Battery state of health estimation method and apparatus and computer program product

Abstract

A battery state of health estimation method and apparatus and a computer program product. The method comprises: acquiring multiple historical charging operating condition data sets of multiple sample batteries, and state of health (SOH) values corresponding to the respective historical charging operating condition data sets, wherein each historical charging operating condition data set comprises a charging current, a battery voltage, a battery temperature, and time points associated with the charging current, the voltage, and the temperature; and acquiring a current charging operating condition data set of a battery to be tested, and using a data model to estimate a current SOH value of the battery to be tested, wherein the data model is capable of establishing an intrinsic correlation between the multiple historical charging operating condition data sets and the SOH values, and the current charging operating condition data set comprises a charging current, a battery voltage, a battery temperature, and time points associated with the charging current, the voltage, and the temperature. The method enables easy estimation of the state of health of a battery.

Description

Methods, apparatus, and computer programs for estimating battery health status Technical Field

[0001] This application relates to the field of batteries, and more specifically to a method, apparatus, and computer program product for estimating the state of health of a battery. Background Technology

[0002] Battery health is crucial for the safe operation of the battery management system. As batteries age, their capacity decreases, reducing the amount of capacity that can be charged within the same State of Charge (SOC) range. This leads to a decline in battery dynamics and a decrease in charge / discharge power. Only by accurately assessing the battery's health can the accurate SOC be calculated and more suitable charge / discharge power matched, thereby maximizing the battery pack's performance, improving its range, and reducing the safety risks of overcharging and over-discharging.

[0003] Existing technologies for estimating battery health status still have limitations and shortcomings. Summary of the Invention

[0004] Traditional methods for estimating battery health status require the battery to be fully charged or fully discharged, as well as the derived two-point method that is close to full charge and discharge. This method requires both the high and low points to be completely at rest in order to determine the battery's state of charge. This method is relatively simple and has high accuracy, but it requires less computer processing power, meaning it is difficult to estimate the battery health status under normal usage conditions.

[0005] To address the aforementioned problems, this application provides a method, apparatus, and computer program product for estimating battery health status.

[0006] In one aspect, a method for estimating the state of health (SOH) of a battery is provided, comprising: acquiring multiple historical charging condition data sets of multiple sample batteries and a state of health (SOH) value corresponding to each historical charging condition data set, wherein the historical charging condition data sets include charging current, battery voltage, battery temperature, and times associated with the charging current, voltage, and temperature; and acquiring a current charging condition data set of a battery under test, and using a data model to estimate the current SOH value of the battery under test, wherein the data model is capable of establishing an intrinsic correlation between the multiple historical charging condition data sets and the SOH value, wherein the current charging condition data set includes charging current, battery voltage, battery temperature, and times 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 set; extracting multiple feature parameters for each capacity increment curve; and inputting the multiple feature parameters corresponding to each historical charging condition data set and the SOH value into the data model to train the data model, wherein the multiple feature parameters include parameters of one or more peaks of the capacity increment curve during the process of voltage variation.

[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 perform the method as described above.

[0008] In one aspect, an apparatus for estimating the state of health (SOH) of a battery is provided, the apparatus comprising: a memory storing instructions thereon; and a processor configured to execute the instructions stored in the memory to: acquire multiple historical charging condition data sets of multiple sample batteries and a SOH value corresponding to each historical charging condition data set, the historical charging condition data sets including charging current, battery voltage, battery temperature, and times associated with the charging current, voltage, and temperature; and acquire a current charging condition data set of a battery under test, and use a data model to estimate the current SOH value of the battery under test, the data model being capable of establishing the multiple historical charging condition data sets. The processor is configured to: establish an intrinsic correlation between historical charging condition data sets and the State of Health (SOH) value; include charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature in the current charging condition data set; calculate the capacity increment curve corresponding to each historical charging condition data set; extract multiple feature parameters for each capacity increment curve; and input the multiple feature parameters and the SOH value corresponding to each historical charging condition data set into a data model to train the data model. The multiple feature parameters include parameters of one or more peaks in the capacity increment curve as it changes with voltage.

