Method, apparatus and computer program product for estimating battery state of health

Abstract

The application discloses a method, an apparatus and a computer program product for estimating a state of health of a battery. The invention provides a method for estimating the health state of a battery, which is characterized by comprising the following steps of: acquiring a plurality of historical charging condition data groups of a plurality of sample batteries and a health state SOH value corresponding to each historical charging condition data group, the historical charging condition data set comprises the charging current, the voltage of the battery, the temperature of the battery and moments associated with the charging current, the voltage and the temperature; and acquiring a current charging condition data set of the battery to be tested, and estimating a current SOH value of the battery to be tested by using a data model which can establish internal association between a plurality of historical charging condition data sets and the SOH value, the current charging condition data set comprises the charging current, the voltage of the battery, the temperature of the battery and moments associated with the charging current, the voltage and the temperature. The method of the present disclosure can easily estimate the state of health of the battery.

Description

Technical Field

[0001] The present application relates to the field of batteries, and in particular to a method, device, and computer program product for estimating the health state of a battery. Background Art

[0002] The battery health status is crucial to the safe operation of the battery management system. As the battery ages, the battery capacity continues to decrease, the capacity charged in the same SOC (remaining power) range decreases, the battery's dynamic performance decreases, and the charge and discharge power will decrease accordingly. Only by evaluating the accurate battery health status can we calculate the accurate SOC and match a more appropriate charge and discharge power, thereby exerting the best performance of the battery pack, improving the battery pack's endurance, and reducing the safety risks of overcharging and over-discharging.

[0003] The existing methods for estimating the battery health status still have limitations and shortcomings. Summary of the invention

[0004] The traditional method of estimating the health status of the battery requires the battery to be fully charged or fully discharged, and the two-point method derived from this, which is close to full charge and discharge, requires both the high and low points to be in a completely static state to determine the battery state of charge. This method is relatively simple and has high accuracy, but it uses fewer computers, that is, it is difficult to estimate the health status of the battery under general usage conditions.

[0005] In order to solve the above problems, the present application provides a method, an apparatus and a computer program product for estimating the health status of a battery.

[0006] In one aspect, a method for estimating a battery state of health is provided, comprising: obtaining multiple historical charging condition data groups of multiple sample batteries and a state of health SOH value corresponding to each historical charging condition data group, wherein the historical charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and obtaining a current charging condition data group of a battery to be tested, and using a data model to estimate the current SOH value of the battery to be tested, wherein the data model is capable of establishing an intrinsic association between the multiple historical charging condition data groups and the state of health SOH value, wherein the current charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature, wherein the method further comprises: calculating a capacity increment curve corresponding to each historical charging condition data group; extracting multiple characteristic parameters of each capacity increment curve; and inputting the multiple characteristic parameters and the SOH value corresponding to each historical charging condition data group into the data model to train the data model, wherein the multiple characteristic parameters include parameters of one or more peaks of the capacity increment curve in the process of changing with voltage.

[0007] In one aspect, a computer program product is provided, comprising computer executable instructions, which when executed by one or more processors cause the one or more processors to perform the method as described above.

[0008] In one aspect, a device for estimating the state of health of a battery is provided, the device comprising: a memory having instructions stored thereon; and a processor configured to execute the instructions stored on the memory to: obtain a plurality of historical charging condition data groups of a plurality of sample batteries and a state of health SOH value corresponding to each historical charging condition data group, the historical charging condition data group comprising a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and obtain a current charging condition data group of a battery to be tested, and use a data model to estimate the current SOH value of the battery to be tested, the data model being capable of establishing the plurality of The intrinsic correlation between the historical charging condition data group and the health state SOH value, the current charging condition data group includes the charging current, the battery voltage, the battery temperature and the time associated with the charging current, voltage and temperature, and the processor is further configured to: calculate the capacity increment curve corresponding to each historical charging condition data group; extract multiple characteristic parameters of each capacity increment curve; input the multiple characteristic parameters corresponding to each historical charging condition data group and the SOH value into the data model to train the data model, wherein the multiple characteristic parameters include parameters of one or more peaks of the capacity increment curve in the process of changing with voltage.

