CN114609523A - Online battery capacity detection method, electronic equipment and storage medium - Google Patents
Online battery capacity detection method, electronic equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/005—Testing of electric installations on transport means
- G01R31/008—Testing of electric installations on transport means on air- or spacecraft, railway rolling stock or sea-going vessels
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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
Technical Field
The present disclosure relates to the field of battery technologies, and in particular, to an online detection method for battery capacity, an electronic device, and a computer-readable storage medium.
Background
Lithium ion (Li-ion) batteries play a key role today in traffic electrification and renewable energy systems as one of the main energy storage devices for electric cars and power stations.
Capacity is a very basic indicator of a battery that indicates the maximum amount of energy that can be stored. Which directly affects the state of charge and state of health of the battery. Generally, batteries continue to age under charge and discharge conditions, with the rate of decay varying with ambient temperature and load conditions. The accurate identification of the maximum available capacity of the battery in the actual use process is the key point and difficulty of the research of the power battery at the present stage.
In recent years, some methods for estimating/predicting battery capacity have appeared in the prior art. One of the methods is an empirical model-based method, and the prediction process is simple and easy to calculate by using an empirical model such as a capacity loss model, but the estimation accuracy is low. Another is a physical model-based method that uses partial differential equations to quantify the electrochemical state, including the calculation of the total number of active substances, the resistance of the solid electrolyte interface film, the diffusion coefficient, and the like; the method is a complex process simulation of a battery degradation mechanism, so that the accuracy is high, but the calculation amount is large, and the method cannot be applied in practical engineering.
In view of the above, it is an important need for those skilled in the art to provide a solution to the above technical problems.
Disclosure of Invention
The application aims to provide an online detection method of battery capacity, electronic equipment and a computer readable storage medium, so that the detection process of the battery capacity has small calculation amount, is convenient and easy to popularize and has high result accuracy.
In order to solve the above technical problem, in a first aspect, the present application discloses an online detection method for battery capacity, including:
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.
Optionally, the acquiring the charge state parameter data of the battery in real time includes:
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.
Optionally, the performing normalization calculation on the charge state parameter data to obtain a corresponding open-circuit voltage value in real time includes:
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.
Optionally, the preset voltage interval is determined in advance by the following process:
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 cycle 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.
Optionally, the capacity identification model is determined in advance by the following process:
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.
Optionally, the training generates the capacity recognition model of the target model battery, including:
and generating the capacity identification model of the target type battery by adopting a data fitting mode or a neural network model training mode.
Optionally, the performing a multiple-cycle charge and discharge test on a target model battery with a known actual total capacity value includes:
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.
Optionally, the calculating a charge capacity value of the battery during a period when the value of the open-circuit voltage is within a preset voltage interval includes:
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.
In another aspect, the present application also discloses an electronic device, including:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of any one of the above-described online battery capacity detection methods.
In yet another aspect, the present application further discloses a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, is used for implementing the steps of any one of the above-mentioned online battery capacity detection methods.
The online detection method for the battery capacity comprises 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.
The battery capacity online detection method, the electronic device and the computer-readable storage medium have the advantages that: the method and the device have the advantages that data monitoring and calculation are carried out on the actual charging state of the battery, then the capacity recognition model generated by pre-training is utilized, the actual total capacity value of the battery is matched and recognized according to the charging capacity value in the preset voltage interval, high detection efficiency and result accuracy are achieved, the whole detection process is convenient, simple and convenient, the popularization and the application are convenient, the online use can be realized, and the method and the device are particularly suitable for detecting the battery capacity of the vehicle-mounted battery system inconvenient to detach in large-scale rail locomotives.
Drawings
In order to more clearly illustrate the technical solutions in the prior art and the embodiments of the present application, the drawings that are needed to be used in the description of the prior art and the embodiments of the present application will be briefly described below. Of course, the following description of the drawings related to the embodiments of the present application is only a part of the embodiments of the present application, and it will be obvious to those skilled in the art that other drawings can be obtained from the provided drawings without any creative effort, and the obtained other drawings also belong to the protection scope of the present application.
