CN114609523A - Online battery capacity detection method, electronic equipment and storage medium - Google Patents

Abstract

The application discloses a battery capacity online detection method, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring charging state parameter data of a battery in real time; performing normalization calculation on the charging state parameter data to obtain a corresponding open-circuit voltage value in real time; calculating the charging capacity value of the battery during the period that the value of the open-circuit voltage value is within a preset voltage interval; the preset voltage interval is a battery capacity attenuation obvious change interval; calling a capacity identification model, and determining the actual total capacity value of the battery according to the charging capacity value corresponding to the preset voltage interval; the capacity identification model is generated based on sample test data training in advance. According to the method and the device, data monitoring and calculation are carried out on the actual charging state of the battery, and then the actual total capacity value of the battery is matched and identified by using the capacity identification model generated by pre-training, so that the method and the device have high detection efficiency and result accuracy, the detection process is convenient and simple, and the method and the device are convenient to popularize and apply and can be used on line.

Description

On-line detection method, electronic device and storage medium for battery capacity

technical field

The present application relates to the field of battery technology, and in particular, to an online detection method of battery capacity, an electronic device, and a computer-readable storage medium.

Background technique

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 stations.

Capacity is a very basic indicator of a battery that indicates the maximum amount of energy that can be stored. It directly affects the state of charge and state of health of the battery. In general, batteries will continue to age under charging and discharging conditions, and their decay rate varies with ambient temperature and load conditions. Accurately identifying the maximum usable capacity of the battery during actual use is the focus and difficulty of power battery research at this stage.

In recent years, some battery capacity estimation/prediction methods have appeared in the prior art. One of them is the methods based on empirical models. Using empirical models such as capacity loss models, the prediction process is simple and easy to calculate, but the estimation accuracy is low. The other category is physical model-based methods, which use partial differential equations to quantify the electrochemical state, including the calculation of parameters such as the total number of active materials, the resistance of the solid-electrolyte interfacial film, the diffusion coefficient, etc.; It is a complex process simulation of the mechanism, so the accuracy is high, but the calculation amount is large, and it cannot be practically applied in engineering.

In view of this, providing a solution to the above-mentioned technical problems is an urgent need for those skilled in the art.

SUMMARY OF THE INVENTION

The purpose of this application is to provide an on-line detection method, electronic device and computer-readable storage medium for battery capacity, so that the battery capacity detection process requires less calculation, is convenient and easy to popularize, and has high result accuracy.

In order to solve the above technical problems, in the first aspect, the present application discloses an online detection method of battery capacity, including:

Acquiring the state-of-charge parameter data of the battery in real time;

normalizing and calculating the state-of-charge parameter data to obtain the corresponding open-circuit voltage value in real time;

calculating the charging capacity value of the battery when the open-circuit voltage value is within a preset voltage interval; the preset voltage interval is a significant change interval of the capacity attenuation of the battery;

The capacity identification model is invoked, and the actual total capacity value of the battery is determined according to the charging capacity value corresponding to the preset voltage interval; the capacity identification model is pre-trained and generated based on sample test data.

Optionally, obtaining the state-of-charge parameter data of the battery in real time includes:

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

Optionally, the normalized calculation of the state-of-charge parameter data to obtain the corresponding open-circuit voltage value in real time includes:

Establish the DC internal resistance estimation model of the target battery based on the sample test data;

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

OCV(T,SOC)=V-I*R(T,SOC);

Among them, 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 value; R(T, SOC) is the estimated value of the DC internal resistance.

Optionally, the preset voltage interval is determined in advance through the following process:

Based on the sample test data, establish the open circuit voltage estimation model of the target type battery;

Perform multiple cycle charge-discharge tests on the battery of the target type, monitor the battery capacity during the charging process, and calculate the open-circuit voltage estimate;

Generate IC curves under different number of cycles of charge and discharge and determine the peak value of each curve; wherein, the ordinate of the IC curve is the derivation value of the battery capacity to the estimated value of the open circuit voltage, and the abscissa is the estimated value of the open circuit voltage value;

Determine the peak value of the curve with the most significant changes under different cycles of charge and discharge as the target peak value;

The voltage variation interval of the target peak value is determined as the preset voltage interval.