[0009] The method, apparatus, and computer program product disclosed herein for estimating battery health status enable battery health status estimation for general operating conditions, thereby greatly increasing the applicability of the estimation method and making it easier to estimate battery health status.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0011] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0012] Figure 1 shows examples of multiple capacity increment curves of the battery under different SOH during the charging process.

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

[0014] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0016] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0017] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0019] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0020] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0021] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0022] Terminology Explanation

[0023] 1. SOC: State of Charge (SOC) refers to the percentage of usable charge remaining in a battery relative to its nominal capacity. It is an important monitoring data point for the battery management system (BMS). The BMS can control the battery's operating state based on the SOC value. The remaining charge of the battery reflects its state of charge.

[0024] 2. SOH: State of health, which in this article refers to the percentage of the battery's current capacity relative to its factory capacity;

[0025] 3. ICA: Incremental capacity analysis refers to a method that estimates or estimates the capacity decay of a battery by analyzing the relationship between the change in battery capacity increment and the charging voltage during constant current charging.

[0026] In existing technologies, traditional capacity definition methods require testing the battery across its entire charge-discharge range (i.e., the SOC range or SOC variation range), which is quite demanding. Even the improved two-point method requires a large SOC variation range and a resting condition, resulting in fewer computational opportunities (i.e., operating conditions that meet the computational requirements of the algorithm used to calculate the battery's state of health, thus outputting the battery's state of health (SOH) result).

[0027] Furthermore, existing capacity increment curve methods do not consider the impact of different charging conditions on the ICA curve, resulting in limited coverage in practical applications. They also fail to account for differences in ICA curve characteristic parameters caused by individual battery variations, leading to significant errors in the battery health status estimation results. For example, without considering individual battery differences (material differences, aging path differences, etc.), the estimated battery capacity results may differ even under the same characteristic parameters.

[0028] Traditional methods for estimating battery health status require the battery to be fully charged or fully discharged, as well as the derived two-point method that is close to full charge and discharge. This method requires both the high and low points to be completely at rest in order to determine the battery's state of charge. This method is relatively simple and has high accuracy, but it requires less computer processing.

[0029] Furthermore, existing capacity increment methods do not consider the effects of different starting voltages, temperatures, or charging currents on the ICA curve, resulting in deviations in the obtained ICA curves due to these factors. Additionally, they do not account for individual battery differences, leading to varying estimated battery capacities even with the same characteristic parameters.

[0030] To address one or more problems in the prior art, this paper proposes a method for estimating battery state of health that is highly applicable and can easily estimate battery state of health.

[0031] In one embodiment, this disclosure proposes a method for estimating the state of health (SOH) of a battery, comprising: acquiring multiple historical charging condition data sets of multiple sample batteries and a state of health (SOH) value corresponding to each historical charging condition data set, wherein the historical charging condition data set includes charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature; and acquiring a current charging condition data set of a battery under test, and using a data model to estimate the current SOH value of the battery under test, wherein the data model is capable of establishing an intrinsic correlation between the multiple historical charging condition data sets and the SOH value, and the current charging condition data set includes charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature.

[0032] Since this disclosure can establish an intrinsic correlation between multiple historical charging condition data sets and the state of health (SOH) value through a data model, this technical solution does not require full-range testing of the battery during full charge and discharge. It can estimate the SOH value based on historical charging condition data within a smaller SOC variation range.

[0033] In one embodiment, historical charging condition data sets and corresponding State of Health (SOH) values ​​for each historical charging condition data set have been obtained from the sample battery. These data sets and SOH values ​​were previously obtained through actual measurements. The data model can estimate the current SOH value of the 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 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 intrinsic correlation between multiple historical charging condition data sets and the state of health (SOH) value, thereby estimating the SOH value of the battery under test based on this correlation.

[0035] The historical charging condition data set includes the values ​​of 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 changes in charging current, voltage, and temperature (i.e., the time when that value occurred). For example, the time format could be xxxx year xx month xx day xx hour xx minute xx second.

[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, the method may optionally include: calculating a capacity increment curve corresponding to each historical charging condition data group; extracting multiple feature parameters for each capacity increment curve; and inputting the multiple feature parameters corresponding to each historical charging condition data group and the SOH value into a data model to train the data model.

