​or may be different values. For example, the deterioration contribution coefficients may be set so that the larger the absolute value of acceleration A4, the larger the value of the deterioration contribution coefficient. Alternatively, the deterioration contribution coefficient corresponding to a specific interval where battery deterioration is severe may be set to a value larger than the deterioration contribution coefficients of other intervals. This enables accurate estimation of the deterioration state of battery 2.

[0088] Returning to FIG. 13 , seventh step S407 is executed by the outside air temperature degradation degree calculation unit 40 in FIG. 12. In this seventh step S407, an outside air temperature degradation degree H is calculated from the outside air temperature A5 estimated in the sixth step S406. In this seventh step S407, a frequency distribution chart (histogram) M3 showing the frequency distribution of the outside air temperature A5 is used, as shown in FIG. 15 . The sum of values ​​obtained by multiplying each of the frequencies N21 to N29 in this frequency distribution chart M3 by the individually set degradation contribution coefficients γ1 to γ9 is set as the outside air temperature degradation degree H. That is, the calculation formula expressed by the following formula (3) is used.

[0089] H=γ1×N21+γ2×N22+γ3×N23+γ4×N24+γ5×N25 +γ6×N26+γ7×N27+γ8×N28+γ9×N29…(3)

[0090] Note that, although the frequency distribution diagram M3 in FIG. 15 illustrates an example in which there are nine sections of the outside air temperature A5, the number is not limited to this. Furthermore, the deterioration contribution coefficients γ1 to γ9 may be constant values ​​or may be different values. For example, the deterioration contribution coefficients may be set so that they increase as the outside air temperature A5 increases, or the deterioration contribution coefficient corresponding to a specific section where battery deterioration is severe may be set to a value greater than the deterioration contribution coefficients of other sections. This enables accurate estimation of the deterioration state of the battery 2.

[0091] Returning to Fig. 13 , the ninth step S409 is executed by the deterioration calculation unit 62 in Fig. 12 . In this ninth step S409, the travel distance F calculated in the fourth step S404, the acceleration deterioration level G calculated in the fifth step S405, the outside air temperature deterioration level H calculated in the seventh step S407, and, as needed, the vehicle internal information J acquired in the eighth step S408 are substituted into a deterioration model equation generated in advance. As a result, a deterioration level index representing the deterioration state of the battery 2 is calculated. This deterioration level index is output to the output unit 81 as needed.

[0092] In this ninth step S409, for example, a single deterioration model equation can be used, which is generated using three explanatory variables: the mileage F, the acceleration degradation G, and the outside air temperature degradation H; or four explanatory variables: the mileage F, the acceleration degradation G, the outside air temperature degradation H, and the vehicle internal information J; and the true value of the SOH (State of Health), which is one of the degradation indicators of the battery 2, as the objective variable. The SOH of the battery 2 is calculated using this deterioration model equation. Note that a deterioration model equation related to a degradation indicator other than the SOH may also be used to calculate the relevant degradation indicator. Alternatively, a deterioration indicator may be calculated using three deterioration model equations each using the mileage F, the acceleration degradation G, and the outside air temperature degradation H as explanatory variables, and then another deterioration indicator may be calculated from the three degradation indicators.

[0093] According to the fourth embodiment, the degradation state of the battery 2 can be estimated without estimating battery information such as the current B or the battery temperature C. In this case, the calculation load can be reduced by omitting the process for estimating the battery information.

[0094] In addition, the same effects as those in the first embodiment are achieved.

[0095] In a modification particularly related to the fourth embodiment, the degradation state of the battery 2 may be estimated based on one or two of the travel distance F, the acceleration degradation degree G, and the outside air temperature degradation degree H, or the degradation state of the battery 2 may be estimated based on one or two of the travel distance F, the acceleration degradation degree G, and the outside air temperature degradation degree H and the vehicle internal information J. Also, the battery temperature estimation unit 36 ​​may be omitted, and instead a temperature sensor capable of measuring the temperature in the vicinity of the battery 2 may be used to directly and accurately obtain the battery temperature C.

[0096] 16 differs from the battery state estimation system 104 of the fourth embodiment in that an outside air temperature A5 is acquired using an outside air temperature sensor 4 attached to an electric vehicle 1. In accordance with this difference, the configurations of an information acquisition unit 10D and a feature amount calculation unit 30D differ from those of the information acquisition unit 10C and the feature amount calculation unit 30C of the fourth embodiment.

[0097] The information acquisition unit 10D includes an outside air temperature acquisition unit 15 in addition to the position information acquisition unit 11. The outside air temperature acquisition unit 15 has a function of acquiring an outside air temperature A5 outside the electric vehicle 1 from the outside air temperature sensor 4 on the electric vehicle 1 side.

[0098] The feature amount calculation unit 30D has a configuration in which the outside air temperature estimation unit 35 is omitted from the feature amount calculation unit 30C (see FIG. 12 ) of the fourth embodiment. This is because the outside air temperature A5 can be acquired on the information acquisition unit 10D side, and therefore there is no need to estimate the outside air temperature A5 again.

[0099] The other configurations are the same as those of the fourth embodiment.

[0100] In the battery state estimation process by the battery state estimation system 105, the first step S501 to the ninth step S509 in the flowchart shown in Fig. 17 are executed in sequence. One or more steps may be added to these steps as necessary, or multiple steps may be integrated as appropriate.

[0101] The first step S501 is executed by the location information acquisition unit 11 in FIG. 16. In this first step S501, location information A1 is acquired from the GPS receiver 3. The second step S502 to the fifth step S505 are substantially the same as the second step S402 to the fifth step S405 (see FIG. 13) of the fourth embodiment. The sixth step S506 is executed by the outside air temperature acquisition unit 15 in FIG. 16. In this sixth step S506, the outside air temperature A5 is acquired from the outside air temperature sensor 4. The seventh step S507 to the ninth step S509 are substantially the same as the seventh step S407 to the ninth step S409 (see FIG. 13) of the fourth embodiment.

[0102] According to the fifth embodiment, the outside air temperature A5 can be directly detected with high accuracy using the outside air temperature sensor 4. This is particularly effective when the outside air temperature A5 changes rapidly. As a result, the accuracy of estimating the degradation state of the battery 2 can be improved.

[0103] In addition, the same effects as those of the fourth embodiment are achieved.

