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

Abstract

The present application discloses a method, an apparatus, and a computer program product for estimating the state of health of a battery. There is provided a method for estimating the state of health of a battery, characterized by comprising: obtaining a plurality of historical charging condition data sets of a plurality of sample batteries and the state of health (SOH) values corresponding to each historical charging condition data set, the historical charging condition data set including a charging current, a voltage of the battery, a temperature of the battery, and a moment associated with the charging current, the voltage, and the temperature; and obtaining a current charging condition data set of a battery to be measured, and using a data model to estimate the current SOH value of the battery to be measured, the data model being capable of establishing an internal association between the plurality of historical charging condition data sets and the state of health (SOH) values, the current charging condition data set including a charging current, a voltage of the battery, a temperature of the battery, and a moment 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 particularly to a method, a device, and a computer program product for estimating the state of health of a battery. Background Art

[0002] The state of health of a battery is crucial for the safe operation of a battery management system. As the battery ages, the battery capacity continuously decreases, the capacity charged in the same SOC (state of charge) interval decreases, the kinetic performance of the battery deteriorates, and the charge and discharge power will accordingly decrease. Only by accurately evaluating the state of health of the battery can an accurate SOC be calculated and a more appropriate charge and discharge power be matched, thereby bringing out the best performance of the battery pack, improving the endurance of the battery pack, and reducing the safety risks of overcharging and over-discharging.

[0003] The existing methods for estimating the state of health of a battery still have limitations and deficiencies. Summary of the Invention

[0004] Traditional methods for estimating the state of health of a battery require full charge or full discharge conditions of the battery, as well as the two-point method derived therefrom that is close to full charge and full discharge. It is necessary for both the high and low points to be in a completely static state to determine the state of charge of the battery. This method is relatively simple and has high accuracy, but there are few opportunities for a computer, that is, it is difficult to estimate the state of health of a battery for general usage conditions.

[0005] To solve the above problems, the present application provides a method, a device, and a computer program product for estimating the state of health of a battery.

[0006] In one aspect, a method for estimating the state of health of a battery is provided, including: obtaining a plurality of historical charging condition data groups of a plurality of sample batteries and the state of health SOH values corresponding to each historical charging condition data group, where the historical charging condition data group includes a charging current, a voltage of the battery, a temperature of the battery, and a moment associated with the charging current, voltage, and temperature; and obtaining a current charging condition data group of a battery to be measured and using a data model to estimate the current SOH value of the battery to be measured, where the data model can establish an internal association between the plurality of historical charging condition data groups and the state of health SOH values, and the current charging condition data group includes a charging current, a voltage of the battery, a temperature of the battery, and a moment associated with the charging current, voltage, and temperature. Wherein, the method further includes: calculating a capacity increment curve corresponding to each historical charging condition data group; extracting a plurality of characteristic parameters of each capacity increment curve; and inputting the plurality of characteristic parameters corresponding to each historical charging condition data group and the SOH value into the data model to train the data model, where the plurality of characteristic parameters include parameters of one or more peaks in the process of the capacity increment curve changing with the voltage.

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

[0008] In one aspect, a device for estimating the state of health (SOH) of a battery is provided. The device includes: a memory storing instructions thereon; and a processor configured to execute the instructions stored on the memory to: obtain a plurality of historical charging condition data sets of a plurality of sample batteries and the corresponding SOH values of the health state for each historical charging condition data set, where the historical charging condition data set includes a charging current, a voltage of the battery, a temperature of the battery, and a time associated with the charging current, voltage, and temperature; and obtain a current charging condition data set of a battery to be measured and use a data model to estimate the current SOH value of the battery to be measured, where the data model can establish an internal correlation between the plurality of historical charging condition data sets and the SOH values of the health state, and the current charging condition data set includes a charging current, a voltage of the battery, a temperature of the battery, and a time associated with the charging current, voltage, and temperature. The processor is further configured to: calculate a capacity increment curve corresponding to each historical charging condition data set; extract a plurality of characteristic parameters of each capacity increment curve; and input the plurality of characteristic parameters corresponding to each historical charging condition data set and the SOH value into the data model to train the data model, where the plurality of characteristic parameters include parameters of one or more peaks in the process of the capacity increment curve changing with the voltage.

