An online battery capacity detection method, electronic equipment and storage medium

Abstract

This application discloses an online method, electronic device, and computer-readable storage medium for detecting battery capacity. The method includes: acquiring battery state-of-charge (SOC) parameter data in real time; normalizing the SOC parameter data to obtain the corresponding open-circuit voltage value in real time; calculating the battery's charging capacity value during a period when the open-circuit voltage value is within a preset voltage range; the preset voltage range is a range where the battery's capacity decay changes significantly; calling a capacity recognition model to determine the battery's actual total capacity value based on the charging capacity value corresponding to the preset voltage range; the capacity recognition model is pre-trained based on sample test data. This application monitors and calculates the battery's actual SOC data, and then uses a pre-trained capacity recognition model to match and identify the battery's actual total capacity value. This not only has high detection efficiency and accuracy, but also a convenient and simple detection process, making it easy to promote and apply, and it can be used online.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to an online method for detecting battery capacity, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Today, lithium-ion (Li-ion) batteries play a key role in transportation electrification and renewable energy systems as one of the main energy storage devices for electric vehicles and power plants.

[0003] Capacity is a fundamental indicator of a battery, representing the maximum energy it can store. It directly impacts the battery's state of charge and state of health. Generally, batteries age continuously under charging and discharging conditions, and their degradation rate varies with ambient temperature and load conditions. Accurately identifying the maximum usable capacity of a battery during practical use is currently a key focus and challenge in power battery research.

[0004] In recent years, several methods for assessing / predicting battery capacity have emerged in existing technologies. One type is based on empirical models, such as capacity loss models. These methods are simple to calculate but have low accuracy. Another type is based on physical models, which use partial differential equations to quantify the electrochemical state, including calculations of parameters such as the total amount of active material, the resistance of the solid electrolyte interfacial membrane, and the diffusion coefficient. These methods simulate the complex processes of battery degradation mechanisms, thus achieving higher accuracy, but they involve large computational loads and cannot be practically applied in engineering.

[0005] Therefore, providing a solution to the above-mentioned technical problems is something that those skilled in the art urgently need to focus on. Summary of the Invention

[0006] The purpose of this application is to provide an online detection method, electronic device, and computer-readable storage medium for battery capacity, so that the battery capacity detection process has low computational load, is convenient and easy to promote, and has high accuracy.

[0007] To address the aforementioned technical problems, in a first aspect, this application discloses an online method for detecting battery capacity, comprising:

[0008] Real-time acquisition of the battery's charging status parameter data;

[0009] The charging state parameter data is normalized and calculated to obtain the corresponding open-circuit voltage value in real time;

[0010] Calculate the charging capacity of the battery during the period when the open-circuit voltage value is within a preset voltage range; the preset voltage range is the range in which the battery's capacity decay changes significantly.

[0011] The capacity recognition model is invoked to determine the actual total capacity of the battery based on the charging capacity value corresponding to the preset voltage range; the capacity recognition model is pre-trained and generated based on sample test data.

[0012] Optionally, the real-time acquisition of the battery's state-of-charge parameters includes:

[0013] The battery voltage, battery current, temperature, and battery state of charge are acquired in real time during charging.

[0014] Optionally, the normalization calculation of the charging state parameter data to obtain the corresponding open-circuit voltage value in real time includes:

[0015] A DC internal resistance estimation model for the target battery model was established based on sample test data.

[0016] The real-time open-circuit voltage value is obtained based on the following normalized calculation formula:

[0017] OCV(T,SOC)=V–I*R(T,SOC);

[0018] Where V is the battery voltage; I is the battery current; T is the temperature; SOC is the battery state of charge; OCV(T, SOC) is the open-circuit voltage; and R(T, SOC) is the estimated DC internal resistance.

[0019] Optionally, the preset voltage range is determined in advance through the following process:

[0020] An open-circuit voltage estimation model for the target battery model was established based on sample test data.