[0104] In a modification particularly related to the fifth embodiment, the degradation state of the battery 2 may be estimated based on one or two of the travel distance F, the acceleration degradation degree G, and the outside air temperature degradation degree H, or the degradation state of the battery 2 may be estimated based on one or two of the travel distance F, the acceleration degradation degree G, and the outside air temperature degradation degree H and the vehicle internal information J. Also, the battery temperature estimation unit 36 ​​may be omitted, and instead, a temperature sensor capable of measuring the temperature in the vicinity of the battery 2 may be used to directly and accurately obtain the battery temperature C.

[0105] 18 differs from the battery state estimation system 105 of the fifth embodiment in that the battery state estimation system 106 of the sixth embodiment acquires non-battery information using a speed sensor 5 and an acceleration sensor 6 attached to the electric vehicle 1 instead of the GPS receiver 3. In accordance with this difference, the configurations of an information acquisition unit 10E and a feature amount calculation unit 30E differ from those of the information acquisition unit 10D and the feature amount calculation unit 30D of the fifth embodiment.

[0106] The information acquisition unit 10E includes an outside air temperature acquisition unit 15, a speed acquisition unit 13, and an acceleration acquisition unit 14. The speed acquisition unit 13 has a function of acquiring the speed A3 of the electric vehicle 1 from the speed sensor 5 on the electric vehicle 1 side. The acceleration acquisition unit 14 has a function of acquiring the acceleration A4 of the electric vehicle 1 from the acceleration sensor 6 on the electric vehicle 1 side.

[0107] The feature amount calculation unit 30E has a configuration in which the speed calculation unit 31 and the acceleration calculation unit 32 are omitted from the feature amount calculation unit 30D (see FIG. 16 ) of embodiment 5. This is because the speed A3 and the acceleration A4 can be acquired on the information acquisition unit 10E side, and therefore there is no need to calculate the speed A3 and the acceleration A4 anew.

[0108] The other configurations are the same as those of the fifth embodiment.

[0109] In the battery state estimation process by the battery state estimation system 106, the first step S601 to the eighth step S608 in the flowchart shown in Fig. 19 are executed in sequence. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated.

[0110] The first step S601 is executed by the speed acquisition unit 13 in Fig. 18. In this first step S601, a speed A3 is acquired from the speed sensor 5. The second step S602 is executed by the acceleration acquisition unit 14 in Fig. 18. In this second step S602, an acceleration A4 is acquired from the acceleration sensor 6. The third step S603 to the eighth step S608 are substantially the same as the fourth step S504 to the ninth step S509 (see Fig. 17) of the fifth embodiment.

[0111] According to the sixth embodiment, the speed A3 and acceleration A4 can be directly detected with high accuracy using the speed sensor 5 and acceleration sensor 6. As a result, the accuracy of estimating the degradation state of the battery 2 can be improved.

[0112] In addition, the same effects as those in the fifth embodiment are achieved.

[0113] In a modification particularly related to the sixth embodiment, the degradation state of the battery 2 may be estimated based on one or two of the mileage F, the acceleration degradation degree G, and the outside air temperature degradation degree H, or the degradation state of the battery 2 may be estimated based on one or two of the mileage F, the acceleration degradation degree G, and the outside air temperature degradation degree H and the vehicle internal information J. Furthermore, the acceleration sensor 6 may be omitted, and instead the acceleration A4 may be calculated by time-differentiating the speed A3 acquired from the speed sensor 5. Furthermore, the battery temperature estimation unit 36 ​​may be omitted, and instead a temperature sensor capable of measuring the temperature in the vicinity of the battery 2 may be used to directly and accurately acquire the battery temperature C.

[0114] 20 differs from the battery state estimation system 101 of the first embodiment in that the battery state estimation system 107 of the seventh embodiment uses a gradient A6 in addition to a velocity A3 and an acceleration A4 to estimate a current B. In this battery state estimation system 107, the configuration of an information acquisition unit 10F is similar to that of the information acquisition unit 10A of the first embodiment, but the configuration of a feature calculation unit 30F differs from that of the feature calculation unit 30A of the first embodiment.

[0115] The feature calculation unit 30F has a configuration in which a gradient calculation unit 32a is added to the feature calculation unit 30A of the first embodiment. The gradient calculation unit 32a has a function of calculating a gradient A6 based on the position information A1 acquired by the position information acquisition unit 11 from the GPS receiver 3. The gradient A6 is "non-battery information." The current estimation unit 33 is configured to estimate a current B of the battery 2 from the speed A3 calculated by the speed calculation unit 31, the acceleration A4 calculated by the acceleration calculation unit 32, and the gradient A6 calculated by the gradient calculation unit 32a. By previously understanding the correlation between the speed A3, the acceleration A4, the gradient A6, and the current B, the current estimation unit 33 can acquire the current B without using a current sensor, which is difficult to retrofit.

[0116] The other configurations are the same as those in the first embodiment.

[0117] In the battery state estimation process by the battery state estimation system 107, the first step S701 to the tenth step S710 of the flowchart shown in FIG. 21 are sequentially executed. One or more steps may be added to these steps as needed, or multiple steps may be appropriately integrated. The steps other than the additional step S703a and the fourth step 704 are substantially the same as the corresponding steps in the first embodiment (see FIG. 2 ). That is, steps S701 to S703 and steps S705 to S710 are substantially the same as steps S101 to S103 and steps S105 to S110 in the first embodiment.

[0118] Additional step S703a is executed by the gradient calculation unit 32a in Fig. 20. In this additional step S703a, a gradient A6 related to the electric vehicle 1 is calculated. Here, the distance difference and altitude difference between any two points can be found from the position information A1 acquired from the GPS receiver 3, and the gradient A6 can be calculated by using the trigonometric function formula A6 = atan (altitude difference / distance difference).

[0119] Then, in a fourth step 704, the current B is estimated using a model equation that calculates the current B from the speed A3, acceleration A4, and gradient A6.

[0120] When estimating current B in step 704, for example, a running resistance model equation can be used to calculate the running resistance of electric vehicle 1 from speed A3, acceleration A4, and gradient A6. In this running resistance model equation, the value of running resistance is the sum of the resistance values ​​of acceleration resistance, air resistance, gradient resistance, and rolling resistance. That is, running resistance can be expressed as running resistance = acceleration resistance + air resistance + gradient resistance + rolling resistance. Then, an estimated value of current B can be calculated using the running resistance calculated using this running resistance model equation. When this estimated value is compared with the true value of current B measured by a sensor or the like on the electric vehicle 1 side, it has been confirmed that the error of the estimated value relative to the true value can be kept low. Therefore, this estimation method is effective for estimating current B with high accuracy.

