CN116577686A - Multi-working condition SOH estimation method and system based on local stage charging data - Google Patents
Multi-working condition SOH estimation method and system based on local stage charging data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 93
- 238000007600 charging Methods 0.000 title claims abstract description 49
- 230000036541 health Effects 0.000 claims abstract description 180
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 162
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 162
- 238000013507 mapping Methods 0.000 claims abstract description 58
- 230000032683 aging Effects 0.000 claims abstract description 42
- 238000002474 experimental method Methods 0.000 claims abstract description 35
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 33
- 238000013528 artificial neural network Methods 0.000 claims abstract description 25
- 230000008569 process Effects 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 17
- 238000010280 constant potential charging Methods 0.000 claims description 11
- 238000010277 constant-current charging Methods 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 4
- 210000004027 cell Anatomy 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 208000028659 discharge Diseases 0.000 description 78
- 238000010586 diagram Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 4
- 238000004146 energy storage Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000000157 electrochemical-induced impedance spectroscopy Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000010281 constant-current constant-voltage charging Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 208000032953 Device battery issue Diseases 0.000 description 1
- 229910010707 LiFePO 4 Inorganic materials 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
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- 230000007613 environmental effect Effects 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
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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
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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]
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Abstract
The invention discloses a multi-working condition SOH estimation method and system based on local stage charging data, wherein the method comprises the following steps: s1: under different working conditions, carrying out a cyclic charge-discharge experiment on the lithium battery to obtain a battery aging cyclic database under different working conditions; s2: extracting health factors based on the battery aging cycle database, and acquiring a mapping parameter library of the health factors and the health state of the lithium battery; s3: establishing a health state estimation model of the lithium battery based on a neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions; s4: and selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model. The method solves the limitation that complete charging data is needed in the existing method, and can accurately estimate SOH under multiple working conditions for multiple working conditions.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a multi-working condition SOH estimation method and system based on local stage charging data.
Background
The lithium battery has the advantages of high energy density, long service life and the like, is widely applied to new energy automobiles, large-scale power grid energy storage power stations and the field of military aerospace at present, and is an excellent energy storage tool. The performance of the lithium battery itself may be degraded due to various factors such as temperature, charge-discharge cycles, and aging. While inaccurate estimation of battery state of health may lead to premature battery failure, reduced battery life, and reduced system performance. Thereby causing the increase of maintenance cost and the decrease of productivity, and even having potential safety hazards in key applications such as electric automobiles, military aerospace systems and the like. Therefore, accurate and reliable lithium battery state of health estimation is particularly important for energy storage and grid stabilization in large-scale energy storage power stations.
At present, the estimation method of the lithium battery health state comprises the following steps:
1. the ampere-hour integration method estimates the health state by accumulating the charged or discharged electric quantity, and can obtain an accurate SOH value, but the requirement is difficult to meet due to different use conditions in practice under the working condition that the battery is required to be fully charged and fully discharged.
2. The electrochemical impedance spectroscopy (Electrochemical Impedance Spectroscopy, EIS) measures the impedance information of the battery, and establishes a mapping relation with SOH, and the method can accurately estimate the health state of the battery, but has strict measurement conditions, needs professional equipment and is difficult to be practically applied.
3. The method based on the model is characterized in that the electrical model is used for identifying a plurality of model parameters so as to estimate SOH, and the method is high in robustness, but sensitive to the accuracy of parameter identification and difficult to apply online.
4. Based on the data driving method, the direct mapping relation with SOH is established by extracting the relevant characteristic parameters in the current and voltage data, the method has high precision and convenient application, but the quality of a data set can directly influence the estimation effect, and the method needs the charging or discharging data in the full SOC range, but most of the situations in the practical application of the battery can only obtain a few fragments, so the application range of the method is still to be strengthened.
Disclosure of Invention
The invention aims to provide a multi-working-condition SOH estimation method and system based on local-stage charging data, which solve the limitation that complete charging data is required in the existing method and can realize accurate estimation of SOH under multiple working conditions under the complex condition of multiple working conditions.