[0021] Multiple charge-discharge cycles were performed on the target battery model, and the battery capacity was monitored and the estimated open-circuit voltage was calculated during the charging process.

[0022] Generate IC curves for different charge-discharge cycles and determine the peak values ​​of each curve; wherein, the vertical axis of the IC curve is the derivative of the battery capacity with respect to the estimated open-circuit voltage, and the horizontal axis is the estimated open-circuit voltage.

[0023] The peak value of the curve that changes most significantly under different charge-discharge cycles is determined as the target peak value;

[0024] The voltage variation range of the target peak value is determined as the preset voltage range.

[0025] Optionally, the capacity identification model is determined in advance through the following process:

[0026] Multiple charge-discharge cycles were conducted on the target battery model for which the actual total capacity was known, and the charging status parameter data was monitored in real time during the charging process.

[0027] The corresponding open-circuit voltage value is obtained in real time through normalization calculation;

[0028] Calculate the charging capacity of the target battery model during the period when the open-circuit voltage value is within the preset voltage range;

[0029] Using the charging capacity value of the target battery model as sample input data and the actual total capacity value of the target battery model as sample output data, a capacity recognition model for the target battery model is trained and generated.

[0030] Optionally, the training to generate the capacity identification model for the target battery model includes:

[0031] The capacity identification model of the target battery model is generated by using data fitting or neural network model training.

[0032] Optionally, the step of performing multiple charge-discharge cycles on a target battery model with a known actual total capacity includes:

[0033] Different temperature and charging current conditions were set to perform multiple charge-discharge tests on the target battery model with a known actual total capacity.

[0034] Optionally, calculating the charging capacity of the battery during the period when the open-circuit voltage value is within a preset voltage range includes:

[0035] The charging capacity of the battery is calculated using the ampere-hour integration method while the open-circuit voltage value is within the preset voltage range.

[0036] In another aspect, this application also discloses an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor for executing the computer program to implement the steps of any of the online battery capacity detection methods described above.

[0039] In another aspect, this application also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of any of the online battery capacity detection methods described above.

[0040] The online battery capacity detection method provided in this application includes: acquiring the charging state parameter data of the battery in real time; normalizing the charging state parameter data to obtain the corresponding open circuit voltage value in real time; calculating the charging capacity value of the battery during the period when the open circuit voltage value is within a preset voltage range; the preset voltage range is the range in which the battery capacity decays significantly; calling a capacity recognition model to determine the actual total capacity value of the battery based on the charging capacity value corresponding to the preset voltage range; the capacity recognition model is pre-trained and generated based on sample test data.

[0041] The beneficial effects of the online battery capacity detection method, electronic device, and computer-readable storage medium provided in this application are as follows: This application monitors and calculates the actual charging state of the battery, and then uses a pre-trained capacity recognition model to match and identify the actual total capacity value of the battery based on the charging capacity value within a preset voltage range. This not only has high detection efficiency and result accuracy, but also the entire detection process is convenient and simple, easy to promote and apply, and can be used online. It is particularly suitable for battery capacity detection of on-board battery systems in some large rail locomotives that are inconvenient to disassemble. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the prior art and the embodiments of this application, the accompanying drawings used in the description of the prior art and the embodiments of this application will be briefly introduced below. Of course, the accompanying drawings described below with respect to the embodiments of this application are only a part of the embodiments in this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and such other drawings also fall within the protection scope of this application.

[0043] Figure 1 This is a flowchart of an online battery capacity detection method disclosed in an embodiment of this application;

[0044] Figure 2 This is a graph showing the decay of battery capacity with the number of charge-discharge cycles disclosed in an embodiment of this application.

[0045] Figure 3 This is a charging voltage curve of a battery under different charge-discharge cycles disclosed in an embodiment of this application;

[0046] Figure 4 This is a flowchart of a method for determining a preset voltage range disclosed in an embodiment of this application;

[0047] Figure 5 This is a schematic diagram of an IC curve disclosed in an embodiment of this application;

[0048] Figure 6This is a flowchart of a method for training and generating a capacity recognition model disclosed in an embodiment of this application;

[0049] Figure 7 This is a structural block diagram of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0050] The core of this application is to provide an online detection method, electronic device, and computer-readable storage medium for battery capacity, so that the battery capacity detection process has low computational load, is convenient and easy to promote, and has high accuracy.