[0121] According to the seventh embodiment, it is possible to estimate the current B from the calculated speed A3, acceleration A4, and gradient A6.

[0122] In addition, the same effects as those in the first embodiment are achieved.

[0123] Although the additional step S703a in Fig. 21 illustrates a case where the gradient A6 is calculated based on the position information A1 acquired from the GPS receiver 3, the gradient A6 may instead be calculated using another example that will be described with reference to Fig. 22. This another example is generally a method of calculating the gradient A6 from the output value of the acceleration sensor 6 (see Fig. 20) and the speed A3 of the electric vehicle 1.

[0124] As shown in Fig. 22 , assuming that the electric vehicle 1 is traveling while inclined at a gradient θ, the gradient θ can be calculated using a calculation formula (calculation formula shown in Fig. 22 ) that determines the arctangent function of the value obtained by dividing the horizontal acceleration component Ay of the acceleration A by the vertical acceleration component Az. The gradient θ in Fig. 22 corresponds to "gradient A6" in Figs. 20 and 21 .

[0125] 22 , the horizontal acceleration component Ay is a value obtained by subtracting the acceleration Ay_d from the horizontal acceleration Ay_u when the direction along the slope is defined as the horizontal direction. The acceleration Ay_u is obtained based on the output value of the acceleration sensor 6. The acceleration Ay_d is obtained as a differential value of the speed A3 (vehicle speed) of the electric vehicle 1. The speed A3 in this case may be a vehicle speed measurement value measured by the electric vehicle 1, or may be a GPS vehicle speed value obtained by the GPS receiver 3 (see FIG. 20 ). The vertical acceleration component Az is obtained based on the output value of the acceleration sensor 6.

[0126] As in the above-described alternative example, if the gradient A6 is calculated based on the output value of the acceleration sensor 6 and the differential value of the speed A3 of the electric vehicle 1, there is no need to use altitude data, and therefore no calculation error occurs even when traveling on a highway or an elevated road. Therefore, it is possible to calculate the gradient A6 with stable accuracy regardless of the traveling route of the electric vehicle 1.

[0127] Furthermore, in the above running resistance model equation, the term that combines acceleration resistance and gradient resistance can be expressed using the output value of the acceleration sensor 6 (see FIG. 20). Therefore, in this case, running resistance can be calculated using the output value of the acceleration sensor 6 without actually calculating the gradient A6. This simplifies the process of estimating the current B.

[0128] 23 , when the electric vehicle 1 is loaded, the area where the luggage is carried generally sinks compared to other areas, and it is therefore assumed that the gradient θ of the electric vehicle 1 will differ between when it is loaded and when it is not. Therefore, when the electric vehicle 1 travels along the same travel route, if the gradient θ is always offset by a certain amount compared to the gradient transition data detected when it is not loaded, it can be determined that the electric vehicle 1 is loaded with luggage. A gradient map prepared in advance may be used instead of the gradient transition data.

[0129] When it is determined that the electric vehicle 1 is loaded, the vehicle mass of the electric vehicle 1 is corrected to a value to which the luggage mass is added, and the current B is estimated using the corrected vehicle mass. At this time, as shown in the correlation diagram of FIG. 24, the luggage mass can be calculated from the gradient offset amount Δθ in FIG. 23. This makes it possible to accurately estimate the current B by taking into account the loaded state of the electric vehicle 1. Note that while FIG. 23 illustrates an example in which the gradient is offset so that it is larger when loaded than when normal, there may also be cases in which the gradient is offset so that it is smaller when loaded than when normal.

[0130] Eighth Embodiment A battery state estimation system 108 of an eighth embodiment shown in Fig. 25 differs from the battery state estimation system 103 of the third embodiment in that a gradient sensor 7 is added to the electric vehicle 1 and a gradient A6, which is non-battery information, is acquired using the gradient sensor 7. The gradient sensor 7 is an add-on device that is retrofitted to the electric vehicle 1. In accordance with this difference, the configuration of an information acquisition unit 10G differs from that of the information acquisition unit 10B of the second embodiment. On the other hand, the configuration of a feature calculation unit 30G is similar to that of the feature calculation unit 30B of the third embodiment.

[0131] The information acquisition unit 10G has a configuration in which a gradient acquisition unit 16 is added to the information acquisition unit 10B of embodiment 3. The gradient acquisition unit 16 has a function of acquiring a gradient A6 from a gradient sensor 7 on the side of the electric vehicle 1. In the feature calculation unit 30G, the current estimation unit 33 is configured to estimate the current B of the battery 2 from the speed A3 acquired by the speed acquisition unit 13, the acceleration A4 calculated by the acceleration acquisition unit 14, and the gradient A6 acquired by the gradient acquisition unit 16, as in embodiment 7.

[0132] The other configurations are the same as those of the third embodiment.

[0133] In the battery state estimation process by the battery state estimation system 108, the first step S801 to the ninth step S809 of the flowchart shown in FIG. 26 are sequentially executed. One or more steps may be added to these steps as needed, or multiple steps may be appropriately integrated. The steps other than the additional step S802a and the third step 803 are substantially the same as the corresponding steps in the third embodiment (see FIG. 11 ). That is, steps S801, S802, and S804 to S809 are substantially the same as steps S301, S302, and S304 to S309 in the third embodiment.

[0134] The additional step S802a is executed by the gradient acquisition unit 16 in FIG. 25 . In this additional step S802a, a gradient A6 of the electric vehicle 1 is acquired from the gradient sensor 7. At this time, a known inclination sensor whose sensor output can be used as an inclination angle proportional to the angle at which the electric vehicle 1 is inclined can be used as the gradient sensor 7. Also, instead of the inclination sensor, a known inertial sensor such as an acceleration sensor 6 or a gyro sensor may be used to acquire the gradient A6. In this case, it is preferable to combine the outputs of the acceleration sensor 6 and the gyro sensor.

[0135] In the third step S803, the current B is estimated based on the speed A3 obtained in the first step S801, the acceleration A4 obtained in the second step S802, and the gradient A6 obtained in the additional step S802a.