In order to achieve the above object, the present invention provides a multi-working condition SOH estimation method based on local phase charging data, including:
s1: under different working conditions, carrying out a cyclic charge-discharge experiment on the lithium battery to obtain a battery aging cyclic database under different working conditions;
s2: extracting health factors based on the battery aging cycle database, and acquiring a mapping parameter library of the health factors and the health state of the lithium battery;
s3: establishing a health state estimation model of the lithium battery based on a neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions;
s4: and selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model.
Further, S1, under different working conditions, carrying out a cyclic charge-discharge experiment on the lithium battery to obtain a battery aging cyclic database under different working conditions, wherein the method comprises the following steps:
s101: at a fixed temperature, carrying out a charging experiment on the lithium battery by adopting a constant-current and constant-voltage charging method at a preset charging multiplying power, so that the current of the lithium battery reaches a preset cut-off current; after the lithium battery is kept stand for a first preset time, a discharge experiment of a constant-current discharge method is adopted for the lithium battery at a preset discharge rate, so that the voltage of the lithium battery reaches a preset cut-off voltage; wherein the predetermined charge rate and the predetermined discharge rate are identical;
S102: firstly, performing constant-current and then constant-voltage charging experiments on the lithium battery according to the required charging multiplying power, namely, performing constant-current charging experiments on the lithium battery until the voltage reaches a preset cut-off voltage, and performing constant-voltage charging experiments until the current reaches a preset cut-off current;
s103: performing a constant-current discharge experiment on the lithium battery according to the required discharge multiplying power to ensure that the discharge depth of the lithium battery reaches the required discharge depth;
s104: repeating the steps S102-S103 until the battery capacity of the lithium battery is reduced to a preset capacity, and executing the step S101 once and performing primary battery capacity calibration when the first preset times are repeated;
s105: according to the experimental result of the cyclic charge-discharge experiment, a battery aging cyclic database corresponding to the working condition at a fixed temperature is established;
wherein, different working conditions represent different charge-discharge depths and different charge-discharge multiplying powers.
Further, in steps S102 and S103, the multiplying power of the lithium battery in the constant-current charging or discharging stage under the same working condition is consistent, and the predetermined cutoff current of the lithium battery in the constant-voltage charging stage and the predetermined cutoff voltage in the constant-current charging stage in different cycles are respectively kept consistent;
in step S105, after performing data preprocessing of filling the missing value and correcting the error value on the experimental result of the cyclic charge-discharge experiment, the battery aging cyclic database is established;
The experimental result is current information, voltage information and temperature information of the lithium battery in the cyclic charge and discharge experimental process;
the missing value is a position where a current value or a voltage value or a temperature value or a capacity value at a certain moment is in a vacancy, and the error value is a position where the current value or the voltage value or the temperature value or the capacity value at a certain moment is compared with the average value of the front data and the back data to exceed the preset number;
the data preprocessing comprises the following steps: spline interpolation function, average value function, correct and smooth missing value and error value.
Further, S2: extracting health factors based on the battery aging cycle database, and obtaining a mapping parameter library of the health factors and the health state of the lithium battery, wherein the method comprises the following steps:
s201, fitting battery capacity calibration cycles under each working condition based on the battery aging cycle database, obtaining accurate capacity of each cycle, and fitting capacity increment curves or current curves of each stage;
s202, repeatedly executing the step S201 to obtain standard capacity of all cycles under each working conditionAnd carrying out parameter identification on the capacity increment curve or the current curve of each stage to obtain a health factor parameter set under each working condition, namely a mapping parameter library of the health factors and the health states of the lithium battery.
Further, S201, based on the battery aging cycle database, fitting a capacity calibration cycle under each working condition to obtain accurate capacity of each cycle, and fitting a capacity increment curve or a current curve of each stage, including:
selecting data with the same working condition from the battery aging cycle database, and extracting the charge and discharge capacity in each cycle by using an ampere-hour integration methodCharging capacity at constant current stage->Charge capacity during constant voltage phase>；
Selecting capacity calibration circulation under the same working condition from the battery aging circulation database to obtain capacity calibration circulation capacity；
Calibrating the capacity to the circulating capacityAdopting cubic spline interpolation to obtain standard capacity of all cycles under a specified working condition>；
Processing charging data of a complete constant current stage and a partial constant current stage in the battery aging cycle database by using a Gauss filter function, and constructing a corresponding capacity increment curve;
selecting a peak value of a corresponding capacity increment curve, and taking the peak value position and the peak value width as health factors;
fitting a current curve of a high SOC stage by using a Sigmoid function and a least square method based on constant voltage stage data in the battery aging cycle database;
The complete constant current stage represents a charge-discharge cycle process of a lithium battery with a first preset charge-discharge depth, the partial constant current stage represents a charge-discharge cycle process of the lithium battery with a second preset charge-discharge depth, and the first preset charge-discharge depth is larger than the second preset charge-discharge depth;
the high SOC stage represents a charge-discharge cycle process in which the lithium battery is at a third predetermined charge-discharge depth, the third predetermined charge-discharge depth being not less than 20% of the charge-discharge depth.