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

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. And in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0012] Figure 1Shows an example of multiple capacity increment curves of a battery at different SOHs during charging.

[0013] Figure 2 Shows a schematic diagram of the peak height, peak position, and half-peak area of the peak on the capacity increment curve. Detailed implementation mode

[0014] The embodiments of the technical solution of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, so they are only examples and cannot be used to limit the protection scope of the present application.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled 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" and any variations thereof in the specification and claims of this application and the above accompanying drawings are intended to cover non-exclusive inclusion.

[0016] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order, or primary-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.

[0017] Referring to "embodiment" herein means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0018] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0019] 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).

[0020] In the description of the embodiments of the present application, the orientation or positional relationship indicated by technical terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the embodiments of the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the embodiments of the present application.

[0021] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside 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 specific situations.

[0022] Term Explanation

[0023] 1. SOC: State Of Charge, which refers to the proportion of the available power in the battery to the nominal capacity. It 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.

[0024] 2. SOH: state of health of the battery. In this article, for example, it refers to the percentage of the current capacity of the battery to the factory capacity;

[0025] 3. ICA: incremental capacity analysis, which refers to a method of estimating or evaluating the capacity attenuation of a battery by analyzing the relationship between the change in the capacity increment of the battery and the charging voltage during the constant current charging process.

[0026] In the prior art, the traditional capacity definition method requires testing the battery in the full range of full charge and full discharge (i.e., the SOC range or the SOC change range), and the conditions are relatively harsh. Even the improved two-point method requires a working condition with a large SOC change range and a static condition, and there are fewer computer conditions (i.e., working conditions that meet the required calculation conditions for the algorithm for calculating the state of health of the battery, and thus can output the state of health SOH result of the battery).

[0027] In addition, the existing capacity increment curve method in the prior art does not consider the influence of different charging conditions on the ICA curve, resulting in fewer covered scenarios in the actual application process. It also does not consider the differences in the characteristic parameters of the ICA curve caused by different battery individual differences, and thus there are failure cases with relatively large errors in the estimation results of the health state of individual batteries. For example, without considering the individual differences of batteries (such as material differences and aging path differences), it may lead to deviations in the estimated battery capacity results under the same characteristic parameters.

[0028] Traditional methods for estimating the health state of batteries require full charge or full discharge conditions of the battery, and the two-point method derived therefrom, which is close to full charge and full discharge, requires both high and low points to be in a completely static state to determine the state of charge of the battery. This method is relatively simple and has high accuracy, but it requires less computing power.

[0029] In addition, the capacity increment method in the prior art does not consider the influence of different starting voltages, different temperatures, or different charging currents on the ICA curve, and the obtained ICA curve will be deviated due to the influence of these factors. Moreover, it does not consider the individual differences of batteries, resulting in different estimated battery capacity results under the same characteristic parameters.

[0030] To solve one or more problems in the prior art, this paper proposes a method for estimating the health state of batteries, which has high applicability and can easily estimate the health state of batteries.

[0031] In one embodiment, the present disclosure proposes a method for estimating the health state of batteries, including: obtaining a plurality of historical charging condition data groups of a plurality of sample batteries and the corresponding state of health (SOH) values for each historical charging condition data group, where the historical charging condition data group includes charging current, the voltage of the battery, the temperature of the battery, and the time associated with the charging current, voltage, and temperature; and obtaining the current charging condition data group of the battery to be tested, and using a data model to estimate the current SOH value of the battery to be tested, where the data model can establish an internal association between the plurality of historical charging condition data groups and the state of health (SOH) values, and the current charging condition data group includes charging current, the voltage of the battery, the temperature of the battery, and the time associated with the charging current, voltage, and temperature.

[0032] Since the present disclosure can establish an internal association between a plurality of historical charging condition data groups and the state of health (SOH) values through a data model, this technical solution does not require a full-range test of full charge and full discharge of the battery, and can estimate the SOH value based on the historical charging condition data in a relatively small SOC change interval.

[0033] In one embodiment, a historical charging condition data set of a sample battery and a state of health (SOH) value corresponding to each historical charging condition data set have been obtained from the sample battery, and these data sets and SOH values were previously obtained through actual measurements. The data model can estimate the current SOH value of a battery under test (e.g., a battery currently in actual use) based on these existing data sets and SOH values.