[0136] According to the eighth embodiment, it is possible to estimate the current B from the speed A3, acceleration A4, and gradient A6 obtained using the respective sensors. At this time, the gradient A6 can be directly detected with high accuracy using the gradient sensor 7. As a result, it is possible to improve the accuracy of estimating the degradation state of the battery 2.

[0137] In addition, the same effects as those in the third embodiment are achieved.

[0138] 27 differs from the battery state estimation system 101 of Embodiment 1 in that the battery state estimation system 109 of Embodiment 9 further has a function of complementing degradation feature amounts for periods during which non-battery information has not been acquired. For this reason, the battery state estimation system 109 is additionally provided with a feature amount complementing unit 71.

[0139] The feature quantity complementing unit 71 has a function of complementing the integrated current D (deterioration feature quantity) calculated by the integrated current calculation unit 34 for a period during which the location information acquisition unit 11 has not been able to acquire location information A1 (non-battery information). This feature quantity complementing unit 71 includes a determination unit 72 and a complementation calculation unit 73. The determination unit 72 has a function of determining whether or not to complement the integrated current D. The complementation calculation unit 73 has a function of performing complementation calculation processing on the integrated current D. Then, the battery deterioration estimation unit 60 estimates the deterioration state of the battery 2 based on the integrated current D after the complementation calculation processing has been performed by the complementation calculation unit 73 of the feature quantity complementing unit 71.

[0140] The other configurations are the same as those in the first embodiment.

[0141] In the battery state estimation process by the battery state estimation system 109, steps S901 to S910 in the flowchart shown in FIG. 28 are sequentially executed. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated. Steps other than additional step S905a and additional step S905b are substantially the same as the corresponding steps in embodiment 1 (see FIG. 2). That is, steps S901 to S905 and steps S906 to S910 are substantially the same as steps S101 to S105 and steps S101 to S110 in embodiment 1.

[0142] Additional step S905a is executed by the determination unit 72 in FIG. 27. In additional step S905a, it is determined whether or not to complement the integrated current D. For example, it is determined that the integrated current D is to be complemented when there is an unacquired period in which the position information A1 has not been acquired. If it is determined in additional step S905a that the integrated current D is to be complemented (if "Yes" in additional step S905a), the process proceeds to additional step S905b. If it is not determined that the integrated current D is to be complemented (if "Yes" in additional step S905a), the process skips additional step S905b and proceeds to step S910.

[0143] Additional step S905b is executed by the complement calculation unit 73 in Fig. 27. In this additional step S905b, a complement calculation process is performed to complement the integrated current D for the unacquired period. An example of this complement calculation process will be described with reference to Figs. 29 and 30. Note that, although the combination of position information A1 and integrated current D is illustrated in this embodiment, the combination of non-battery information and deterioration feature amount is not limited to this and can be changed appropriately as necessary.

[0144] As shown in FIG. 29 , in a correlation diagram showing the correlation between the integrated current D and time, if the purchase date of the electric vehicle 1 can be obtained, the elapsed time from the purchase date is set to time t1, and an approximation line L1 (e.g., obtained using the least squares method) of the acquired data for the acquired period is extended until the time reaches 0 (zero), thereby extrapolating data for the unacquired period. At this time, the acquired data is offset by the amount of the extrapolated data for the unacquired period. This allows the integrated current D for the unacquired period to be interpolated. According to FIG. 29 , it is estimated that the integrated current D at time t1 is D1, and the integrated current D at time t2 is D2. Instead of the correlation diagram of FIG. 29 , a correlation diagram showing the correlation between the integrated current D and mileage may be used.

[0145] When the purchase date of the electric vehicle 1 cannot be obtained, it is possible to use the correlation diagram shown in FIG. 30 that shows the correlation between travel distance F and time. In this correlation diagram, data for the unacquired period is extrapolated by extending the approximate line L2 (for example, obtained using the least squares method) of the acquired data until the travel distance F becomes 0 (zero). The time when the travel distance F is 0 can be set to 0, and the time elapsed up to time t3 can be regarded as the unacquired period. This unacquired period corresponds to the time elapsed from the purchase date in FIG. 29. This makes it possible to supplement the integrated current D for the unacquired period even when the purchase date of the electric vehicle 1 cannot be obtained.

[0146] According to the ninth embodiment, the degradation state of the battery 2 is estimated using the degradation feature amount obtained after the non-acquisition period has been complemented, thereby improving the accuracy of estimating the degradation state of the battery 2 .

[0147] In addition, the same effects as those in the first embodiment are achieved.

[0148] 31 differs from the battery state estimation system 101 of the first embodiment in that the battery degradation estimation unit 60 includes an evaluation unit 63 in addition to a model generation unit 61 and a degradation calculation unit 62. The evaluation unit 63 has a function of evaluating the SOH (degradation index) calculated by the degradation calculation unit 62.

[0149] The other configurations are the same as those in the first embodiment.

[0150] In the battery state estimation process by the battery state estimation system 110, steps S1001 to S1011 in the flowchart shown in FIG. 32 are executed sequentially. One or more steps may be added to these steps as needed, or multiple steps may be appropriately integrated. The steps other than step S1011 are substantially the same as the corresponding steps in the first embodiment (see FIG. 2). That is, steps S1001 to S1010 are substantially the same as steps S101 to S110 in the first embodiment.

[0151] Step S1011 is executed by the evaluation unit 63 in FIG. 31. In this step S1011, the estimation result of step S1010 is compared with reference information for evaluation. For example, as shown in FIG. 33, the estimated value of SOH is compared with the reference value. Then, if the reference value is lower than the estimated value and the difference between the reference value and the estimated value exceeds a threshold, it is evaluated that the battery 2 is in a state of rapid degradation. This evaluation result is output to the output unit 81 (see FIG. 31). Here, the estimated value is a value on an estimation curve, and the reference value is a value on a reference curve. The reference curve is, for example, highly accurate reference information accumulated in a battery monitoring unit or the like.

[0152] According to the tenth embodiment, it is possible to notify the user that the battery 2 is actually in a state of rapid degradation based on the evaluation result of the estimated value of the SOH, thereby making it possible to prompt the user to take appropriate measures against the rapid degradation of the battery 2.

[0153] In addition, the same effects as those in the first embodiment are achieved.