[0009] The method, device, and computer program product for estimating the battery health state disclosed in the present invention enable estimation of the battery health state for general usage conditions, thereby greatly increasing the applicable scenarios of the estimation method, making it easier to estimate the battery health state.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings: Figure 1Examples of multiple capacity increment curves of a battery at different SOH during charging are shown.

[0012] Figure 2 A schematic diagram showing the peak height, peak position and half-peak area of ​​the peak on the capacity increment curve. DETAILED DESCRIPTION

[0013] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0015] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0018] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0019] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0020] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0021] Explanation of terms 1. SOC: State Of Charge (SOC for short) refers to the ratio of available power in the battery to the nominal capacity, and is an important monitoring data of the battery management system. The battery management system can control the working state of the battery according to the SOC value. The remaining power of the battery also reflects the state of charge of the battery.

[0022] 2. SOH: battery state of health, in this article, for example, refers to the percentage of the battery's current capacity to its factory capacity; 3. ICA: Incremental capacity analysis refers to a method of estimating or predicting battery capacity attenuation by analyzing the relationship between the incremental change in battery capacity and the charging voltage during constant current charging.

[0023] In the prior art, the traditional capacity definition method requires the battery to be tested in the full range (i.e., SOC range or SOC variation range) of full charge and discharge, which is a relatively harsh condition. Even the improved two-point method requires a large SOC variation range and static conditions, and the computer has fewer opportunities (i.e., the conditions that meet the required calculation conditions of the algorithm for calculating the battery's health status, thereby outputting the battery health status SOH result).

[0024] In addition, the existing capacity increment curve method in the prior art does not consider the impact of different charging conditions on the ICA curve, resulting in fewer coverage scenarios in actual application, and does not consider the differences in the characteristic parameters of the ICA curve caused by individual differences in different batteries, which leads to failure cases with large errors in the health status estimation results of individual batteries. For example, without considering the individual differences of batteries (material differences, aging path differences, etc.), the estimated battery capacity results under the same characteristic parameters may be biased.

[0025] The traditional method of estimating the health status of the battery requires the battery to be fully charged or fully discharged, and the two-point method derived from this, which is close to full charge and discharge, requires both the high and low points to be in a completely static state to determine the battery state of charge. This method is relatively simple and has high accuracy, but it requires less computer time.

[0026] In addition, the capacity increment method in the prior art does not consider the impact of different starting voltages, different temperatures or different charging currents on the ICA curve, and the obtained ICA curve will be affected by these factors and produce deviations. In addition, it does not consider the individual differences of batteries, resulting in different estimated battery capacity results under the same characteristic parameters.

[0027] In order to solve one or more problems in the prior art, this paper proposes a method for estimating the health state of a battery, which has high applicability and can easily estimate the health state of a battery.

[0028] In one embodiment, the present disclosure proposes a method for estimating the state of health of a battery, comprising: obtaining multiple historical charging condition data groups of multiple sample batteries and a state of health SOH value corresponding to each historical charging condition data group, wherein the historical charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and obtaining a current charging condition data group of a battery to be tested, and using a data model to estimate the current SOH value of the battery to be tested, wherein the data model can establish an intrinsic correlation between the multiple historical charging condition data groups and the state of health SOH value, wherein the current charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature.

[0029] Since the present disclosure can establish an intrinsic relationship between multiple historical charging condition data groups and the health state SOH value through a data model, the technical solution does not need to perform a full-range test of the battery with full charge and discharge, and can estimate the SOH value based on historical charging condition data in a smaller SOC change range.

[0030] In one embodiment, historical charging condition data sets and health status SOH values ​​corresponding to each historical charging condition data set have been obtained from the sample battery, and these data sets and SOH values ​​are previously obtained through actual measurement. The data model can estimate the current SOH value of the battery to be tested (for example, the battery currently in actual use) based on these existing data sets and SOH values.