[0034] In one embodiment, one of the multiple sample batteries can be the battery under test itself, because the historical data of the battery under test itself can also be used to enable the data model to establish an internal correlation between multiple historical charging condition data sets and the state of health SOH value, so as to estimate the SOH value of the battery under test based on this correlation.

[0035] The historical charging condition data set includes values of the charging current, the voltage of the battery, and the temperature of the battery measured multiple times at different times during the charging process of the battery, as well as the time corresponding to each value during the change process of the charging current, voltage, and temperature (i.e., the time when the value appears). For example, the format of the time can be xx:xx:xx, xx / xx / xxxx.

[0036] In one embodiment, the historical charging condition data set of the sample battery may include the device number of the battery (or battery pack).

[0037] In one embodiment, optionally, the method may further include: calculating a capacity increment curve corresponding to each historical charging condition data set; extracting multiple characteristic parameters of each capacity increment curve; and inputting the multiple characteristic parameters corresponding to each historical charging condition data set and the SOH value into the data model to train the data model.

[0038] Through the above training, the data model can further establish an internal correlation between multiple historical charging condition data sets and the state of health SOH value.

[0039] Figure 1 Examples of multiple capacity increment curves of the battery at different SOHs during the charging process are shown, where the abscissa represents the voltage of the battery and the ordinate represents the capacity increment of the battery. As Figure 1 shown, at different SOHs, the capacity increment curves of the battery during the constant current charging process are different. For example, the position where the capacity increment appears at the peak and the height of the peak, etc. are different during the process of the increase of the battery voltage. The characteristics of the capacity increment curve are not limited to the characteristics related to the peak or peak value, and may also include, for example, characteristics related to the gradient change and envelope area of the capacity increment curve.

[0040] In one embodiment, optionally, each set of historical charging condition data may be obtained under a charging condition where the change interval of the remaining battery charge is greater than a specific percentage.

[0041] The change interval of the remaining battery charge refers to the difference between the percentage of the remaining battery charge at the end of charging and the percentage of the remaining battery charge at the start of charging during the charging process of the battery. The larger this difference is, the closer the charging process is to the full-range charging condition of fully discharging and then fully charging the battery, and the corresponding set of historical charging condition data is more likely to enable the data model to establish the above-mentioned internal relationship more quickly and accurately. It should be emphasized that "the change interval of the remaining battery charge is greater than a specific percentage" is not necessary, because even if the change interval of the remaining battery charge is small, the data model can establish the above-mentioned internal relationship through a larger number of sets of historical charging condition data. Therefore, the accuracy can be improved by increasing the number of data sets.

[0042] The specific percentage may be a value appropriately selected by those skilled in the art according to the actual situation and technical requirements when implementing the technology of the present disclosure. For example, the specific percentage may be any value selected from 5% to 100%, such as 30%, 40%, 60%, 70%, etc.

[0043] In one embodiment, calculating the capacity increment curve corresponding to each set of historical charging condition data may include: before calculating the capacity increment curve, performing Gaussian filtering on the voltage data in each set of historical charging condition data.

[0044] Specifically, perform Gaussian filtering on all historical charging voltage data to reduce the influence of voltage sampling noise on the result. As an example, the following formula can be used as the one-dimensional Gaussian filtering formula:

[0045] (1)

[0046] where x is the input data to be filtered, and σ is the standard deviation of the Gaussian distribution, representing the degree of dispersion of the data. The larger σ is, the more dispersed the distribution is, the greater the filtering intensity is, and the flatter the curve is. For example, σ can be adaptively set according to the sampling interval of the input data.

[0047] In one embodiment, calculating the capacity increment curve corresponding to each set of historical charging condition data includes: calculating the capacity increment corresponding to each voltage interval window; and performing Gaussian filtering on the capacity increment.

[0048] Specifically, for example, the capacity increment can be calculated in the way of equal voltage intervals, that is, the charging voltage is slid in a window with a set voltage interval window ΔV, and the change in ampere-time integral I*Δt within each window ΔV is calculated. For the calculated capacity increment data, Gaussian filtering is used to filter the capacity increment data to make the capacity increment data smoother. The overall shape of the incremental capacity (IC) curve is as Figure 1 shown.