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

Carry out multiple cycle charge-discharge tests on the target type battery whose actual total capacity value is known, and monitor the state of charge parameter data in real time during the charging process;

Obtain the corresponding open-circuit voltage value in real time through normalized calculation;

calculating the charging capacity value of the battery of the target type when the open-circuit voltage value is within the preset voltage interval;

Taking the charging capacity of the battery of the target model as the sample input data and the actual total capacity of the battery of the target model as the sample output data, train and generate the capacity identification model of the battery of the target model.

Optionally, the training generates the capacity identification model of the target model battery, including:

The capacity identification model of the battery of the target model is generated by a data fitting method or a neural network model training method.

Optionally, performing multiple cycle charge-discharge tests on the target model battery whose actual total capacity value is known includes:

Set different temperature conditions and charging current conditions, respectively, and perform multiple charge and discharge tests on the target type of battery whose actual total capacity value is known.

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

During the period when the open-circuit voltage value is within the preset voltage range, the ampere-hour integration method is used to calculate the charging capacity value of the battery.

In another aspect, the present application also discloses an electronic device, comprising:

memory for storing computer programs;

The processor is configured to execute the computer program to realize the steps of any one of the above-mentioned methods for on-line detection of battery capacity.

In another aspect, the present application also discloses a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, it is used to implement any of the above-mentioned batteries Steps of an on-line detection method for capacity.

The on-line detection method for battery capacity provided by the present application includes: obtaining the state-of-charge parameter data of the battery in real time; normalizing and calculating the state-of-charge parameter data to obtain the corresponding open circuit voltage value in real time; The charging capacity value of the battery when the value is within the preset voltage interval; the preset voltage interval is the significant change interval of the capacity attenuation of the battery; the capacity identification model is called, according to the value corresponding to the preset voltage interval The charging capacity value determines the actual total capacity value of the battery; the capacity identification model is pre-trained and generated based on sample test data.

The on-line detection method, electronic device and computer-readable storage medium of battery capacity provided by the present application have the beneficial effects that: the present application performs data monitoring and calculation on the actual state of charge of the battery, and then utilizes a capacity identification model generated by pre-training. , according to the charging capacity value in the preset voltage range to match and identify the actual total capacity value of the battery, not only has high detection efficiency and result accuracy, but also the whole detection process is convenient and simple, easy to popularize and apply, and can be used online , especially suitable for battery capacity detection of on-board battery systems that are inconvenient to disassemble in some large rail locomotives.

Description of drawings

In order to more clearly illustrate the prior art and the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings to be used in the description of the prior art and the embodiments of the present application. Of course, the following drawings related to the embodiments of the present application describe only a part of the embodiments of the present application. For those of ordinary skill in the art, without any creative effort, they can also obtain other embodiments according to the provided drawings. The accompanying drawings and other drawings obtained also belong to the protection scope of the present application.

FIG. 1 is a flowchart of a method for on-line detection of battery capacity disclosed in an embodiment of the present application;

FIG. 2 is a graph showing a decay curve of battery capacity with the number of cycles of charge and discharge disclosed in an embodiment of the present application;

FIG. 3 is a charging voltage curve diagram of a battery disclosed in an embodiment of the application under different cycles of charging and discharging;

4 is a flowchart of a method for determining a preset voltage interval disclosed in an embodiment of the present application;

5 is a schematic diagram of an IC curve disclosed in an embodiment of the present application;

6 is a flowchart of a method for training and generating a capacity identification model disclosed in an embodiment of the present application;

FIG. 7 is a structural block diagram of an electronic device disclosed in an embodiment of the present application.

Detailed ways

The core of the present application is to provide an online battery capacity detection method, an electronic device and a computer-readable storage medium, so that the battery capacity detection process requires less calculation, is convenient and easy to popularize, and has high result accuracy.