[0031] In one embodiment, one of the multiple sample batteries may be the battery to be tested itself, because the historical data of the battery to be tested itself may also be used to enable the data model to establish an intrinsic correlation between multiple historical charging condition data groups and the health state SOH value, thereby estimating the SOH value of the battery to be tested based on this correlation.

[0032] The historical charging condition data set includes the values ​​of the charging current, battery voltage and battery temperature measured multiple times at different times during the battery charging process, as well as the time corresponding to each value in the process of charging current, voltage and temperature change (i.e., the time when the value appears). For example, the time format can be xxxx year xx month xx day xx hour xx minute xx second.

[0033] In one embodiment, the historical charging condition data set of the sample battery may include the device number of the battery (or battery pack).

[0034] In one embodiment, optionally, the method may also include: calculating a capacity increment curve corresponding to each historical charging condition data group; extracting multiple characteristic parameters of each capacity increment curve; and inputting the multiple characteristic parameters corresponding to each historical charging condition data group and the SOH value into a data model to train the data model.

[0035] Through the above training, the data model can further establish the intrinsic relationship between multiple historical charging condition data groups and the health status SOH value.

[0036] Figure 1 1 shows an example of multiple capacity increment curves of a battery at different SOH during charging, where the abscissa represents the voltage of the battery and the ordinate represents the capacity increment of the battery. Figure 1 As shown, under different SOH, the capacity increment curve of the battery during constant current charging is different. For example, the position of the peak value of the capacity increment as the battery voltage increases, the height of the peak value, etc. are different. The characteristics of the capacity increment curve are not limited to the characteristics related to the peak or peak value, and can also include, for example, the characteristics related to the gradient change of the capacity increment curve, the envelope area, etc.

[0037] In one embodiment, optionally, each historical charging condition data set may be obtained under a charging condition where the battery's remaining power variation interval is greater than a specific percentage.

[0038] The remaining power variation interval refers to the difference between the remaining power percentage at the end of charging and the remaining power percentage at the beginning of charging during the charging process. The larger the difference, the closer the charging process is to the full-range charging condition of the battery, which is full discharge and then full charge, and the corresponding historical charging condition data group makes it easier for the data model to establish the above-mentioned intrinsic relationship more quickly and accurately. It should be emphasized that "the remaining power variation interval is greater than a specific percentage" is not necessary, because even if the remaining power variation interval is small, the data model can establish the above-mentioned intrinsic relationship through more historical charging condition data groups. Therefore, accuracy can be improved by increasing the data group.

[0039] The specific percentage may be a value appropriately selected by those skilled in the art according to actual conditions and technical requirements when implementing the technology disclosed herein. For example, the specific percentage may be any value selected from 5% to 100%, for example, 30%, 40%, 60%, 70%, etc.

[0040] In one embodiment, calculating the capacity increment curve corresponding to each historical charging condition data group may include: before calculating the capacity increment curve, performing Gaussian filtering on the voltage data in each historical charging condition data group.

[0041] Specifically, all historical operating condition charging voltage data are Gaussian filtered to reduce the impact of voltage sampling noise on the results. As an example, the following formula can be used as a one-dimensional Gaussian filter formula: (1) Where x is the input data to be filtered, and σ is the standard deviation of the Gaussian distribution, which represents the degree of discreteness of the data. The larger σ is, the more discrete the distribution is, the greater the filtering intensity is, and the flatter the curve is. For example, σ can be set adaptively according to the sampling interval of the input data.

[0042] In one embodiment, calculating the capacity increment curve corresponding to each historical charging condition data group includes: calculating the capacity increment corresponding to each voltage interval window; and performing Gaussian filtering on the capacity increment.

[0043] Specifically, for example, the capacity increment can be calculated using an equal voltage interval method, that is, the charging voltage is slid according to the set voltage interval window ΔV, and the ampere-time integral change I*Δt within each window ΔV is calculated. For the calculated capacity increment data, the capacity increment data is filtered using a Gaussian filter to make the capacity increment data smoother. The overall shape of the capacity increment (IC) curve is as follows: Figure 1 shown.