[0051] To provide a clearer and more complete description of the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0052] Capacity is a fundamental indicator of a battery, representing the maximum energy it can store. It directly impacts the battery's state of charge and state of health. Generally, batteries age continuously under charging and discharging conditions, and their degradation rate varies with ambient temperature and load conditions. Accurately identifying the maximum usable capacity of a battery during practical use is currently a key focus and challenge in power battery research.

[0053] In recent years, several methods for evaluating / predicting battery capacity have emerged in existing technologies. One type is based on empirical models, such as capacity loss models, which offer simple and computationally savvy predictions but suffer from low accuracy. Another type is based on physical models, using partial differential equations to quantify the electrochemical state, including calculations of parameters such as the total amount of active material, the resistance of the solid electrolyte interfacial membrane, and the diffusion coefficient. These methods simulate the complex processes of battery degradation mechanisms, resulting in higher accuracy, but also requiring extensive computation, hindering practical engineering applications. Therefore, this application provides an online battery capacity detection scheme that effectively addresses the aforementioned problems.

[0054] See Figure 1 As shown in the figure, this application discloses an online method for detecting battery capacity, which mainly includes:

[0055] S101: Real-time acquisition of battery charging status parameter data.

[0056] S102: Normalize the charging state parameter data to obtain the corresponding open-circuit voltage value in real time.

[0057] S103: Calculate the battery's charging capacity during the period when the open-circuit voltage value is within a preset voltage range; the preset voltage range is the range in which the battery's capacity decay changes significantly.

[0058] S104: Call the capacity recognition model to determine the actual total capacity of the battery based on the charging capacity value corresponding to the preset voltage range; the capacity recognition model is pre-trained and generated based on sample test data.

[0059] Specifically, the capacity of a power battery will gradually decrease during use. For details, please refer to... Figure 2 , Figure 2 This is a graph illustrating the capacity decay of a battery as a function of the number of charge-discharge cycles, as disclosed in an embodiment of this application. Furthermore, the applicant has also found in practical applications that the battery's charging voltage curve changes with the increase in the number of charge-discharge cycles, primarily manifested as a shortening of the charging voltage plateau period. For details, please refer to... Figure 3 , Figure 3 This is a charging voltage curve of a battery disclosed in this application under different cycles of charge and discharge.

[0060] In response, the applicant, through comprehensive analysis of the two aforementioned variation curves, concluded that the change in the charging voltage plateau period related to the number of charge-discharge cycles corresponds to a certain relationship with the battery capacity decay. Therefore, this application proposes a technical solution to identify the actual capacity of a battery by utilizing its charging characteristics during the charging voltage plateau period.

[0061] Specifically, this application obtains a large amount of sample test data by conducting charge-discharge tests and state monitoring on a large number of batteries with known actual capacity values ​​in advance. The sample test data includes the charging capacity value and actual capacity value of these sample batteries within a preset voltage range. Then, the application uses the correspondence between the actual capacity value and the charging capacity value of different batteries in the sample test data to train a capacity recognition model. Thus, the actual capacity value of a battery in its current state can be matched and identified based on the charging capacity value of a specified model battery within a preset voltage range.

[0062] The preset voltage range corresponds to the charging voltage plateau period in the charging voltage curve, which is also the range where the battery's capacity decay changes significantly. It should be noted that the range of significant capacity decay is a range of values ​​for the battery's open-circuit voltage. When the battery's open-circuit voltage falls within this range, it is evident that the battery's capacity decay changes significantly with the number of charge-discharge cycles. That is, the dQ / dV-V curves for different charge-discharge cycles will show a significant non-overlapping phenomenon within this preset voltage range. Here, Q represents the battery capacity, and V represents the battery voltage.