[0154] 34 differs from the battery state estimation system 107 of the seventh embodiment in that it further has a function of estimating the SOC of the battery 2. For this reason, the battery state estimation system 111 is additionally provided with an SOC estimation unit 74. The SOC is an index representing the state of charge of the battery 2, and indicates the current state of charge as a percentage, with fully charged being 100% and fully discharged being 0%.

[0155] The SOC estimation unit 74 has a function of estimating the SOC of the battery 2 based on the period integrated current Da calculated by the integrated current calculation unit 34 and the SOH (deterioration level index) calculated by the deterioration calculation unit 62. Here, the period integrated current Da is the time integral value of the current within the period for which the SOC is to be estimated. The SOC estimation result by this SOC estimation unit 72 is output to the output unit 81 of the terminal device 80.

[0156] The other configurations are the same as those of the seventh embodiment.

[0157] In the battery state estimation process by the battery state estimation system 111, steps S1101 to S1112 in the flowchart shown in Fig. 35 are executed sequentially. One or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated. Steps other than step S1111 and step S1112 are substantially the same as the corresponding steps in embodiment 7 (see Fig. 21). That is, steps S1101 to S1110 are substantially the same as steps S701 to S710 in embodiment 7.

[0158] In step S1103a, similar to step 703a in the seventh embodiment, gradient A6 is calculated based on the position information A1 acquired from the GPS receiver 3. Alternatively, in this step S1103a, as a modified example, gradient A6 may be calculated from the output value of the acceleration sensor 6 and the speed A3 of the electric vehicle 1. Alternatively, as in the eighth embodiment, gradient A6 may be acquired from the gradient sensor 7 instead of calculating gradient A6.

[0159] In step S1104, the current B can be estimated based on the speed A3, acceleration A4, and gradient A6 by using the running resistance model equation described above in step 704 of embodiment 7. Alternatively, as a modification of step S1104, the current B may be estimated based only on the speed A3 and acceleration A4 without using the gradient A6.

[0160] Step S1111 is executed by the accumulated current calculation unit 34 in FIG. 34. In step S1111, a period accumulated current Da corresponding to the period from when the initial SOC value is acquired to when the SOC is estimated is calculated. Step S1112 is executed by the SOC estimation unit 74 in FIG. 34. In step S1112, an estimated SOC value Se is estimated based on the SOH estimated in step S1110 and the period accumulated current Da calculated in step S1111. In step S1112, for example, the calculation formula represented by the following formula (4) is used.

[0161] Se = SOC initial value - Da / (battery capacity × SOH) × 100 ... (4)

[0162] In equation (4), the battery capacity is the capacity immediately after the manufacture of the battery 2. The SOC value immediately after the manufacture of the battery 2 or the actual measurement value of the SOC obtained during rapid charging or inspection of the battery 2 can be used as the initial SOC value.

[0163] For example, if the electric vehicle 1 is a vehicle intended for regular use, such as a taxi or a bus, the SOC of the battery 2 at the time of departure when the vehicle departs from the base on time can be set as the initial SOC value (e.g., 100), and the SOC estimation value Se can be estimated at any estimation time after departure.

[0164] Furthermore, for example, if the electric vehicle 1 departs after being parked for a certain period of time or longer at a location where an arbitrary charger is installed, the SOC of the battery 2 at the time of departure can be set as the initial SOC value (e.g., 100), and the SOC estimation value Se can be estimated at any estimation time after departure.

[0165] The SOC estimation value Se' may be estimated using the following formula (5), which omits the SOH term from formula (4). In this case, it is sufficient to execute the SOC estimation process independently of the process for estimating battery degradation. Therefore, the process for estimating battery degradation may be omitted and only the SOC estimation process may be executed, or both the process for estimating battery degradation and the SOC estimation process may be executed.

[0166] Se′=SOC initial value−Da / battery capacity×100 (5)

[0167] According to the tenth embodiment, by estimating the SOC, which is one of the battery states of the battery 2, it becomes possible to formulate an efficient charging plan for the battery 2 according to the SOC estimation result.

[0168] In addition, the same effects as those of the seventh embodiment are achieved.

[0169] In a modified example particularly related to embodiment 11, when the initial SOC value is unknown in the above equation (4) or (5), the SOC decrease amounts Sd, Sd′ may be estimated instead of the SOC estimation values ​​Se, Se′ using the calculation formulas represented by the following equations (6) or (7).

[0170] Sd = Da / (battery capacity × SOH) × 100 ... (6)

[0171] Sd'=Da / battery capacity×100 (7)

[0172] Although the present disclosure has been described based on the above-described embodiments, it is understood that the present disclosure is not limited to these forms and structures. The present disclosure also encompasses various modifications and modifications within the scope of equivalents. In addition, various combinations and forms, as well as other combinations and forms including only one element, more than one element, or less than one element, are also within the scope and spirit of the present disclosure. For example, the following forms can be implemented by applying the above-described forms.

[0173] In the above-described embodiment, multiple pieces of information, namely, position information A1, date and time information A2, speed A3, acceleration A4, outside air temperature A5, and gradient A6, have been given as examples of non-battery information, but non-battery information other than these multiple pieces of information may also be used if necessary.

[0174] In the above embodiment, an estimation technique for estimating the degradation state of a battery mounted on an electric vehicle has been exemplified, but this estimation technique may also be applied to estimation techniques for estimating the degradation state of a battery mounted on consumer equipment or industrial equipment other than an electric vehicle.