Further, the health factor is represented by the following formula:
；
wherein ,indicating battery capacity,/->Represents lithium battery voltage in a single cycle, +.>Representing a voltage sequence of the lithium battery in a single cycle; />Indicate->Peak value,/->Representing the number of peaks; />Representing natural constant->Indicate->Peak positions; />Indicate->The peak width.
Further, before fitting the current curve of the high SOC stage, normalizing the high SOC stage data, including:
when the duration value isIn case of fluctuation greater than the second predetermined time, the end time of the high SOC phase is +.>Is standard; the normalized high SOC stage interval is:
；
wherein ,Representing a normalized high SOC phase interval, +.>Indicates the end time of the high SOC phase, +.>Representing a second predetermined time; or alternatively, the first and second heat exchangers may be,
when the duration value isGreater than a second predetermined time and a duration value +.>In case of fluctuation of less than or equal to the second predetermined time, the high SOC stage end time +.>As the starting point position of the high SOC stage in each cycle under each working condition, normalizing the original point position to obtain a normalized high SOC stage section as follows:
；
wherein ,indicates the start time of the high SOC phase current, +.>Indicating the duration of the high SOC phase.
Further, S3: establishing a health state estimation model of the lithium battery based on a neural network, and establishing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions, wherein the method comprises the following steps:
s301: constructing an input layer, an hidden layer and an output layer as a topological structure of the neural network;
s302: learning an initial estimation model of the lithium battery health state by using a neural network, and adjusting the weight of each node according to the error;
s303: training the initial estimation model, and selecting the initial estimation model with the minimum error value in the second preset number of iterations as a final estimation model, namely a lithium battery health state estimation model under the corresponding working condition;
S304: repeating the steps S301-S303 to obtain the health state estimation models under different working conditions; and constructing a mapping model library of the health factors and the health states of the lithium battery according to the health state estimation models and the mapping parameter library under different working conditions.
Further, S302: learning an initial estimation model of the lithium battery health state by using a neural network, and adjusting the weight of each node according to the error, wherein the learning comprises the following steps:
in the learning process of the initial estimation model, the node weight of the neural network is corrected to minimize and output the overall error, so that the adjustment of the node weight is realized;
acquiring the correction quantity of each node weight through gradient descent;
in the learning process of the initial estimation model, a node is outputIs>The error of the data points is expressed as:
；
in the formula ,representing the output node +.>Is>Error of data points, ++>Representing the output node +.>Is>Data point target value, target value is the lithium battery health state in the mapping model library, +.>Representing a predicted value of the initial estimation model on the state of health of the lithium battery;
the node weight minimization output overall error by correcting the neural network is:
；
in the formula ,representing the overall error;
the correction amount of each node weight is as follows:
；
in the formula ,indicate->The (th) of the number of layers>Correction of the weight of individual nodes, +.>Indicate->The (th) of the number of layers>Weights of individual nodes, weight->Indicate->The output of the previous neuron in the number of layers,/->Is the learning rate.
Further, S4: selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by using the corresponding health state estimation model, wherein the method comprises the following steps:
according to the charge-discharge multiplying power and the charge-discharge depth in the actual charge-discharge process of the lithium battery, extracting health factors according to the step S2;
selecting a corresponding health estimation model from the mapping model library, and estimating the health state of the lithium battery by using the corresponding health estimation model to obtain a health state estimation value of the lithium battery;
wherein, the state of health estimate of lithium cell is:
；
in the formula ,represents an estimated state of health value of the lithium battery, +.>An estimated value representing the capacity of the lithium battery; />Representing the calibration capacity of the lithium battery;
wherein, the estimated value of the lithium battery capacity is:
；
in the formula ,Estimated value representing lithium battery capacity, +.>Express multiplying power->Representing the time constant +.>Representing the time offset, +.>Representing a constant.