[0063] It should be further noted that this application uses the charging capacity value within a preset voltage range to identify the actual total capacity of the battery. The detection effect of this application can be achieved as long as the battery's state of charge at the start of charging does not exceed 60%, without requiring the battery to start charging from 0V. Therefore, this application is more in line with practical applications and has practicality.

[0064] Therefore, the online battery capacity detection method disclosed in this application can first acquire the charging state parameter data of the battery during the charging process, and then calculate the real-time open-circuit voltage value of the battery through normalization processing. Let the preset voltage range be [Vmin, Vmax]. When the open-circuit voltage value rises to the left end of the preset voltage range, Vmin, the charging capacity metering is activated. When the open-circuit voltage value continues to rise to the right end of the preset voltage range, Vmax, the charging capacity metering is deactivated. This yields the battery's charging capacity value during the period when the open-circuit voltage value is within the preset voltage range. Furthermore, by calling a pre-trained capacity recognition model, the actual total capacity value of the battery can be identified based on this charging capacity value.

[0065] It should also be noted that the online battery capacity detection method disclosed in this application can be specifically applied to some vehicle-mounted devices, such as vehicle-mounted battery power detection terminals or power battery management systems; in addition, it can also be specifically applied to cloud devices such as cloud platforms that can communicate with vehicle networks. Those skilled in the art can choose according to the actual application situation, and this application does not limit it.

[0066] As can be seen, the online battery capacity detection method provided in this application monitors and calculates the actual charging state of the battery, and then uses a pre-trained capacity recognition model to match and identify the actual total capacity value of the battery based on the charging capacity value within a preset voltage range. This method not only has high detection efficiency and accuracy, but also the entire detection process is convenient and simple, easy to promote and apply, and can be used online. It is particularly suitable for battery capacity detection of on-board battery systems in some large rail locomotives that are inconvenient to disassemble.

[0067] As a specific embodiment, the online battery capacity detection method provided in this application, based on the above content, acquires battery charging state parameter data in real time, including: acquiring battery voltage, battery current, temperature, and battery state of charge during charging in real time.

[0068] Furthermore, the normalization calculation of the charging state parameter data to obtain the corresponding open-circuit voltage value in real time can specifically include:

[0069] A DC internal resistance estimation model for the target battery model was established based on sample test data.

[0070] The real-time open-circuit voltage value is obtained based on the following normalized calculation formula:

[0071] OCV(T,SOC)=V–I*R(T,SOC);

[0072] Where V is the battery voltage; I is the battery current; T is the temperature; SOC is the battery state of charge; OCV(T, SOC) is the open-circuit voltage; and R(T, SOC) is the estimated DC internal resistance.

[0073] It should be noted that, for the DC internal resistance estimation model, a feature database can be established by conducting DC internal resistance related tests on sample batteries of known capacity, recording test data at different temperatures and under different states of charge, and then establishing a DC internal resistance estimation model based on the sample test data of DC internal resistance.

[0074] R(T,SOC)=Function1(T,SOC).

[0075] See Figure 4 As shown, Figure 4 This is a flowchart illustrating a method for determining a preset voltage range disclosed in an embodiment of this application. As a specific embodiment, such as... Figure 4 As shown, the preset voltage range can be determined in advance through the following process:

[0076] S201: Establish an open-circuit voltage estimation model for the target battery model based on sample test data.

[0077] Specifically, for the open-circuit voltage estimation model of a certain target battery model, a feature database can be established by conducting relevant parameter tests on sample batteries of known capacity, recording test data at different temperatures and under different states of charge, and then establishing an open-circuit voltage estimation model based on the sample test data of open-circuit voltage:

[0078] OCV(T,SOC)=Function2(T,SOC).

[0079] It should be noted that, compared to the normalized calculation formula mentioned above, the open-circuit voltage estimation model here is only used to roughly estimate the open-circuit voltage value when a preset voltage range is determined, and its accuracy is limited.