[0049] IC curve calculation formula:

[0050] (2)

[0051] where, represents the capacity increment result obtained by calculation in the k-th voltage interval window during the charging process. I represents the charging current, and V represents the charging voltage. and are the upper and lower limit values of the voltage interval window, the difference between the two is ΔV, dt represents the charging time within this voltage interval window. Q represents the electric charge, is the increment of the electric charge within this voltage interval window.

[0052] In one embodiment, the multiple characteristic parameters may include parameters of one or more peaks in the process of the incremental capacity curve changing with voltage.

[0053] As Figure 1 shown, there is one or more peaks (peak values) in the process of each incremental capacity curve changing with voltage. The parameters of the one or more peaks may include one or more of the following: the peak height of the peak, the position of the peak, the area of the peak, the temperature of the battery at the position of the peak, the temperature of the battery at the position of the peak, the half-peak area of the peak.

[0054] For example, in the example shown in Figure 1 , there are three peaks, which are respectively called the first peak ( Figure 1 peak No. 1 in Figure 1 ), the second peak ( Figure 1 peak No. 2 in Figure 1 ) and the third peak ( Figure 1 peak No. 3 in Figure 1 ). In different types of batteries, the number of peaks is different, so the number of peaks can be any number.

[0055] In one embodiment, the multiple characteristic parameters include one or more of the following parameters related to the second peak and the third peak in the process of the incremental capacity curve 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, the half-peak area of the third peak.

[0056] 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 value of the peak is reached. Figure 2 It is only a schematic diagram of the curve shape of the peak. The peak curve shape of the actual capacity increment curve may be different from Figure 2 the example shown, but the calculation methods of the peak area or half-peak area of the peak are not limited by the specific peak curve shape, and these calculation methods are well-known.

[0057] In one embodiment, the plurality of characteristic parameters may further include one or more of the charging start voltage, charging start temperature, and average charging current of the battery.

[0058] In one embodiment, the battery is charged in a constant current charging mode. However, constant current means approximately constant current. Therefore, in the case of constant current, there are still small fluctuations in the charging current, so it is still necessary to calculate the average value of the charging current of the constant current.

[0059] In one embodiment, the method may further include: obtaining a historical charging condition data set obtained under a charging condition in which the change range of the remaining battery power of the battery to be measured 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 set, obtaining the SOH value corresponding to the historical charging condition data set, and using the SOH value as the current SOH value.

[0060] The historical charging condition data set described above refers to the charging condition data set that has been obtained by measurement during the previous use of the battery to be measured (for example, in the case where the battery is a battery on an electric vehicle, it is the battery of the currently used electric vehicle), and the SOH value of the battery has been obtained corresponding to the charging condition data set.

[0061] When the change range of the remaining battery power is greater than the specific percentage, the charging process of the battery is relatively close to the full-range charging condition of full discharge and then full charge. Therefore, the corresponding SOH value obtained after the end of this charging process should be relatively accurate, and thus can be used as the current SOH value of the battery.

[0062] When the charging time of the historical charging condition is within a specific time length (for example, one week, that is, 7 days) before the charging time of the current charging condition data set, since the time interval is short, the historical charging condition data set and its corresponding SOH value are relatively new, so the SOH value can be used as the current SOH value of the battery.

[0063] When the above two conditions are satisfied simultaneously, 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 to correct the current SOH value estimated through the data model.

[0064] In one embodiment, the method may further include: counting the number of times the SOH value is obtained, and when the number of times is greater than a predetermined number of times, calculating the mean value of the deviation between the estimated current SOH value and the SOH value, and using the mean value to correct all the estimated current SOH values of the battery under test.

[0065] The above-mentioned predetermined number of times may be, for example, 3 times, or any other multiple. For example, if the mean value of the deviation is 2%, this mean value of 2% can be used to correct all the SOH results estimated for the battery under test through the data model.

[0066] Through the above method, the actually measured SOH obtained under a full-range charging condition that is relatively close to full discharge and then full charge and / or in the relatively recent past can be used to correct the SOH result estimated through the data model, thereby further improving the estimation accuracy. In addition, through this method, the error of the capacity estimation result caused by other factors such as the difference between battery cells can also be reduced.