Based on the same inventive concept, a multi-working condition SOH estimation system based on local phase charging data comprises: an acquisition unit, an extraction acquisition unit, a construction unit and an estimation unit,
the acquisition unit is used for carrying out a cyclic charge-discharge experiment on the lithium battery under different working conditions to acquire a battery aging cyclic database under different working conditions;
the extraction and acquisition unit is used for extracting health factors based on the battery aging cycle database and acquiring a mapping parameter library of the health factors and the health state of the lithium battery;
the construction unit is used for establishing a health state estimation model of the lithium battery based on the neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions;
the estimation unit is used for selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model.
Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the multi-working condition SOH estimation method based on the local phase charging data when executing the computer program.
Based on the same inventive concept, the embodiment of the invention further provides a computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and the computer executable instructions realize the multi-working condition SOH estimation method based on the local phase charging data when being executed.
The invention has the technical effects and advantages that: according to the invention, the state of health estimation model is trained for estimating and predicting the SOH of the lithium battery, and the high-precision estimation of the state of health of the battery under a plurality of working conditions can be realized by means of the charging data in the constant current stage and the high SOC stage; the method is low in calculation complexity and high in anti-fluctuation capability, and the battery SOH high-precision estimation under various working conditions can be completed by means of the multi-working-condition model mapping library.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a multi-condition SOH estimation method based on local phase charging data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a fitted capacity calibration loop curve of an interpolation function in an embodiment of the present invention;
FIG. 3 is an extraction diagram of a health factor of a capacity increment curve in a constant current stage in an embodiment of the invention;
FIG. 4 is a schematic diagram of a least squares fit high SOC stage current curve in an embodiment of the present invention;
FIG. 5 is a schematic error diagram of a least squares fit high SOC stage current curve in an embodiment of the present invention;
FIG. 6 is a schematic diagram of training set estimation result errors of a health state estimation model according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the error of the estimation result of the test set of the health status estimation model according to the embodiment of the present invention;
FIG. 8 is a schematic diagram of the error of the estimation result of the verification set of the health state estimation model according to the embodiment of the present invention;
fig. 9 is a schematic structural diagram of a multi-working-condition SOH estimation system based on local phase charging data according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of current ripple at a high SOC stage in an embodiment of the present invention;
fig. 11 is an estimation result error diagram of a health state estimation model under the condition of high SOC stage current fluctuation in the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the defects in the prior art, the invention discloses a multi-working condition SOH estimation method based on local stage charging data, which comprises the following steps as shown in fig. 1:
Step S1: under different working conditions, carrying out a cyclic charge-discharge experiment on the lithium battery to obtain a battery aging cyclic database under different working conditions; the method specifically comprises the following steps:
the requirements of the environmental temperature fluctuation amplitude and the acquisition accuracy of voltage, temperature, current and capacity in the experiment are implemented according to national standard GB/T36276. Placing the lithium battery in an incubator, and performing a charging experiment on the lithium battery at a preset charging multiplying power (such as 1/3 multiplying power) at a fixed temperature (such as 25 ℃), wherein the lithium battery is charged by adopting a constant current and constant voltage charging method, so that the current of the lithium battery reaches a preset cut-off current; after the lithium battery is kept stand for a first preset time (30 minutes), a discharge experiment of a constant-current discharge method is adopted for the lithium battery at a preset discharge multiplying power (such as 1/3 multiplying power), so that the voltage of the lithium battery reaches a preset cut-off voltage; the preset charging multiplying power is consistent with the preset discharging multiplying power, and different working conditions represent different charging and discharging depths and different charging and discharging multiplying powers.
S102: and a constant-current and constant-voltage charging experiment is adopted for the lithium battery according to the required charging multiplying power, namely the lithium battery is subjected to the constant-current charging experiment until the voltage reaches a preset cut-off voltage, and then subjected to the constant-voltage charging experiment until the current reaches the preset cut-off current.