[0044] IC curve calculation formula: (2) in, It represents the capacity increment result calculated in the kth voltage interval window during the charging process. I represents the charging current and V represents the charging voltage. and are the upper and lower limits of the voltage interval window, the difference between the two is ΔV, dt represents the charging time in this voltage interval window. Q represents the charge, is the increment of charge in this voltage interval window.

[0045] In one embodiment, the plurality of characteristic parameters may include parameters of one or more peaks in a capacity increment curve as the capacity increment curve changes with voltage.

[0046] like Figure 1 As shown, each capacity increment curve has one or more peaks (peak values) in the process of changing with voltage. The parameters of the one or more peaks may include one or more of the following: peak height, peak position, peak area, battery temperature at the peak position, battery temperature at the peak position, and peak half-peak area.

[0047] For example, in Figure 1 In the example shown, there are three peaks, which are called the first peak ( Figure 1 Peak 1 in the middle), the second peak ( Figure 1 Peak 2 in the Figure 1 The number of peaks is different in different types of batteries, so the number of peaks can be any number.

[0048] In one embodiment, the plurality of characteristic parameters include one or more of the following parameters related to the second peak and the third peak of the capacity increment curve in the process of changing with voltage: peak height of the second peak, position of the second peak, area of ​​the second peak, peak height of the third peak, position of the third peak, area of ​​the third peak, temperature of the battery at the position of the second peak, temperature of the battery at the position of the third peak, half-peak area of ​​the second peak, half-peak area of ​​the third peak.

[0049] Figure 2 A schematic diagram showing the peak height, peak position and half peak area of ​​the peak on the capacity increment curve. The peak position refers to the voltage of the battery when the peak reaches its peak value. Figure 2 This is just a schematic diagram of the peak curve shape. The peak curve shape of the actual capacity increment curve is different from Figure 2The examples shown may be different, but the calculation methods of the peak area or half-peak area of ​​a peak are not limited to a specific peak curve shape, and these calculation methods are well known.

[0050] In one embodiment, the plurality of characteristic parameters may further include one or more of a charging start voltage, a charging start temperature, and a charging current average value of the battery.

[0051] In one embodiment, the battery is charged in a constant current charging mode. However, the constant current refers to a roughly constant current, so in the case of a constant current, the charging current still has a slight fluctuation, so it is still necessary to calculate the average value of the constant current charging current.

[0052] In one embodiment, the method may also include: obtaining a historical charging condition data group of the battery to be tested under a charging condition where the battery's remaining power variation range is greater than the specific percentage and / or the charging time is within a specific time length before the charging time of the current charging condition data group, obtaining an SOH value corresponding to the historical charging condition data group, and using the SOH value as the current SOH value.

[0053] The historical charging condition data group described above refers to a charging condition data group that has been obtained through measurement during the previous use of the battery to be tested (for example, when the battery is a battery on an electric vehicle, it is the battery of the electric vehicle currently in use), and the SOH value of the battery is obtained corresponding to the charging condition data group.

[0054] When the battery's remaining power variation range is greater than the specific percentage, the battery's charging process is closer to a full-range charging condition of full discharge followed by full charge. Therefore, the corresponding SOH value obtained after the charging process is completed should be relatively accurate and can be used as the battery's current SOH value.

[0055] When the charging time of the historical charging condition is within a specific length of time (for example, one week, i.e., 7 days) before the charging time of the current charging condition data group, since the time interval is short, the historical charging condition data group and its corresponding SOH value are relatively new, so the SOH value can be used as the current SOH value of the battery.

[0056] When the above two conditions are met at the same time, it indicates that the historical charging condition data set and its corresponding SOH value are not only obtained under a full-range charging condition that is relatively close to full discharge and then full charge, but also obtained in the recent past. Therefore, this SOH value is more suitable for use as the current SOH value of the battery, so as to be used to correct the current SOH value estimated by the data model.