[0067] In one embodiment, the data model used in the present disclosure may include, but is not limited to, any one or a combination of the following models: linear regression model, random forest, support vector machine, neural network model.

[0068] Merely as an example, for example, in the case of using a linear regression model as the data model, the least squares method can be used to obtain (i.e., train) the equation of the specific data model:

[0069] (3)

[0070] Wherein, X is the feature vector extracted by the data model from the input historical charging condition data set, and Y is the SOH value vector corresponding to the historical charging condition data set.

[0071] 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 , which includes the second peak height, the second peak position, the second peak area, the third peak height, the third peak position, the third peak area, the charging start voltage, the charging start temperature, the average charging current, the temperature at the second peak position, the temperature at the third peak position, the half-peak area of the second peak, and the half-peak area of the third peak. Here, w is the weight corresponding to the 13-dimensional features, b is the intercept, and w and b are parameters to be obtained by regression fitting (i.e., obtained through training).

[0072] In each of the above examples, each feature vector xm includes 13 feature parameters, but this is only an example. One or more of these 13 feature parameters can be used, or one or more feature parameters other than these 13 feature parameters can be used.

[0073] After obtaining the equation (3) of the above data model by training using the feature vector X and the SOH value vector Y corresponding to the feature vector X, the current charging condition data set of the battery to be measured can be obtained, and the trained data model equation (3) can be used to estimate the current SOH value of the battery to be measured according to the current charging condition data set of the battery to be measured.

[0074] In one embodiment, after obtaining the current charging condition data set of the battery to be measured, the capacity increment curve corresponding to the current charging condition data set can be calculated, and multiple feature parameters of the capacity increment curve can be extracted. Before calculating the capacity increment curve, Gaussian filtering can be performed on the voltage data in the current charging condition data set. When calculating the capacity increment curve corresponding to the current charging condition data set, the capacity increment corresponding to each voltage interval window can be calculated, and Gaussian filtering can be performed on the capacity increment. The method for extracting multiple feature parameters of the capacity increment curve corresponding to the current charging condition data set can be, for example, the same as the method for the historical charging condition data set above.

[0075] In one embodiment, the current charging condition data set of the battery under test may include the device number of the battery under test (or battery pack).

[0076] The present disclosure also provides a computer program product, which includes computer-executable instructions that, when executed by one or more processors, cause the one or more processors to execute the methods according to any one of the above embodiments or combinations of embodiments.

[0077] The present disclosure also provides a device for estimating the state of health of a battery. The device may include: a memory storing instructions thereon; and a processor configured to execute the instructions stored on the memory to: obtain a plurality of historical charging condition data sets of a plurality of sample batteries and the state of health (SOH) values corresponding to each historical charging condition data set, where the historical charging condition data set includes a charging current, a voltage of the battery, a temperature of the battery, and a moment associated with the charging current, voltage, and temperature; and obtain a current charging condition data set of a battery to be measured and use a data model to estimate the current SOH value of the battery to be measured, where the data model can establish an internal association between the plurality of historical charging condition data sets and the state of health (SOH) values, and the current charging condition data set includes a charging current, a voltage of the battery, a temperature of the battery, and a moment associated with the charging current, voltage, and temperature.

[0078] The processor of the above device for estimating the state of health of a battery may also be configured to execute the instructions stored on the memory to perform the steps or operations in the method according to any one of the above embodiments or a combination of embodiments.

[0079] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be covered by the scope of the claims and the specification of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present application is not limited to the specific embodiments disclosed in the text, but includes all technical solutions falling 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: Calculating a capacity increment curve corresponding to each historical charging condition data group of the plurality of sample batteries; 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 of the plurality of sample batteries into a data model to train the data model, Among them, 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, the half-peak area of ​​the third peak, the battery's charging start voltage, the charging start temperature and the average charging current.

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 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.

6. The method according to claim 5, 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.

7. 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.

8. 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 7.

9. 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: Calculating a capacity increment curve corresponding to each historical charging condition data group of the plurality of sample batteries; Extracting 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 of the plurality of sample batteries into a data model to train the data model, Among them, 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, the half-peak area of ​​the third peak, the battery's charging start voltage, the charging start temperature and the average charging current.

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