S103: and carrying out constant-current discharge experiments on the lithium battery according to the required discharge multiplying power, so that the discharge depth of the lithium battery reaches the required discharge depth.
S104: repeating the steps S102-S103 until the battery capacity of the lithium battery is reduced to a preset capacity, and executing the step S101 once and performing primary battery capacity calibration when the first preset times are repeated.
S105: carrying out data preprocessing of filling missing values and correcting error values on experimental results of the cyclic charge-discharge experiment; according to the experimental result of the processed cyclic charge-discharge experiment, a battery aging cyclic database of corresponding working conditions (namely corresponding charge-discharge multiplying power and corresponding charge-discharge depth) at a fixed temperature is established;
the experimental results of the cyclic charge-discharge experiment comprise: information such as current, voltage, temperature, etc.; the missing value is the position where the current value or voltage value or temperature value or capacity value at a certain moment is missing; the error value is the position of the current value or voltage value or temperature value or capacity value at a certain moment compared with the average value of the front and back data by more than a preset number (such as 50 percent); the data preprocessing comprises the following steps: spline interpolation function, average value function, correct and smooth missing value and error value.
S2: extracting health factors based on the battery aging cycle database, and acquiring a mapping parameter library of the health factors and the health state of the lithium battery; the method specifically comprises the following steps:
s201, fitting battery capacity calibration cycles under each working condition based on the battery aging cycle database, obtaining accurate capacity of each cycle, and fitting capacity increment curves or current curves of each stage; the method comprises the following steps:
selecting data of the same working condition (same charge-discharge multiplying power and same charge-discharge depth) from the battery aging cycle database, and extracting charge-discharge capacity in each cycle by utilizing an ampere-hour integration methodCharge capacity at constant current stageCharge capacity during constant voltage phase>The method comprises the steps of carrying out a first treatment on the surface of the The calculation formula for each capacity is as follows:
，
，
；
wherein ,indicating the moment when CCCV working condition is charged to charge cut-off current, < + >>Indicating the moment when CCCV working condition is charged to the charge cut-off voltage, < + >>Indicate->Current value at time, ">Representing the load current sampling time interval.
Selecting capacity calibration circulation under the same working condition from the battery aging circulation database to obtain capacity calibration circulation capacityThe method comprises the steps of carrying out a first treatment on the surface of the As shown in FIG. 2, the cyclic capacity is calibrated for said capacity>Adopting cubic spline interpolation to obtain standard capacity of all cycles under a specified working condition >。
Wherein, the standard capacity of all the cycles under the specified working conditionThe method comprises the following steps:
；
in the formula ,standard capacity representing all cycles under specified conditions, +.>Indicate->Third degree polynomial of each interval, +.>Representing the number of capacity calibration cycles; />Represents the total number of capacity calibration cycles, +.>The cycle number of the capacity calibration cycle in the whole charge-discharge cycle is represented.
The standard capacityThe boundary conditions in the calculation are as follows:
；
in the formula ,representing capacity calibration cycle capacity, +.>Polynomial values representing the number of corresponding capacity calibration cycles,/->Represents the number of cycles of the capacity calibration cycle in the whole charge-discharge cycle,/->Indicates the number of capacity calibration cycles, +.>Indicating the total number of capacity calibration cycles.
Processing charging data of a complete constant current stage and a partial constant current stage in the battery aging cycle database by using a Gauss filter function, and constructing a corresponding capacity increment curve; and selecting the peak value of the corresponding capacity increment curve, and taking the peak value position and the peak value width as health factors.
The health factor is represented by the following formula:
；
in the formula ,indicating battery capacity,/->Represents lithium battery voltage in a single cycle, +.>Representing a voltage sequence of the lithium battery in a single cycle; / >Indicate->Peak value,/->Representing the number of peaks; />Representing natural constant->Indicate->Peak position->Representing peak position; />Indicate->The peak width.
The complete constant current stage represents a charge-discharge cycle process of the lithium battery with a first preset charge-discharge depth (for example, 100% charge-discharge depth), the partial constant current stage represents a charge-discharge cycle process of the lithium battery with a second preset charge-discharge depth (for example, 60% charge-discharge depth), and the first preset charge-discharge depth is larger than the second preset charge-discharge depth.
Defining the peak concept as: if there is a value greater than 0So that all->Are all provided withIt can be referred to as a local extreme point of the curve.