[0038] Through the above training, the data model can further establish the intrinsic correlation between multiple historical charging condition data sets and the State of Health (SOH) value.

[0039] Figure 1 shows examples of multiple capacity increment curves of a battery under different state of equilibrium (SOH) during charging. The horizontal axis represents the battery voltage, and the vertical axis represents the battery capacity increment. As shown in Figure 1, the capacity increment curves of a battery during constant current charging differ under different SOHs. For example, the location and height of the peak value as the battery voltage increases vary. The characteristics of the capacity increment curve are not limited to those related to peaks or peak values; they can also include features related to gradient changes and envelope area, for example.

[0040] In one embodiment, optionally, each set of historical charging condition data can be obtained under charging conditions where the remaining battery capacity varies by more than a certain percentage.

[0041] The remaining capacity variation range refers to the difference between the percentage of remaining capacity at the end of charging and the percentage at the beginning of charging. The larger this difference, the closer the charging process is to a full-range charging condition of full discharge followed by full charge. The larger this difference, the easier it is for the data model to quickly and accurately establish the aforementioned intrinsic relationship using historical charging condition data sets. It is important to emphasize that a "remaining capacity variation range greater than a specific percentage" is not necessary. Even with a small remaining capacity variation range, the data model can establish the aforementioned intrinsic relationship using a large number of historical charging condition data sets. Therefore, increasing the number of data sets can improve accuracy.

[0042] The specific percentage may be a value appropriately selected by those skilled in the art based on actual circumstances and technical requirements when implementing the technology disclosed herein. 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 historical charging condition data group may include: performing Gaussian filtering on the voltage data in each historical charging condition data group before calculating the capacity increment curve.

[0044] Specifically, Gaussian filtering is applied to all historical charging voltage data under various operating conditions 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 filtering formula:

[0045] Where x represents the input data to be filtered, and σ is the standard deviation of the Gaussian distribution, representing the dispersion of the data. The larger σ is, the more discrete the distribution, the stronger the filtering, and the smoother the curve. For example, σ can be adaptively set according to the sampling interval of the input data.

[0046] 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.

[0047] Specifically, for example, the capacity increment can be calculated using an equal voltage interval method. That is, the charging voltage is windowed according to a 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, a Gaussian filter is used to filter the capacity increment data, making the capacity increment data smoother. The overall shape of the capacity increment (IC) curve is shown in Figure 1.

[0048] IC curve calculation formula:

[0049] Among them, IC k This represents the capacity increment calculated during the k-th voltage interval window during charging. I represents the charging current, and V represents the charging voltage. k and V k-1 These are the upper and lower limits of the voltage interval window, and the difference between them is ΔV. dt represents the charging time within this voltage interval window. Q represents the amount of charge. k -Q k-1 It is the increment of charge within this voltage interval window.

[0050] In one embodiment, the plurality of characteristic parameters may include parameters of one or more peaks of the capacity increment curve as it changes with voltage.

[0051] As shown in Figure 1, each capacity increment curve has one or more peaks (peak values) as it changes 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, and half-peak area.

[0052] For example, in the example shown in Figure 1, there are three peaks, referred to as the first peak (peak 1 in Figure 1), the second peak (peak 2 in Figure 1), and the third peak (peak 3 in Figure 1). The number of peaks varies in different types of batteries, so the number of peaks can be arbitrary.

[0053] In one embodiment, the plurality of characteristic parameters include one or more of the following parameters related to the second and third peaks of the capacity increment curve as it changes 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, battery temperature at the position of the second peak, battery temperature at the position of the third peak, half-peak area of ​​the second peak, and half-peak area of ​​the third peak.

[0054] Figure 2 illustrates the peak height, peak position, and half-peak area of ​​the peak on the capacity increment curve. The peak position refers to the battery voltage at which the peak value is reached. Figure 2 is merely a schematic diagram of the peak curve shape; the actual peak curve shape of the capacity increment curve may differ from the example shown in Figure 2. However, the methods for calculating the peak area or half-peak area are not limited by the specific peak curve shape, and these calculation methods are well-known.