[0057] In one embodiment, the method may further include: counting the number of times the SOH value is obtained, and when the number is greater than a predetermined number, calculating the average of the deviation values ​​between the estimated current SOH value and the SOH value, and using the average to correct all estimated current SOH values ​​of the battery to be tested.

[0058] The predetermined number of times may be, for example, 3 times, or any other number of times. For example, if the mean value of the deviation value is 2%, the mean value of 2% may be used to correct all SOH results estimated for the battery to be tested by the data model.

[0059] Through the above method, the SOH result estimated by the data model can be corrected using the actual measured SOH obtained under the full range charging condition close to full discharge and then full charge and / or in the recent past, thereby further improving the estimation accuracy. In addition, through this method, the error of the capacity estimation result caused by other factors such as battery cell differences can also be reduced.

[0060] In one embodiment, the data model used in the present disclosure may include, but is not limited to, any one of the following models or a combination thereof: a linear regression model, a random forest, a support vector machine, a neural network model.

[0061] Just as an example, for example, in the case of using a linear regression model as a data model, the least squares method can be used to find (ie, train) an equation as a specific data model: (3) Wherein, X is the feature vector extracted by the data model from the input historical charging condition data group, and Y is the SOH value vector corresponding to the historical charging condition data group.

[0062] As an example, Y is the SOH value vector (y1, y2, ..., ym), X is the input ICA feature vector (x1, x2, ..., xm), and xm is a 13-dimensional variable , includes the second peak height, second peak position, second peak area, third peak height, third peak position, third peak area, charging start voltage, charging start temperature, charging current mean, second peak position temperature, third peak position temperature, second peak half-peak area, third peak half-peak area, w is the weight corresponding to the 13-dimensional feature, b is the intercept, w and b are the parameters to be regressed and fitted (i.e., obtained through training).

[0063] In the above example, each feature vector xm includes 13 feature parameters, but this is only an example. One or more feature parameters among the 13 feature parameters may be used, or one or more feature parameters other than the 13 feature parameters may be used.

[0064] After the above data model equation (3) is obtained by training using the feature vector X and the SOH value vector Y corresponding to the feature vector X, the current charging condition data group of the battery to be tested can be obtained, and the current SOH value of the battery to be tested can be estimated based on the current charging condition data group of the battery to be tested using the trained data model equation (3).

[0065] In one embodiment, after obtaining the current charging condition data group of the battery to be tested, the capacity increment curve corresponding to the current charging condition data group can be calculated, and multiple characteristic parameters of the capacity increment curve can be extracted. Before calculating the capacity increment curve, the voltage data in the current charging condition data group can be Gaussian filtered. When calculating the capacity increment curve corresponding to the current charging condition data group, the capacity increment corresponding to each voltage interval window can be calculated, and the capacity increment can be Gaussian filtered. The method for extracting multiple characteristic parameters of the capacity increment curve corresponding to the current charging condition data group can be, for example, the same as the above method for the historical charging condition data group.

[0066] In one embodiment, the current charging condition data group of the tested battery may include the device number of the tested battery (or battery pack).

[0067] The present disclosure also provides a computer program product, which includes computer executable instructions. When the computer executable instructions are executed by one or more processors, the one or more processors perform a method according to any one of the embodiments or combinations of embodiments described above.

[0068] The present disclosure also provides a device for estimating the state of health of a battery, which may include: a memory having instructions stored thereon; and a processor configured to execute the instructions stored on the memory to: obtain multiple historical charging condition data groups of multiple sample batteries and a state of health SOH value corresponding to each historical charging condition data group, wherein the historical charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and obtain a current charging condition data group of a battery to be tested, and use a data model to estimate the current SOH value of the battery to be tested, wherein the data model can establish an intrinsic association between the multiple historical charging condition data groups and the state of health SOH value, wherein the current charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature.