Taking the maximum extreme point of the capacity increment curve as the reference, and defining the minimum peak value distance as follows:
；
in the formula ,the minimum peak distance of the capacity increment curve is represented, and DOD represents the depth of discharge.
According to different working conditions, the minimum peak distance is defined to be different, taking LIB#0611 75 cycle under 2C100DOD working conditions as an example, the identification result is shown in FIG. 3, and the identification parameters are as follows: peak value:peak position: />Peak width: />。
And fitting a current curve of a high SOC stage by using a Sigmoid function and a least square method based on constant voltage stage data in the battery aging cycle database. The high SOC stage represents a charge-discharge cycle process in which the lithium battery is at a third predetermined charge-discharge depth, and the third predetermined charge-discharge depth is not less than 20% of the charge-discharge depth.
Before fitting the current curve of the high SOC stage, carrying out normalization processing on the data of the high SOC stage, wherein the normalization processing comprises the following steps: when the duration value isIn case of fluctuation greater than the second predetermined time, the end time of the high SOC phase is +.>Is standard; the normalized high SOC stage interval is:
；
in the formula ,representing a normalized high SOC phase interval, +.>Indicates the end time of the high SOC phase, +.>Representing a second predetermined time; or alternatively, the first and second heat exchangers may be,
when the duration value isGreater than a second predetermined time and a duration value +.>In case of fluctuation of less than or equal to the second predetermined time, the high SOC stage end time +.>As the starting point position of the high SOC stage in each cycle under each working condition, normalizing the original point position to obtain a normalized high SOC stage section as follows:
；
in the formula ,indicates the start time of the high SOC phase current, +.>Indicating the duration of the high SOC phase.
The current curve for the high SOC stage is represented by:
；
in the formula ,current curve representing high SOC phase, +.>Express multiplying power->Representing a constant->Representing the time constant +.>Representing the time offset, +.>Representing natural constants.
The specific steps of fitting the current curve of the high SOC stage include:
finding the corresponding parameters to minimize the sum of squares:
；
in the formula ,representing the minimized sum of squares,/->Representing a current sequence in a high SOC phase; />A determined value, referring to the end of the current time; />Representing the residual, defined as: />，Indicate->Current value at time, ">Representing +.>Current value at time.
When (when)When the minimum value is taken, the gradient is zero, and four total values need to be identified, so that 4 gradient equations can be obtained:
；
in the formula ,four parameters representing the need to identify the fit, +.>Representing residual error,/->Represents the current sequence in the high SOC phase, +.>Representing the number of parameters>Indicate->And parameters.
Whereas in a nonlinear system, the partial derivativeAt the same time as the argument time->Parameter->Therefore, the gradient equations are generally not closed-solved, and then are solved by adopting an initial value iteration method, specifically:
；
in the formula ,represents the number of iterations, +.>Representing an offset vector +.>Parameter values representing the next iteration, +.>Representing the corresponding parameter variation; the first order taylor expansion is then employed with respect thereto to linearize the model:
；
in the formula ,current curve obtained by representing least square fitting parameters, jacobian matrix +.>Is a function of a constant, an argument and a parameter, thus +. >Not a fixed value, for the linearization model:
；
the residual expression can be rewritten as:
，
；
in the formula ,indicating the current variation at the corresponding moment +.>Indicate->The value of the current at the moment in time,representing least squares fit ∈>Current curve obtained by parameters +.>Representing residual error,/->Represents the current sequence in the high SOC phase, +.>Representing the number of parameters>A determined value, referring to the end of the current time;current curve representing the least squares fitting parameters,/->Representing the corresponding parameter variation; />Representing the number of parameters, wherein: />。
Bringing the above expression into a gradient equation can result in:
；
the above can be simplified into a plurality of linear equations, which can be rewritten as follows using a matrix representation:
；
in the formula ,representing an offset vector +.>Represents the current sequence in the high SOC phase, +.>Representing the number of parameters>Representing the current variation, jacobian matrix +.>Is a constant.
Taking 1667 th cycle under the working condition of 2C20% DOD charge-discharge cycle in this embodiment as an example, the identification result is:，/>，/>，/>. The identification result is shown in fig. 4, and the identification error is shown in fig. 5.