[0055] In one embodiment, the plurality of characteristic parameters may further include one or more of the battery’s charging start voltage, charging start temperature, and average charging current.

[0056] In one embodiment, the battery is charged using a constant current charging method. However, constant current refers to an approximate constant current; therefore, even with a constant current, the charging current still experiences slight fluctuations, necessitating the calculation of the average constant current charging current.

[0057] In one embodiment, the method may further include: acquiring a historical charging condition data set of the battery under test, where the remaining charge variation range of the battery 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; acquiring the SOH value corresponding to the historical charging condition data set; and using the SOH value as the current SOH value.

[0058] The historical charging condition data set described above refers to the charging condition data set that has been measured during the previous use of the battery under test (for example, the battery of the electric vehicle currently in use). The SOH value of the battery is obtained corresponding to this charging condition data set.

[0059] When the remaining charge of the battery varies beyond the specified percentage, the 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 current SOH value of the battery.

[0060] If the charging time of a historical charging condition is within a specific time length (e.g., one week, i.e., 7 days) before the charging time of the current charging condition data set, the historical charging condition data set and its corresponding SOH value are relatively recent due to the short time interval. Therefore, the SOH value can be used as the current SOH value of the battery.

[0061] If both of the above conditions are met, it indicates that the historical charging condition data set and its corresponding SOH value were obtained not only under a full-range charging condition that is close to full discharge and then full charge, but also in the recent past. Therefore, the SOH value is more suitable as the current SOH value of the battery so as to correct the current SOH value estimated by the data model.

[0062] In one embodiment, the method may further include: counting the number of times the SOH value is acquired; when the number of acquisitions exceeds a predetermined number of acquisitions, calculating the mean of the deviations between the estimated current SOH value and the SOH value; and using the mean to correct all estimated current SOH values ​​of the battery under test.

[0063] The predetermined number of times mentioned above could be, for example, 3 times, or any other number of times. For example, if the mean deviation value is 2%, this mean of 2% can be used to correct all SOH results estimated by the data model for the battery under test.

[0064] The above method allows for the correction of the SOH estimated by the data model using actual measurements obtained in the near-full discharge-full charge cycle and / or in the recent past, thereby further improving the estimation accuracy. Furthermore, this method can also reduce errors in capacity estimation caused by factors such as individual cell variations.

[0065] In one embodiment, the data model used in this 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.

[0066] Just as an example, when using a linear regression model as the data model, the least squares method can be used to derive (i.e., train) the equation for the specific data model: Y = w T X+b (3)

[0067] Where 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.

[0068] 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 x m =(x m1 ,x m2 ,…,x m13 The data includes the height of the second peak, the position of the second peak, the area of ​​the second peak, the height of the third peak, the position of the third peak, the area of ​​the third peak, the charging start voltage, the charging start temperature, the average charging current, the temperature at the position of the second peak, the temperature at the position of the third peak, the half-peak area of ​​the second peak, and the half-peak area of ​​the third peak. w is the weight corresponding to the 13-dimensional features, b is the intercept, and w and b are the parameters to be regressed (i.e., obtained through training).

[0069] In the example above, each feature vector xm contains 13 feature parameters, but this is just an example. You can use one or more of these 13 feature parameters, or one or more other feature parameters besides these 13.

[0070] After training the above data model equation (3) by 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 under test can be obtained, and the current SOH value of the battery under test can be estimated by using the trained data model equation (3) based on the current charging condition data set of the battery under test.

[0071] In one embodiment, after acquiring the current charging condition data set of the battery under test, a 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 applied to 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 applied to 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 described above for historical charging condition data sets.

[0072] In one embodiment, the current charging condition data set of the battery under test may include the device number of the battery (or battery pack) under test.

[0073] This disclosure also provides a computer program product including computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform a method according to any one or a combination of embodiments described above.