[0080] S202: Perform multiple charge-discharge cycles on the target battery model, monitor the battery capacity during charging, and calculate the estimated open-circuit voltage.

[0081] Typically, the estimated open-circuit voltage at a specified temperature T0 can be calculated, where T0 can be specifically room temperature (25°C).

[0082] S203: Generate IC curves for different charge-discharge cycles and determine the peak values ​​of each curve; where the vertical axis of the IC curve is the derivative of the battery capacity with respect to the estimated open-circuit voltage, and the horizontal axis is the estimated open-circuit voltage.

[0083] As mentioned earlier, the length of the battery's charging voltage plateau period is related to its capacity decay. This application uses the charging capacity value during the charging voltage plateau period to determine the battery's actual total capacity. Specifically, to determine the preset voltage range corresponding to the charging voltage plateau period, this application plots the derivative of the battery capacity with respect to the estimated open-circuit voltage, dQ / dV-V curve, also known as the IC curve, for different charge-discharge cycles. Here, Q represents the battery capacity, and V is the battery voltage.

[0084] For details, please refer to Figure 5 , Figure 5 This is a schematic diagram of an IC curve disclosed in an embodiment of this application. Figure 5 As shown, the dQ / dV-V curve exhibits three peak values: peak1, peak2, and peak3. Among them, peak2 shows the most significant change with increasing charge-discharge cycles, exhibits the greatest non-overlapping degree, and spans the widest voltage range. Therefore, peak2 is the target peak value; and the voltage variation range spanned by this target peak2 is determined as the preset voltage range.

[0085] S204: The peak value of the curve that changes most significantly under different charge-discharge cycles is determined as the target peak value.

[0086] S205: Determine the voltage variation range of the target peak as the preset voltage range.

[0087] See Figure 6 As shown, Figure 6 This is a flowchart illustrating a method for training and generating a capacity recognition model, as disclosed in an embodiment of this application. As a specific embodiment, such as... Figure 6 As shown, the capacity identification model can be determined in advance through the following process:

[0088] S301: Perform multiple charge-discharge cycles on a target battery model with a known actual total capacity, and monitor the charging status parameters in real time during the charging process.

[0089] S302: Obtain the corresponding open-circuit voltage value in real time through normalization calculation.

[0090] S303: Calculate the charging capacity of the target battery model while the open-circuit voltage value is within a preset voltage range.

[0091] S304: Using the charging capacity of the target battery model as the input data and the actual total capacity of the target battery model as the output data, train and generate a capacity recognition model for the target battery model.

[0092] Furthermore, as a specific embodiment, training and generating a capacity identification model for the target battery model includes: generating a capacity identification model for the target battery model using a data fitting method or a neural network model training method.

[0093] As a specific embodiment, the online battery capacity detection method provided in this application, when training and generating a capacity recognition model, performs multiple cyclic charge-discharge tests on a target model battery with a known actual total capacity value. Specifically, it includes setting different temperature conditions and charging current conditions, and performing a single charge-discharge test on the target model battery with a known actual total capacity value.

[0094] To eliminate interference from multiple factors and avoid inaccurate calculation results, each charge-discharge test of the target battery model can be conducted at a constant temperature and constant current to calculate the charging capacity value under these constant temperature and current conditions. Then, by changing only one variable among temperature and current, test data under various conditions can be obtained to train the capacity recognition model, ensuring more accurate results under different conditions.

[0095] As a specific embodiment, the online battery capacity detection method provided in this application, based on the above content, calculates the battery charging capacity value during the period when the open circuit voltage value is within a preset voltage range, including: calculating the battery charging capacity value using the ampere-hour integration method during the period when the open circuit voltage value is within the preset voltage range.

[0096] What is easy to understand is that, in the IC curve graph, the area of ​​the curve within the preset voltage range is the charging capacity value to be calculated.

[0097] See Figure 7 As shown in the figure, an embodiment of this application discloses an electronic device, including:

[0098] Memory 401 is used to store computer programs;

[0099] Processor 402 is configured to execute the computer program to implement the steps of any of the online battery capacity detection methods described above.