In order to describe the technical solutions in the embodiments of the present application more clearly and completely, the technical solutions in the embodiments of the present application will be introduced below with reference to the drawings in the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Capacity is a very basic indicator of a battery that indicates the maximum amount of energy that can be stored. It directly affects the state of charge and state of health of the battery. In general, batteries will continue to age under charging and discharging conditions, and their decay rate varies with ambient temperature and load conditions. Accurately identifying the maximum usable capacity of the battery during actual use is the focus and difficulty of power battery research at this stage.

In recent years, some battery capacity estimation/prediction methods have appeared in the prior art. One of them is the methods based on empirical models. Using empirical models such as capacity loss models, the prediction process is simple and easy to calculate, but the estimation accuracy is low. The other category is physical model-based methods, which use partial differential equations to quantify the electrochemical state, including the calculation of parameters such as the total number of active materials, the resistance of the solid-electrolyte interfacial film, the diffusion coefficient, etc.; It is a complex process simulation of the mechanism, so the accuracy is high, but the calculation amount is large, and it cannot be practically applied in engineering. In view of this, the present application provides a solution for on-line detection of battery capacity, which can effectively solve the above problems.

Referring to FIG. 1 , an embodiment of the present application discloses an online detection method for battery capacity, which mainly includes:

S101: Obtain the parameter data of the state of charge of the battery in real time.

S102: Normalize the charge state parameter data to obtain the corresponding open circuit voltage value in real time.

S103: Calculate the charging capacity value of the battery when the open-circuit voltage value is within a preset voltage range; the preset voltage range is a significant change range of the capacity attenuation of the battery.

S104: Call the capacity identification model, and determine the actual total capacity value of the battery according to the charging capacity value corresponding to the preset voltage interval; the capacity identification model is pre-trained and generated based on sample test data.

Specifically, the battery capacity of the power battery will gradually decay during use. Specifically, reference may be made to FIG. 2 , which is a graph showing a decay curve of battery capacity with the number of cycles of charge and discharge disclosed in an embodiment of the present application. At the same time, the applicant has also found in practical applications that with the increase of the number of cycles of charging and discharging of the battery, the charging voltage curve of the battery also changes, which is mainly manifested in the shortening of the charging voltage plateau period. Specifically, reference may be made to FIG. 3 , which is a charging voltage curve diagram of a battery disclosed in an embodiment of the present application under different number of cycles of charge and discharge.

In this regard, the applicant obtained through a comprehensive analysis of the above two change curves, that the change in the plateau period of the charging voltage related to the number of cycles of charge and discharge has a certain corresponding relationship with the attenuation of the battery capacity. Therefore, the present application proposes a technical solution for identifying the actual capacity of the battery by utilizing the charging characteristics of the battery during the charging voltage plateau.

Specifically, the present application obtains a large number of sample test data by performing charge-discharge tests and state monitoring on a large number of batteries with known actual capacity values in advance. capacity value, and then use the corresponding relationship between the actual capacity value of different batteries in the sample test data and their charging capacity value for training to obtain a capacity identification model, which can be matched and identified according to the charging capacity value of the specified battery type within the preset voltage range. The actual capacity value of the battery in the current state.

Wherein, the preset voltage interval corresponds to the charging voltage plateau period in the charging voltage curve, and is also the significant change interval of the capacity attenuation of the battery. It should be noted that the significant change range of capacity decay is a value range of the battery open circuit voltage. When the battery open circuit voltage is within this value range, it can be clearly seen that the capacity decay of the battery varies with the number of cycles of charge and discharge. However, significant changes, that is, the dQ/dV-V curves under different cycles of charge and discharge times will obviously not overlap within this preset voltage range. Among them, Q is the battery capacity, and V is the battery voltage.

It should be added that the application uses the charging capacity value in the preset voltage range to identify the actual total capacity value of the battery, and the state of charge value at the beginning of charging of the battery does not exceed 60% to achieve the detection effect of the application. Without having to fully require the battery to start charging from the 0V state. It can be seen that the present application is more in line with the actual application and has practicability.