[0175] The features of the present disclosure are as follows: [1] A battery state estimation system (101, 102, 103, 104, 105, 106, 107, 108, 109, 110) comprising: an information acquisition unit (10, 10A, 10B, 10C, 10D, 10E, 10F, 10G) that acquires non-battery information (A1, A2, A3, A4, A5, A6) while an electric vehicle (1) is traveling; a feature calculation unit (30, 30A, 30B, 30C, 30D, 30E, 30F, 30G) that calculates deterioration feature quantities (D, E, F, G, H) related to deterioration of a battery (2) mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and a battery deterioration estimation unit (60) that estimates a deterioration state of the battery based on the deterioration feature quantities calculated by the feature calculation unit. [2] The battery state estimation system according to [1], wherein the information acquisition unit includes a position information acquisition unit (11) that acquires position information (A1) of the electric vehicle, and the feature calculation unit includes: a speed calculation unit (31) that calculates a speed (A3) of the electric vehicle based on the position information acquired by the position information acquisition unit; an acceleration calculation unit (32) that calculates an acceleration (A4) of the electric vehicle from the speed calculated by the speed calculation unit; a current estimation unit (33) that estimates a current (B) of the battery from the speed calculated by the speed calculation unit and the acceleration calculated by the acceleration calculation unit; and an integrated current calculation unit (34) that time-integrates the current estimated by the current estimation unit to calculate an integrated current (D) that is the deterioration feature. [3] The battery state estimation system according to [2], wherein the feature amount calculation unit includes a gradient calculation unit (32a) that calculates a gradient related to the electric vehicle, and the current estimation unit estimates the current from the speed calculated by the speed calculation unit, the acceleration calculated by the acceleration calculation unit, and the gradient calculated by the gradient calculation unit. [4] The battery state estimation system according to [3], wherein the gradient calculation unit calculates the gradient based on an output value of an acceleration sensor (6) provided in the electric vehicle and a differential value of the speed (A3) of the electric vehicle. [5] The battery state estimation system according to any one of [2] to [4], wherein the integrated current calculation unit calculates the integrated current for at least one of charging and discharging of the battery.[6] The battery state estimation system according to any one of [1] to [5], wherein the information acquisition unit includes a date and time information acquisition unit (12) that acquires date and time information (A2) when the electric vehicle is running, and the feature calculation unit includes: an outside air temperature estimation unit (35) that estimates an outside air temperature (A5) based on the date and time information acquired by the date and time information acquisition unit; a battery temperature estimation unit (36) that estimates a battery temperature (C) of the battery from the outside air temperature estimated by the outside air temperature estimation unit; and a battery temperature degradation degree calculation unit (37) that calculates a battery temperature degradation degree (E), which is the degradation feature, from the battery temperature estimated by the battery temperature estimation unit. [7] The battery state estimation system according to [6], wherein the battery temperature estimation unit corrects the outside air temperature based on an offset amount (ΔC) to set the battery temperature as the battery temperature. [8] The battery state estimation system according to [7], wherein the battery temperature estimation unit sets the offset amount to a different value depending on the outside air temperature. [9] The battery state estimation system according to [7] or [8], wherein the battery temperature estimation unit sets the offset amount to a different value depending on the current (B) of the battery.

[10] The battery state estimation system according to any one of [6] to [9], wherein the battery temperature degradation degree calculation unit sets the battery temperature degradation degree to a sum of values ​​obtained by multiplying each frequency (N1 to N9) in a frequency distribution diagram (M1) showing the frequency distribution of the battery temperature by individually set degradation contribution coefficients (α1 to α9).

[11] The battery state estimation system according to [1], wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires a speed (A3) of the electric vehicle; and an acceleration acquisition unit (14) that acquires an acceleration (A4) of the electric vehicle; and the feature calculation unit comprises: a current estimation unit (33) that estimates a current (B) of the battery from the speed acquired by the speed acquisition unit and the acceleration acquired by the acceleration acquisition unit; and an integrated current calculation unit (34) that calculates an integrated current (D), which is the deterioration feature, by integrating the current estimated by the current estimation unit over time.

[12] The battery state estimation system according to any one of [1] to

[12] , wherein the information acquisition unit includes a gradient acquisition unit (16) that acquires a gradient (A6) related to the electric vehicle, and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit, the acceleration acquired by the acceleration acquisition unit, and the gradient acquired by the gradient acquisition unit.

[13] The battery state estimation system according to any one of [1] to

[12] , wherein the information acquisition unit includes an outside air temperature acquisition unit (15) that acquires an outside air temperature (A5) of the electric vehicle, and the feature calculation unit includes: a battery temperature estimation unit (36) that estimates a battery temperature (C) of the battery from the outside air temperature acquired by the outside air temperature acquisition unit, and a battery temperature degradation degree calculation unit (37) that calculates a battery temperature degradation degree (E), which is the degradation feature amount, from the battery temperature estimated by the battery temperature estimation unit.

[14] The battery state estimation system according to [1], wherein the information acquisition unit includes a position information acquisition unit (11) that acquires position information (A1) of the electric vehicle, and the feature calculation unit includes: a speed calculation unit (31) that calculates a speed (A3) of the electric vehicle based on the position information acquired by the position information acquisition unit; an acceleration calculation unit (32) that calculates an acceleration (A4) of the electric vehicle from the speed calculated by the speed calculation unit; a traveling distance calculation unit (38) that calculates a traveling distance (F) that is the deterioration feature amount from the speed calculated by the speed calculation unit; and an acceleration degradation degree calculation unit (39) that calculates an acceleration degradation degree (G) that is the deterioration feature amount from the acceleration calculated by the acceleration calculation unit.

[15] The battery state estimation system according to

[14] , wherein the acceleration degradation degree calculation unit calculates the acceleration degradation degree by adding up the values ​​obtained by multiplying each frequency (N11 to N19) in a frequency distribution diagram (M2) showing the frequency distribution of the absolute values ​​of the acceleration by individually set degradation contribution coefficients (β1 to β9).

[16] The battery state estimation system according to any one of [1] to

[15] , wherein the information acquisition unit includes a date and time information acquisition unit (12) that acquires date and time information (A2) when the electric vehicle is running, and the feature calculation unit includes an outside air temperature estimation unit (35) that estimates an outside air temperature (A5) based on the date and time information acquired by the date and time information acquisition unit, and an outside air temperature degradation degree calculation unit (40) that calculates an outside air temperature degradation degree (H), which is the degradation feature amount, from the outside air temperature estimated by the outside air temperature estimation unit.

[17] The battery state estimation system according to

[16] , wherein the outside air temperature degradation degree calculation unit sets the outside air temperature degradation degree to a sum of values ​​obtained by multiplying each frequency (N21 to N29) in a frequency distribution diagram (M3) that shows a frequency distribution of the outside air temperature by individually set degradation contribution coefficients (γ1 to γ9).

[18] The battery state estimation system according to any one of [1] to

[18] , wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires a speed (A3) of the electric vehicle, and an acceleration acquisition unit (14) that acquires an acceleration (A4) of the electric vehicle, and the feature amount calculation unit comprises: a traveled distance calculation unit (38) that calculates a traveled distance (F) that is the degradation feature amount from the speed acquired by the speed acquisition unit, and an acceleration degradation degree calculation unit (39) that calculates an acceleration degradation degree (G) that is the degradation feature amount from the acceleration acquired by the acceleration acquisition unit.