Fig. 1 is a flowchart of an online battery capacity detection method disclosed in an embodiment of the present application;
FIG. 2 is a graph showing the decay of battery capacity with the number of cycles of charging and discharging according to an embodiment of the present disclosure;
fig. 3 is a charging voltage curve diagram of a battery disclosed in the embodiment of the present application under different numbers of cycles of charging and discharging;
FIG. 4 is a flowchart of a method for determining a predetermined voltage interval according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of an IC curve according to an embodiment of the present disclosure;
FIG. 6 is a flowchart of a method for training a generative capacity recognition model, as disclosed in an embodiment of the present application;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The core of the application is to provide an online detection method of battery capacity, electronic equipment and a computer readable storage medium, so that the detection process of the battery capacity has small calculation amount, is convenient and easy to popularize and has high result accuracy.
In order to more clearly and completely describe the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to 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. Which directly affects the state of charge and state of health of the battery. Generally, batteries continue to age under charge and discharge conditions, with the rate of decay varying with ambient temperature and load conditions. The accurate identification of the maximum available capacity of the battery in the actual use process is the key point and difficulty of the research of the power battery at the present stage.
In recent years, some methods for estimating/predicting battery capacity have appeared in the prior art. One of the methods is an empirical model-based method, and the prediction process is simple and easy to calculate by using an empirical model such as a capacity loss model, but the estimation accuracy is low. Another is a physical model-based method that uses partial differential equations to quantify the electrochemical state, including the calculation of the total number of active substances, the resistance of the solid electrolyte interface film, the diffusion coefficient, and the like; the method is a complex process simulation of a battery degradation mechanism, so that the accuracy is high, but the calculation amount is large, and the method cannot be applied in practical engineering. In view of this, the present application provides a solution for online detection of battery capacity, which can effectively solve the above-mentioned problems.
Referring to fig. 1, an embodiment of the present application discloses an online detection method for battery capacity, which mainly includes:
s101: and acquiring the charging state parameter data of the battery in real time.
S102: and carrying out normalization calculation on the charging state parameter data to obtain a corresponding open-circuit voltage value in real time.
S103: 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 an interval in which the capacity of the battery is remarkably changed in attenuation.
S104: 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.
Specifically, the battery capacity of the power battery gradually decreases during the use process. Specifically, referring to fig. 2, fig. 2 is a graph illustrating a battery capacity decay curve with the number of cycles of charging and discharging according to an embodiment of the present disclosure. Meanwhile, in practical application, the applicant also finds that as the number of times of cycle charging and discharging of the battery increases, the charging voltage curve of the battery also changes, which is mainly represented as the charging voltage plateau period is shortened. Specifically, referring to fig. 3, fig. 3 is a charging voltage curve chart of a battery according to an embodiment of the present disclosure under different charge and discharge cycles.
In contrast, the applicant obtained a comprehensive analysis by combining the two variation curves, and the change in the charging voltage plateau period related to the number of cycles of charge and discharge had a certain correspondence with the decay in the battery capacity. Therefore, the technical scheme of identifying the actual capacity of the battery by using the charging characteristics of the battery during the charging voltage platform is provided.
Specifically, this application carries out charge-discharge test and state monitoring through the battery to a large amount of actual capacity values are known in advance to obtain a large amount of sample test data, sample test data includes the charge capacity value and the actual capacity value of these sample batteries in predetermineeing the voltage interval, then utilize the actual capacity value of different batteries in the sample test data and its corresponding relation of charge capacity value to train, obtain the capacity identification model, thereby can be according to the charge capacity value of appointed model battery in predetermineeing the voltage interval, match the discernment and obtain the actual capacity value of this battery under current state.
The preset voltage interval corresponds to a charging voltage plateau period in a charging voltage curve and is also an interval in which the capacity attenuation of the battery is remarkably changed. It should be noted that the significant capacity fading variation interval is a value interval of the open-circuit voltage of the battery, and when the open-circuit voltage of the battery is in the value interval, it can be obviously seen that the capacity fading condition of the battery significantly varies with different cyclic charge and discharge times, that is, the dQ/dV-V curves under different cyclic charge and discharge times will obviously deviate in the preset voltage interval. Where Q represents the battery capacity and V is the battery voltage.