Therefore, the on-line detection method of battery capacity disclosed in the present application can first obtain the parameter data of the state of charge 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 interval be [Vmin, Vmax], when the open-circuit voltage value rises to the left end Vmin of the preset voltage interval, the charging capacity meter is turned on, and when the open-circuit voltage value continues to rise to the right end Vmax of the preset voltage interval, Turn off the charging power metering, so that the charging capacity value of the battery during the period when the open-circuit voltage value is within the preset voltage range is obtained. Furthermore, by invoking the capacity identification model generated by pre-training, the actual total capacity value of the battery can be identified according to the charging capacity value matching.

It should also be noted that the online detection method of battery capacity disclosed in this application can be specifically applied to some in-vehicle devices, such as an in-vehicle battery power detection terminal, or a power battery management system; in addition, it can also be specifically applied to a cloud platform Among the cloud devices that can communicate with the in-vehicle network, those skilled in the art can choose by themselves according to the actual application, which is not limited in this application.

It can be seen that the online detection method of battery capacity provided by the present application performs data monitoring and calculation on the actual charging state of the battery, and then uses the capacity identification model generated by pre-training to match and identify the charging capacity value in the preset voltage range. The actual total capacity value of the battery not only has high detection efficiency and result accuracy, but also the whole detection process is convenient and simple, easy to popularize and apply, and can be used online, especially suitable for some large rail locomotives inconvenient to disassemble the on-board battery system battery capacity detection.

As a specific embodiment, the method for online detection of battery capacity provided by the embodiments of the present application acquires the parameter data of the state of charge of the battery in real time on the basis of the above content, including: acquiring in real time the battery voltage and battery current of the battery during charging , temperature, battery state of charge.

Further, the normalized calculation of the state-of-charge parameter data to obtain the corresponding open-circuit voltage value in real time may specifically include:

Establish the DC internal resistance estimation model of the target battery based on the sample test data;

Obtain the real-time corresponding open-circuit voltage value based on the following normalized calculation formula:

OCV(T,SOC)=V-I*R(T,SOC);

Among them, 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 value; R(T, SOC) is the estimated value of the DC internal resistance.

Among them, it should be noted that, for the DC internal resistance estimation model, a characteristic database can be established based on the DC internal resistance related test of a sample battery of known capacity, and the test data at different temperatures and different states of charge can be recorded, and then The DC internal resistance estimation model is established based on the sample test data of the DC internal resistance:

R(T,SOC)=Function1(T,SOC).

Referring to FIG. 4 , FIG. 4 is a flowchart of a method for determining a preset voltage interval disclosed in an embodiment of the present application. As a specific embodiment, as shown in FIG. 4 , the preset voltage interval can be determined in advance through the following process:

S201: Establish an open-circuit voltage estimation model of a battery of a target type based on the sample test data.

Specifically, for the open-circuit voltage estimation model of a battery of a certain target type, a feature database can be established based on testing the relevant parameters of a sample battery of known capacity, and the test data at different temperatures and states of charge can be recorded, and then based on Build an open-circuit voltage estimation model for sample test data of open-circuit voltage:

OCV(T, SOC)=Function2(T, SOC).

It should be added that, compared with 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 determining the preset voltage interval, and the accuracy is limited.

S202: Perform multiple cycle charge-discharge tests on the battery of the target type, monitor the battery capacity during the charging process, and calculate the estimated value of the open-circuit voltage.

Generally, an estimated value of the open circuit voltage at a specified temperature T ₀ can be selected by calculation, where T ₀ can be specifically a room temperature of 25°C.

S203: Generate IC curves under different number of cycles of charge and discharge and determine the peak values of each curve; wherein, the ordinate of the IC curve is the derivation value of the battery capacity to the estimated value of the open circuit voltage, and the abscissa is the estimated value of the open circuit voltage.