[19] The battery state estimation system according to any one of [1] to

[18] , wherein the information acquisition unit comprises: an outside air temperature acquisition unit (15) that acquires an outside air temperature (A5) of the electric vehicle, and the feature amount calculation unit comprises: an outside air temperature degradation degree calculation unit (40) that calculates an outside air temperature degradation degree (H) that is the degradation feature amount from the outside air temperature acquired by the outside air temperature acquisition unit.

[20] The battery state estimation system according to any one of [1] to

[19] , wherein the battery degradation estimation unit includes: a model generation unit (61) that generates, by machine learning, a degradation model formula for estimating the degradation state of the battery based on the degradation feature amount; and a degradation calculation unit (62) that calculates a degradation level index of the battery using the degradation model formula generated by the model generation unit.

[21] The battery state estimation system according to

[20] , further comprising: a vehicle inside information acquisition unit (50) that acquires vehicle inside information (J) from the electric vehicle in a stopped state, wherein the deterioration calculation unit calculates the deterioration level index based on the deterioration feature calculated by the feature calculation unit and the vehicle inside information acquired by the vehicle inside information acquisition unit.

[22] The battery state estimation system according to any one of [1] to

[21] , further comprising: a feature complementation unit (71) that complements the deterioration feature for a period in which the information acquisition unit has not been able to acquire the non-battery information.

[23] A battery state estimation system (111) comprising: an information acquisition unit (10, 10A, 10B, 10C, 10D, 10E, 10F, 10G) that acquires non-battery information (A1, A2, A3, A4, A5, A6) while an electric vehicle (1) is traveling; a current estimation unit (33) that estimates a current (B) of a battery (2) mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and an SOC estimation unit (72) that estimates an SOC of the battery based on the current estimated by the current estimation unit.

[24] The battery state estimation system according to

[23] , wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires a speed (A3) of the electric vehicle; and an acceleration acquisition unit (14) that acquires an acceleration (A4) of the electric vehicle, and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit and the acceleration acquired by the acceleration acquisition unit.

[25] The battery state estimation system according to

[24] , wherein the information acquisition unit includes a gradient calculation unit (32a) that calculates a gradient (A6) related to the electric vehicle, and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit, the acceleration acquired by the acceleration acquisition unit, and the gradient calculated by the gradient calculation unit.

[26] The battery state estimation system according to

[25] , wherein the gradient calculation unit calculates the gradient based on an output value of an acceleration sensor (6) provided on the electric vehicle and a differential value of the speed (A3) of the electric vehicle.

Claims

1. A battery state estimation system (101, 102, 103, 104, 105, 106, 107, 108, 109, 110) comprising: an information acquisition unit (10, 10A, 10B, 10C, 10D, 10E, 10F, 10G) that acquires non-battery information (A1, A2, A3, A4, A5, A6) while an electric vehicle (1) is traveling; a feature calculation unit (30, 30A, 30B, 30C, 30D, 30E, 30F, 30G) that calculates deterioration feature quantities (D, E, F, G, H) related to deterioration of a battery (2) mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and a battery deterioration estimation unit (60) that estimates a deterioration state of the battery based on the deterioration feature quantities calculated by the feature calculation unit.

2. The battery state estimation system of claim 1, wherein the information acquisition unit comprises a position information acquisition unit (11) that acquires position information (A1) of the electric vehicle, and the feature calculation unit comprises: a speed calculation unit (31) that calculates a speed (A3) of the electric vehicle based on the position information acquired by the position information acquisition unit; an acceleration calculation unit (32) that calculates an acceleration (A4) of the electric vehicle from the speed calculated by the speed calculation unit; a current estimation unit (33) that estimates a current (B) of the battery from the speed calculated by the speed calculation unit and the acceleration calculated by the acceleration calculation unit; and an integrated current calculation unit (34) that time-integrates the current estimated by the current estimation unit to calculate an integrated current (D) that is the deterioration feature.

3. A battery state estimation system as described in claim 2, wherein the feature calculation unit includes a gradient calculation unit (32a) that calculates a gradient related to the electric vehicle, and the current estimation unit estimates the current from the speed calculated by the speed calculation unit, the acceleration calculated by the acceleration calculation unit, and the gradient calculated by the gradient calculation unit.

4. A battery state estimation system as described in claim 3, wherein the gradient calculation unit calculates the gradient based on the output value of an acceleration sensor (6) provided on the electric vehicle and the differential value of the speed (A3) of the electric vehicle.

5. The battery state estimation system according to claim 2, wherein the integrated current calculation unit calculates the integrated current for at least one of charging and discharging of the battery.

6. A battery state estimation system according to any one of claims 1 to 5, wherein the information acquisition unit comprises a date and time information acquisition unit (12) that acquires date and time information (A2) when the electric vehicle is running, and the feature calculation unit comprises: an outside air temperature estimation unit (35) that estimates an outside air temperature (A5) based on the date and time information acquired by the date and time information acquisition unit; a battery temperature estimation unit (36) that estimates a battery temperature (C) of the battery from the outside air temperature estimated by the outside air temperature estimation unit; and a battery temperature degradation degree calculation unit (37) that calculates a battery temperature degradation degree (E), which is the degradation feature, from the battery temperature estimated by the battery temperature estimation unit.

7. The battery state estimation system according to claim 6, wherein the battery temperature estimation unit corrects the outside air temperature based on an offset amount (ΔC) to obtain the battery temperature.

8. The battery state estimation system according to claim 7, wherein the battery temperature estimation unit sets the offset amount to a different value depending on the outside air temperature.

9. The battery state estimation system according to claim 7, wherein the battery temperature estimation unit sets the offset amount to a different value depending on the current (B) of the battery.

10. A battery state estimation system as described in claim 6, wherein the battery temperature degradation degree calculation unit determines the battery temperature degradation degree as the sum of values ​​obtained by multiplying each frequency (N1 to N9) in a frequency distribution diagram (M1) showing the frequency distribution of the battery temperature by an individually set degradation contribution coefficient (α1 to α9).