It should be added that, the present application utilizes the charging capacity value in the preset voltage interval to identify the actual total capacity value of the battery, and the state of charge value at the beginning of charging the battery does not exceed 60%, so as to achieve the detection effect of the present application, without completely requiring the battery to start charging from the 0V state. Therefore, the method and the device more accord with the practical application condition and have practicability.
Therefore, the online detection method for the battery capacity can acquire the charging state parameter data of the battery in the charging process, and then calculate the real-time open-circuit voltage value of the battery through normalization processing. And setting a preset voltage interval as [ Vmin, Vmax ], starting the charging capacity metering when the open-circuit voltage value rises to a left end point Vmin of the preset voltage interval, and closing the charging capacity metering when the open-circuit voltage value continues to rise to a right end point Vmax of the preset voltage interval, so that the charging capacity value of the battery during the period that the open-circuit voltage value takes the value in the preset voltage interval is obtained. Furthermore, the actual total capacity value of the battery can be matched and identified according to the charging capacity value by calling a capacity identification model generated by pre-training.
It should be further noted that the online detection method for battery capacity disclosed in the present application may be specifically applied to some vehicle-mounted devices, such as a vehicle-mounted battery capacity detection terminal, or a power battery management system, etc.; in addition, the method and the device can be specifically applied to cloud equipment such as a cloud platform and the like which can communicate with a vehicle-mounted network, and technicians in the field can select the cloud equipment according to actual application conditions, and the method and the device are not limited in the application.
Therefore, the online detection method for the battery capacity carries out data monitoring and calculation on the actual charging state of the battery, and then utilizes the capacity recognition model generated by pre-training to match and recognize the actual total capacity value of the battery according to the charging capacity value in the preset voltage interval.
As a specific embodiment, the online detection method for battery capacity provided in the embodiment of the present application, based on the above contents, obtains, in real time, the charging state parameter data of the battery, and includes: and acquiring the battery voltage, the battery current, the temperature and the battery charge state of the battery in real time during charging.
Furthermore, the normalization calculation of the charge state parameter data to obtain a corresponding open-circuit voltage value in real time may specifically include:
establishing a direct current internal resistance estimation model of a target type battery based on sample test data;
acquiring a 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.
It should be noted that, for the dc internal resistance estimation model, the characteristic database may be specifically established based on performing dc internal resistance related tests on a sample battery with a known capacity, and the test data at different temperatures and different charge states are recorded, so as to establish the dc internal resistance estimation model 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 according to an embodiment of the present disclosure. As a specific example, as shown in fig. 4, the preset voltage interval may be determined in advance through the following process:
s201: and establishing an open-circuit voltage estimation model of the target model battery based on the sample test data.
Specifically, for an open-circuit voltage estimation model of a battery of a certain target model, a characteristic database can be established based on a relevant parameter test of a sample battery with known capacity, test data at different temperatures and different charge states are recorded, and then the open-circuit voltage estimation model is established based on the sample test data of the open-circuit voltage:
OCV(T，SOC)＝Function2(T，SOC)。
it should be added that, compared to the aforementioned normalized calculation formula, the open-circuit voltage estimation model is only used for roughly estimating the open-circuit voltage value when determining the preset voltage interval, and the accuracy is limited.
S202: and carrying out multiple times of cyclic charge-discharge tests on the target type battery, monitoring the battery capacity in the charging process and calculating an estimated value of the open-circuit voltage.
In general, the calculation can be selected to be obtained at a specified temperature T0Estimated value of open circuit voltage of where T0It may be specifically room temperature 25 ℃.
S203: 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 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 described above, there is a certain relationship between the length of the charging voltage plateau of the battery and the decay of the capacity, and the present application determines the actual total capacity value of the battery by using the charging capacity value during the charging voltage plateau. Specifically, in order to determine a preset voltage interval corresponding to a charging voltage plateau, a derivative value dQ/dV-V curve, also called an IC curve, of the battery capacity to an open-circuit voltage estimated value under different cycle charging and discharging times is drawn. Where Q represents the battery capacity and V is the battery voltage.