[0074] This disclosure also provides an apparatus for estimating the state of health of a battery, the apparatus comprising: a memory storing instructions thereon; and a processor configured to execute the instructions stored in the memory to: acquire multiple historical charging condition data sets of multiple sample batteries and a state of health (SOH) value corresponding to each historical charging condition data set, the historical charging condition data set including charging current, battery voltage, battery temperature, and a time associated with the charging current, voltage, and temperature; and acquire a current charging condition data set of a battery under test, and use a data model to estimate the current SOH value of the battery under test, the data model being capable of establishing an intrinsic correlation between the multiple historical charging condition data sets and the state of health (SOH) value, the current charging condition data set including charging current, battery voltage, battery temperature, and a time associated with the charging current, voltage, and temperature.

[0075] The processor of the aforementioned device for estimating battery health status may also be configured to execute instructions stored in the memory to perform steps or operations in the method according to any of the embodiments or combinations thereof described above.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for estimating the state of health of a battery, characterized in that, include: Acquire multiple historical charging condition data sets for multiple sample batteries and the State of Health (SOH) value corresponding to each historical charging condition data set. The historical charging condition data sets include charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature; and The current charging condition data set of the battery under test is acquired, and a data model is used to estimate the current state of health (SOH) value of the battery under test. The data model can establish an intrinsic correlation between the multiple historical charging condition data sets and the SOH value. The current charging condition data set includes charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature. The method further includes: Calculate the capacity increment curve corresponding to each historical charging condition data set; Extract multiple feature parameters from each capacity increment curve; The multiple feature parameters and the SOH value corresponding to each historical charging condition data group are input into the data model to train the data model. The plurality of characteristic parameters include parameters of one or more peaks of the capacity increment curve as it changes with voltage.

2. The method as described in claim 1, characterized in that, Each set of historical charging condition data was obtained under charging conditions where the remaining battery capacity changed by more than a certain percentage.

3. The method as described in claim 1, characterized in that, The calculation 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 as described in claim 1, characterized in that, The calculation of the capacity increment curve corresponding to each historical charging condition data group includes: Calculate the capacity increment corresponding to each voltage interval window; and The capacity increment is subjected to Gaussian filtering.

5. The method as described in claim 1, characterized in that, The plurality of characteristic parameters include one or more of the following parameters related to the second and third peaks of the capacity increment curve as it changes 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, battery temperature at the position of the second peak, battery temperature at the position of the third peak, half-peak area of ​​the second peak, and half-peak area of ​​the third peak.

6. The method as described in claim 1, characterized in that, The plurality of characteristic parameters include one or more of the battery’s charging start voltage, charging start temperature, and average charging current.

7. The method as described in claim 1, characterized in that, The method further includes: Obtain historical charging condition data sets for the battery under test when the remaining charge of the battery changes by a certain percentage and / or the charging time is within a certain time length before the charging time of the current charging condition data set. Obtain the SOH value corresponding to the historical charging condition data set and use the SOH value as the current SOH value.

8. The method as described in claim 7, characterized in that, The method further includes: The number of times the SOH value is obtained is counted. When the number of times exceeds a predetermined number, the mean of the deviation between the estimated current SOH value and the SOH value is calculated, and the mean is used to correct all estimated current SOH values ​​of the battery under test.

9. The method as described in claim 1, characterized in that, The data model includes any one or a combination of the following models: linear regression model, random forest, support vector machine, and 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. An apparatus for estimating the state of health of a battery, characterized in that, The device includes: A memory, on which instructions are stored; and The processor is configured to execute instructions stored in the memory to: Acquire multiple historical charging condition data sets for multiple sample batteries and the State of Health (SOH) value corresponding to each historical charging condition data set. The historical charging condition data sets include charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature; and The current charging condition data set of the battery under test is acquired, and a data model is used to estimate the current state of health (SOH) value of the battery under test. The data model can establish an intrinsic correlation between the multiple historical charging condition data sets and the SOH value. The current charging condition data set includes charging current, battery voltage, battery temperature, and the time associated with the charging current, voltage, and temperature. The processor is further configured to: Calculate the capacity increment curve corresponding to each historical charging condition data set; Extract multiple feature parameters from each capacity increment curve; The multiple feature parameters and the SOH value corresponding to each historical charging condition data group are input into the data model to train the data model. The plurality of characteristic parameters include parameters of one or more peaks of the capacity increment curve as it changes with voltage.

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