[0069] The processor of the apparatus for estimating the state of health of a battery may also be configured to execute instructions stored in the memory to perform steps or operations in the method according to any one of the embodiments or combinations of embodiments described above.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for estimating a battery health state, characterized in that: include: Acquire multiple historical charging condition data groups of multiple sample batteries and a health state SOH value corresponding to each historical charging condition data group, wherein the historical charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and Obtain a current charging condition data group of the battery to be tested, and use a data model to estimate the current SOH value of the battery to be tested, wherein the data model can establish an intrinsic association between the multiple historical charging condition data groups and the health state SOH value, and the current charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature. Wherein, the method further comprises: Calculate the capacity increment curve corresponding to each historical charging condition data group; Extract multiple characteristic parameters of each capacity increment curve; inputting the plurality of characteristic parameters and the SOH value corresponding to each historical charging condition data group into a data model to train the data model, The plurality of characteristic parameters include parameters of one or more peaks in the process of the capacity increment curve changing with voltage.

2. The method according to claim 1, characterized in that Each historical charging condition data group is obtained under a charging condition in which the variation range of the remaining power of the battery is greater than a specific percentage.

3. The method according to claim 1, characterized in that The calculating of the capacity increment curve corresponding to each historical charging condition data group includes: before calculating the capacity increment curve, performing Gaussian filtering on the voltage data in each historical charging condition data group.

4. The method according to claim 1, characterized in that The calculating of the capacity increment curve corresponding to each historical charging condition data group comprises: calculating a capacity increment corresponding to each voltage interval window; and Gaussian filtering is performed on the capacity increment.

5. The method according to claim 1, characterized in that The multiple characteristic parameters include one or more of the following parameters related to the second peak and the third peak of the capacity increment curve in the process of changing with voltage: the peak height of the second peak, the position of the second peak, the area of ​​the second peak, the peak height of the third peak, the position of the third peak, the area of ​​the third peak, the temperature of the battery at the position of the second peak, the temperature of the battery at the position of the third peak, the half-peak area of ​​the second peak, and the half-peak area of ​​the third peak.

6. The method according to claim 1, characterized in that The plurality of characteristic parameters include one or more of a charging start voltage, a charging start temperature and a charging current average value of the battery.

7. The method according to claim 1, characterized in that The method further comprises: Obtain a historical charging condition data group of the battery to be tested under a charging condition where the battery's remaining power variation range is greater than a specific percentage and / or the charging time is within a specific time length before the charging time of the current charging condition data group, obtain the SOH value corresponding to the historical charging condition data group, and use the SOH value as the current SOH value.

8. The method according to claim 7, characterized in that The method further comprises: The number of times the SOH value is obtained is counted, and when the number is greater than a predetermined number, a mean of deviations between the estimated current SOH value and the SOH value is calculated, and all estimated current SOH values ​​of the battery to be tested are corrected using the mean.

9. The method according to claim 1, characterized in that The data model includes any one of the following models or a combination thereof: a linear regression model, a random forest, a support vector machine, and a neural network model.

10. A computer program product comprising computer executable instructions, which, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 1 to 9.

11. A device for estimating a battery health state, characterized in that: The device comprises: a memory having instructions stored thereon; and a processor configured to execute instructions stored on the memory to: Acquire multiple historical charging condition data groups of multiple sample batteries and a health state SOH value corresponding to each historical charging condition data group, wherein the historical charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature; and Obtain a current charging condition data group of the battery to be tested, and use a data model to estimate the current SOH value of the battery to be tested, wherein the data model can establish an intrinsic association between the multiple historical charging condition data groups and the health state SOH value, and the current charging condition data group includes a charging current, a battery voltage, a battery temperature, and a time associated with the charging current, voltage, and temperature. Wherein, the processor is further configured to: Calculate the capacity increment curve corresponding to each historical charging condition data group; Extract multiple characteristic parameters of each capacity increment curve; inputting the plurality of characteristic parameters and the SOH value corresponding to each historical charging condition data group into a data model to train the data model, The plurality of characteristic parameters include parameters of one or more peaks in the process of the capacity increment curve changing with voltage.

Images