11. The battery state estimation system of claim 1, wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires the speed (A3) of the electric vehicle; and an acceleration acquisition unit (14) that acquires the acceleration (A4) of the electric vehicle; and the feature calculation unit comprises: a current estimation unit (33) that estimates the current (B) of the battery from the speed acquired by the speed acquisition unit and the acceleration acquired by the acceleration acquisition unit; and an integrated current calculation unit (34) that calculates the integrated current (D), which is the deterioration feature, by integrating the current estimated by the current estimation unit over time.

12. A battery state estimation system as described in claim 11, wherein the information acquisition unit includes a gradient acquisition unit (16) that acquires a gradient (A6) related to the electric vehicle, and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit, the acceleration acquired by the acceleration acquisition unit, and the gradient acquired by the gradient acquisition unit.

13. A battery state estimation system as described in any one of claims 1 to 5 and 12, wherein the information acquisition unit comprises an outside air temperature acquisition unit (15) that acquires an outside air temperature (A5) of the electric vehicle, and the feature calculation unit comprises a battery temperature estimation unit (36) that estimates a battery temperature (C) of the battery from the outside air temperature acquired by the outside air temperature acquisition unit, and a battery temperature degradation degree calculation unit (37) that calculates a battery temperature degradation degree (E), which is the degradation feature, from the battery temperature estimated by the battery temperature estimation unit.

14. The battery state estimation system of claim 1, wherein the information acquisition unit comprises a position information acquisition unit (11) that acquires position information (A1) of the electric vehicle, and the feature calculation unit comprises: a speed calculation unit (31) that calculates a speed (A3) of the electric vehicle based on the position information acquired by the position information acquisition unit; an acceleration calculation unit (32) that calculates an acceleration (A4) of the electric vehicle from the speed calculated by the speed calculation unit; a traveling distance calculation unit (38) that calculates a traveling distance (F), which is the deterioration feature, from the speed calculated by the speed calculation unit; and an acceleration degradation degree calculation unit (39) that calculates an acceleration degradation degree (G), which is the deterioration feature, from the acceleration calculated by the acceleration calculation unit.

15. A battery state estimation system as described in claim 14, wherein the acceleration degradation degree calculation unit determines the acceleration degradation degree as the sum of values ​​obtained by multiplying each frequency (N11 to N19) in a frequency distribution diagram (M2) showing the frequency distribution of the absolute values ​​of the acceleration by individually set degradation contribution coefficients (β1 to β9).

16. A battery state estimation system as described in claim 1 or 14, wherein the information acquisition unit includes a date and time information acquisition unit (12) that acquires date and time information (A2) when the electric vehicle is running, and the feature calculation unit includes an outside air temperature estimation unit (35) that estimates an outside air temperature (A5) based on the date and time information acquired by the date and time information acquisition unit, and an outside air temperature degradation degree calculation unit (40) that calculates an outside air temperature degradation degree (H), which is the degradation feature, from the outside air temperature estimated by the outside air temperature estimation unit.

17. A battery state estimation system as described in claim 16, wherein the outside air temperature degradation degree calculation unit determines the outside air temperature degradation degree as the sum of values ​​obtained by multiplying each frequency (N21 to N29) in a frequency distribution diagram (M3) showing the frequency distribution of the outside air temperature by an individually set degradation contribution coefficient (γ1 to γ9).

18. A battery state estimation system as described in claim 1, wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires the speed (A3) of the electric vehicle; and an acceleration acquisition unit (14) that acquires the acceleration (A4) of the electric vehicle; and the feature calculation unit comprises: a travel distance calculation unit (38) that calculates the travel distance (F), which is the deterioration feature, from the speed acquired by the speed acquisition unit; and an acceleration degradation degree calculation unit (39) that calculates the acceleration degradation degree (G), which is the deterioration feature, from the acceleration acquired by the acceleration acquisition unit.

19. A battery state estimation system as described in claim 1, 14 or 18, wherein the information acquisition unit includes an outside air temperature acquisition unit (15) that acquires the outside air temperature (A5) of the electric vehicle, and the feature calculation unit includes an outside air temperature degradation degree calculation unit (40) that calculates the outside air temperature degradation degree (H), which is the degradation feature, from the outside air temperature acquired by the outside air temperature acquisition unit.

20. A battery state estimation system as described in any one of claims 1 to 5, wherein the battery deterioration estimation unit comprises: a model generation unit (61) that generates a deterioration model formula by machine learning to estimate the deterioration state of the battery based on the deterioration feature; and a deterioration calculation unit (62) that calculates a deterioration level index of the battery using the deterioration model formula generated by the model generation unit.

21. A battery state estimation system as described in claim 20, comprising a vehicle internal information acquisition unit (50) that acquires vehicle internal information (J) from the electric vehicle in a stopped state, and the deterioration calculation unit calculates the deterioration level index based on the deterioration feature calculated by the feature calculation unit and the vehicle internal information acquired by the vehicle internal information acquisition unit.

22. A battery state estimation system as described in any one of claims 1 to 5, further comprising a feature complementing unit (71) that complements the deterioration feature for an unacquired period in which the information acquisition unit is unable to acquire the non-battery information.

23. A battery state estimation system (111) comprising: an information acquisition unit (10, 10A, 10B, 10C, 10D, 10E, 10F, 10G) that acquires non-battery information (A1, A2, A3, A4, A5, A6) while an electric vehicle (1) is running; a current estimation unit (33) that estimates a current (B) of a battery (2) mounted on the electric vehicle based on the non-battery information acquired by the information acquisition unit; and an SOC estimation unit (72) that estimates an SOC of the battery based on the current estimated by the current estimation unit.

24. A battery state estimation system as described in claim 23, wherein the information acquisition unit comprises: a speed acquisition unit (13) that acquires the speed (A3) of the electric vehicle; and an acceleration acquisition unit (14) that acquires the acceleration (A4) of the electric vehicle; and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit and the acceleration acquired by the acceleration acquisition unit.

25. A battery state estimation system as described in claim 24, wherein the information acquisition unit includes a gradient calculation unit (32a) that calculates a gradient (A6) related to the electric vehicle, and the current estimation unit estimates the current from the speed acquired by the speed acquisition unit, the acceleration acquired by the acceleration acquisition unit, and the gradient calculated by the gradient calculation unit.

26. A battery state estimation system as described in claim 25, wherein the gradient calculation unit calculates the gradient based on the output value of an acceleration sensor (6) provided in the electric vehicle and the differential value of the speed (A3) of the electric vehicle.

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