As mentioned above, there is a certain relationship between the length of the charging voltage plateau period of the battery and the capacity decay. The present application uses the charging capacity value during the charging voltage plateau period to determine the actual total capacity value of the battery. Specifically, in order to determine the preset voltage interval corresponding to the charging voltage plateau period, the present application draws the derivation value dQ/dV-V curve of the estimated value of the open circuit voltage of the battery capacity under different cycles of charging and discharging, also known as the IC curve. . Among them, Q is the battery capacity, and V is the battery voltage.

For details, please refer to FIG. 5 , which is a schematic diagram of an IC curve disclosed in an embodiment of the present application. As shown in Figure 5, three curve peaks appear in the dQ/dV-V curve: peak1, peak2, and peak3. Among them, with the increase of the number of cycles of charge and discharge, the peak value peak2 of the curve has the most obvious change, the curve has the largest misalignment, and the spanned voltage interval is the largest. Therefore, the peak value peak2 of the curve is the target peak value; and the voltage change interval spanned by the target peak value peak2 is determined as the preset voltage interval.

S204: Determine the peak value of the curve with the most significant changes under different number of cycles of charge and discharge as the target peak value.

S205: Determine the voltage variation interval of the target peak value as a preset voltage interval.

Referring to FIG. 6, FIG. 6 is a flowchart of a method for training and generating a capacity identification model disclosed in an embodiment of the present application. As a specific embodiment, as shown in Figure 6, the capacity identification model can be determined in advance through the following process:

S301: Perform multiple cycle charge-discharge tests on the target type battery whose actual total capacity value is known, and monitor the parameter data of the state of charge in real time during the charging process.

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

S303: Calculate the charging capacity value of the battery of the target type when the open-circuit voltage value is within the preset voltage range.

S304: Using the charging capacity of the target battery as the sample input data and the actual total capacity of the target battery as the sample output data, train and generate a capacity identification model for the target battery.

Wherein, further, as a specific embodiment, training to generate the capacity identification model of the battery of the target type includes: generating the capacity identification model of the battery of the target type by using a data fitting method or a neural network model training method.

As a specific embodiment, the online detection method for battery capacity provided by the embodiment of the present application performs multiple cycle charge-discharge tests on a target type battery whose actual total capacity value is known during training to generate a capacity identification model, which may specifically include: : Set different temperature conditions and charging current conditions respectively, and perform a single charge-discharge test on the target type battery whose actual total capacity value is known.

Among them, in order to eliminate multi-factor interference and avoid inaccurate calculation results, when charging and discharging the target type battery each time, you can choose to charge and discharge with a constant current at a constant temperature, so that the calculation results can be obtained at the constant temperature. , the charging capacity value under the condition of constant current. Then continue to replace a variable in temperature and current, so as to obtain test data under various conditions to train the capacity identification model, so that more accurate results can be obtained under different conditions.

As a specific embodiment, the online detection method for battery capacity provided by the embodiment of the present application calculates the charging capacity value of the battery when the open-circuit voltage value is within the preset voltage range on the basis of the above content, including: When the open-circuit voltage value is within the preset voltage range, the ampere-hour integration method is used to calculate the charging capacity value of the battery.

Among them, it is easy to understand that, in the IC curve graph, the area of the curve within the preset voltage range is the charging capacity value to be calculated.

Referring to FIG. 7 , an embodiment of the present application discloses an electronic device, including:

memory 401 for storing computer programs;

The processor 402 is configured to execute the computer program to implement the steps of any of the above-mentioned methods for on-line detection of battery capacity.

Further, an embodiment of the present application also discloses a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is used to implement any of the above when executed by a processor. The steps of the on-line detection method of battery capacity.

For the specific content of the electronic device and the computer-readable storage medium, reference may be made to the foregoing detailed introduction on the online detection method of the battery capacity, which will not be repeated here.