Referring to fig. 5, fig. 5 is a schematic diagram of an IC curve disclosed in the embodiment of the present application. As shown in FIG. 5, three peaks of the curve appear in the dQ/dV-V curve: peak1, peak2, peak 3. The peak value peak2 of the curve changes most obviously with the increase of the number of times of cyclic charge and discharge, the non-overlapping degree of the curve is the largest, and the spanned voltage interval width is the largest. Therefore, the peak value peak2 of the curve is the target peak value; the voltage variation interval spanned by the target peak value peak2 is determined as a preset voltage interval.
S204: and determining the peak value of the curve which has the most remarkable change under different cyclic charge and discharge times as a target peak value.
S205: and determining the voltage change 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 a generative capacity recognition model according to an embodiment of the present disclosure. As a specific example, as shown in fig. 6, the capacity recognition model may be determined in advance by the following process:
s301: and carrying out multiple cyclic charge and discharge tests on the target type battery with a known actual total capacity value, and monitoring charge state parameter data in real time in the charging process.
S302: and acquiring a corresponding open-circuit voltage value in real time through normalization calculation.
S303: and 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.
S304: and training to generate a 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.
Further, as a specific embodiment, training to generate a capacity recognition model of a battery of a target model includes: and generating a capacity identification model of the target type battery by adopting a data fitting mode or a neural network model training mode.
As a specific embodiment, the online detection method for battery capacity provided in the embodiment of the present application, when training to generate a capacity identification model, performs multiple cycle charge and discharge tests on a battery of a target model with a known actual total capacity value, and may specifically include: different temperature conditions and charging current conditions are set respectively, and a single charge-discharge test is executed on a target type battery with a known actual total capacity value.
In order to eliminate multi-factor interference and avoid causing inaccurate calculation results, when a charge and discharge test is performed on a target type battery every time, the charge and discharge can be performed in a constant current mode at a constant temperature so as to calculate and obtain a charge capacity value under the constant temperature and constant current condition. And then, one variable in the temperature and the current is replaced singly, so that test data under various conditions is obtained to train a capacity recognition model, and accurate results can be obtained under different conditions.
As a specific embodiment, the method for online detecting battery capacity provided in the embodiment of the present application, based on the above contents, calculates a charging capacity value of a battery during a period when an open-circuit voltage value is within a preset voltage interval, including: and during the period that the value of the open-circuit voltage value is within the preset voltage interval, calculating the charge capacity value of the battery by adopting an ampere-hour integration method.
It is easy to understand that, in the IC graph, the area of the curve in the preset voltage interval is the value of the charge capacity to be calculated.
Referring to fig. 7, an embodiment of the present application discloses an electronic device, including:
a memory 401 for storing a computer program;
a processor 402 for executing said computer program to implement the steps of any of the above described online detection methods of battery capacity.
Further, the present application also discloses a computer-readable storage medium, in which a computer program is stored, and the computer program is used for implementing the steps of any one of the above-mentioned online battery capacity detection methods when being executed by a processor.
For the details of the electronic device and the computer-readable storage medium, reference may be made to the foregoing detailed description of the online detection method for battery capacity, and details thereof are not repeated here.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the equipment disclosed by the embodiment, the description is relatively simple because the equipment corresponds to the method disclosed by the embodiment, and the relevant parts can be referred to the method part for description.
It is further noted that, throughout this document, relational terms such as "first" and "second" are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The technical solutions provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall into 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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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449209A (en) * | 2023-01-12 | 2023-07-18 | 帕诺(常熟)新能源科技有限公司 | Actual operation energy storage lithium capacitance prediction method based on LSTM |
| CN116893357A (en) * | 2023-07-07 | 2023-10-17 | 中国人民解放军国防科技大学 | Key battery screening method, system and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449209A (en) * | 2023-01-12 | 2023-07-18 | 帕诺(常熟)新能源科技有限公司 | Actual operation energy storage lithium capacitance prediction method based on LSTM |
| CN116893357A (en) * | 2023-07-07 | 2023-10-17 | 中国人民解放军国防科技大学 | Key battery screening method, system and storage medium |
| CN116893357B (en) * | 2023-07-07 | 2024-03-19 | 中国人民解放军国防科技大学 | Key battery screening method, system and storage medium |
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