[0100] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of any of the online battery capacity detection methods described above.

[0101] For details regarding the aforementioned electronic devices and computer-readable storage media, please refer to the detailed introduction of the online battery capacity detection method mentioned above; further details will not be repeated here.

[0102] The various embodiments in this application are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0103] It should also be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0104] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this application. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An online method for detecting battery capacity, characterized in that, include: Real-time acquisition of the battery's charging status parameter data; The charging state parameter data is normalized and calculated to obtain the corresponding open-circuit voltage value in real time; Calculate the charging capacity of the battery during the period when the open-circuit voltage value is within a preset voltage range; The preset voltage range is the range in which the battery's capacity decay changes significantly; The calculation of the battery's charging capacity during the period when the open-circuit voltage value is within a preset voltage range includes: activating the charging capacity metering when the open-circuit voltage value rises to the left end point Vmin of the preset voltage range; deactivating the charging capacity metering when the open-circuit voltage value continues to rise to the right end point Vmax of the preset voltage range; and calculating the charging capacity value using the ampere-hour integration method during the period when the charging capacity metering is activated. The preset voltage range is determined in advance through the following process: establishing an open-circuit voltage estimation model for the target battery model based on sample test data; conducting multiple charge-discharge cycles on the target battery model, monitoring the battery capacity and calculating the estimated open-circuit voltage during charging; generating IC curves for different charge-discharge cycles and determining the peak values ​​of each curve; wherein the vertical axis of the IC curve is the derivative of the battery capacity with respect to the estimated open-circuit voltage, and the horizontal axis is the estimated open-circuit voltage; the peak value of the curve with the most significant change under different charge-discharge cycles is determined as the target peak value; and the voltage variation range of the target peak value is determined as the preset voltage range. The capacity recognition model is invoked to determine the actual total capacity of the battery based on the charging capacity value corresponding to the preset voltage range. The capacity recognition model is pre-trained based on sample test data, which includes input data with the charging capacity value of the target battery model as the sample input data and output data with the actual total capacity value of the target battery model as the sample output data.

2. The online detection method according to claim 1, characterized in that, The real-time acquisition of the battery's charging status parameter data includes: The battery voltage, battery current, temperature, and battery state of charge are acquired in real time during charging.

3. The online detection method according to claim 2, characterized in that, The normalization calculation of the charging state parameter data to obtain the corresponding open-circuit voltage value in real time includes: A DC internal resistance estimation model for the target battery model was established based on sample test data. The real-time open-circuit voltage value is obtained based on the following normalized calculation formula: OCV(T,SOC)= V–I*R(T,SOC); Where V is the battery voltage; I is the battery current; T is the temperature; SOC is the battery state of charge; OCV(T, SOC) is the open-circuit voltage; and R(T, SOC) is the estimated DC internal resistance.

4. The online detection method according to claim 3, characterized in that, The capacity identification model is determined in advance through the following process: Multiple charge-discharge cycles were performed on the target battery model for which the actual total capacity was known, and the charging status parameter data were monitored in real time during the charging process. The corresponding open-circuit voltage value is obtained in real time through normalization calculation; Calculate the charging capacity of the target battery model during the period when the open-circuit voltage value is within the preset voltage range; Using the charging capacity value of the target battery model as sample input data and the actual total capacity value of the target battery model as sample output data, a capacity recognition model for the target battery model is trained and generated.

5. The online detection method according to claim 4, characterized in that, The training process generates the capacity identification model for the target battery model, including: The capacity identification model of the target battery model is generated by using data fitting or neural network model training.

6. The online detection method according to claim 4, characterized in that, The multiple charge-discharge tests performed on the target battery model with a known actual total capacity include: Different temperature and charging current conditions were set to perform multiple charge-discharge tests on the target battery model with a known actual total capacity.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the online battery capacity detection method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of the online battery capacity detection method as described in any one of claims 1 to 6.

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