The various embodiments in this application are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments may be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

It should also be noted that, in this application document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require Or imply that there is any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The technical solutions provided by the present application have been introduced in detail above. Specific examples are used herein to illustrate the principles and implementations of the present application, and the descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. It should be pointed out that for those skilled in the art, without departing from the principles of the present application, several improvements and modifications can also be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims (10)

1. An online detection method for battery capacity is characterized by comprising the following steps:

acquiring charging state parameter data of the battery in real time;

performing normalization calculation on the charging state parameter data to obtain a corresponding open-circuit voltage value in real time;

calculating the charging capacity value of the battery during the period that the value of the open-circuit voltage value is within a preset voltage interval; the preset voltage interval is a capacity attenuation significant change interval of the battery;

calling a capacity identification model, and determining the actual total capacity value of the battery according to the charging capacity value corresponding to the preset voltage interval; the capacity identification model is generated based on sample test data training in advance.

2. The on-line detection method according to claim 1, wherein the acquiring the charging state parameter data of the battery in real time comprises:

and acquiring the battery voltage, the battery current, the temperature and the battery charge state of the battery in real time when the battery is charged.

3. The on-line detection method according to claim 2, wherein the normalizing the charging state parameter data to obtain the corresponding open-circuit voltage value in real time comprises:

establishing a direct current internal resistance estimation model of a target type battery based on sample test data;

acquiring the real-time corresponding open-circuit voltage value based on the following normalized calculation formula:

OCV(T，SOC)＝V–I*R(T，SOC)；

wherein V is the battery voltage; i is the battery current; t is the temperature; SOC is the state of charge of the battery; OCV (T, SOC) is an open circuit voltage value; r (T, SOC) is a direct current internal resistance estimated value.

4. The on-line detection method according to claim 3, characterized in that the preset voltage interval is determined in advance by the following procedure:

establishing an open-circuit voltage estimation model of a target model battery based on sample test data;

carrying out multiple cyclic charge and discharge tests on a target model battery, monitoring the battery capacity in the charging process and calculating an estimated value of open-circuit voltage;

generating IC curves under different cyclic charge and discharge times and determining peak values of the curves; the ordinate of the IC curve is a derivative value of the battery capacity to the estimated value of the open-circuit voltage, and the abscissa of the IC curve is the estimated value of the open-circuit voltage;

determining the curve peak value which changes most obviously under different cyclic charge and discharge times as a target peak value;

and determining the voltage change interval of the target peak value as the preset voltage interval.

5. The on-line detection method according to claim 4, wherein the capacity recognition model is determined in advance by the following procedure:

carrying out multiple cyclic charge and discharge tests on a target type battery with a known actual total capacity value, and monitoring charge state parameter data in real time in the charging process;

acquiring a corresponding open-circuit voltage value in real time through normalization calculation;

calculating the charging capacity value of the target model battery during the period that the value of the open-circuit voltage value is within the preset voltage interval;

and training and generating the capacity recognition model of the target model battery by taking the charging capacity value of the target model battery as sample input data and taking the actual total capacity value of the target model battery as sample output data.

6. The on-line detection method of claim 5, wherein the training generates the capacity recognition model of the target model battery, comprising:

and generating the capacity identification model of the target type battery by adopting a data fitting mode or a neural network model training mode.

7. The on-line detection method of claim 5, wherein the multi-cycle charge and discharge test of the target type battery with a known actual total capacity value comprises:

different temperature conditions and charging current conditions are set respectively, and multiple charging and discharging tests are executed on the target type battery with a known actual total capacity value.

8. The on-line detection method according to any one of claims 1 to 7, wherein the calculating the value of the charge capacity of the battery during the period when the value of the open-circuit voltage is within a preset voltage interval comprises:

and calculating the charging capacity value of the battery by adopting an ampere-hour integration method during the period that the value of the open-circuit voltage value is within the preset voltage interval.

9. An electronic device, comprising:

a memory for storing a computer program;

a processor for executing said computer program to implement the steps of the method for online detection of battery capacity according to any of claims 1 to 8.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method for online detection of battery capacity according to any one of claims 1 to 8.

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