S202, repeatedly executing the step S201 to obtain standard capacity of all cycles under each working condition And carrying out parameter identification on the capacity increment curve or the current curve of each stage to obtain a health factor parameter set under each working condition, namely a mapping parameter library of the health factors and the health states of the lithium battery.
S3: establishing a health state estimation model of the lithium battery based on a neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions; the method specifically comprises the following steps:
s301: the topology structure of constructing an input layer, an hidden layer and an output layer as a neural network is defined as follows:
。
s302: learning an initial estimation model of the lithium battery health state by using a neural network, and adjusting the weight of each node according to the error; comprising the following steps:
in the learning process of the initial estimation model, the node weight is adjusted by correcting the node weight of the neural network to minimize and output the overall error (namely, the square sum of the error of each data of each node); and acquiring the correction quantity of each node weight through gradient descent.
Wherein, in the learning process of the initial estimation model, the output nodeIs>The error of the data points is expressed as:
；
in the formula ,representing the output node +. >Is>Error of data points, ++>Representing the output node +.>Is>A data point target value, wherein the target value is the state of health of the lithium battery in the mapping model library, and the state of health of the battery is the current capacity of the battery/the maximum capacity of the battery; />And representing the predicted value of the initial estimation model on the state of health of the lithium battery.
The node weight minimization output overall error by correcting the neural network is:
；
in the formula ,indicating global error,/->Representing the output node +.>Is>Errors in the data points;
the correction amount of each node weight is:
；
in the formula ,indicate->The (th) of the number of layers>Correction of the weight of individual nodes, +.>Indicate->The (th) of the number of layers>Weights of individual nodes, weight->Indicate->The output of the previous neuron in the number of layers,/->Is learning rate, the setting of learning rate needs to ensure that the weight can be converged rapidly without oscillation, and the learning rate is selected>。
S303: training the initial estimation model, and selecting the initial estimation model with the minimum error value in the second preset number of iterations as a final estimation model, namely a lithium battery health state estimation model under the corresponding working condition;
taking lithium battery #0422 as an example under the working conditions of 0.3C charge-discharge rate and 20% discharge depth, as shown in fig. 6 and 7, the training set trains root mean square error rmse= 0.8645, as shown in fig. 8, and the verification set root mean square error rmse= 1.0664.
S304: repeating the steps S301-S303 to obtain the health state estimation models under different working conditions; and constructing a mapping model library of the health factors and the health states of the lithium battery according to the health state estimation models and the mapping parameter library under different working conditions.
S4: selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model; the method specifically comprises the following steps:
according to the charge-discharge multiplying power and the charge-discharge depth in the actual charge-discharge process of the lithium battery, extracting health factors according to the step S2; and selecting a corresponding health estimation model from the mapping model library, and estimating the health state of the lithium battery by using the corresponding health estimation model to obtain a health state estimation value of the lithium battery.
Wherein, the state of health estimate of lithium cell is:
；
in the formula ,represents an estimated state of health value of the lithium battery, +.>An estimated value representing the capacity of the lithium battery; />Representing the calibration capacity of the lithium battery;
wherein, the estimated value of the lithium battery capacity is:
；
in the formula ,estimated value representing lithium battery capacity, +. >Express multiplying power->Representing the time constant +.>Representing the time offset, +.>Representing a constant.
According to the steps, the corresponding health state estimation model is constructed based on the mapping model library data under different working conditions, so that the real-time battery health state is obtained, and compared with the preset life cut-off point, whether the battery health state under the condition that only partial stage charging data under multiple working conditions meets the safety standard can be effectively judged.
Based on the same inventive concept, the present application also provides a multi-working condition SOH estimation system based on local phase charging data, as shown in fig. 9, including: an acquisition unit, an extraction acquisition unit, a construction unit and an estimation unit,
the acquisition unit is used for carrying out a cyclic charge-discharge experiment on the lithium battery under different working conditions to acquire a battery aging cyclic database under different working conditions;
the extraction and acquisition unit is used for extracting health factors based on the battery aging cycle database and acquiring a mapping parameter library of the health factors and the health state of the lithium battery;
the construction unit is used for establishing a health state estimation model of the lithium battery based on the neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions;
And the estimation unit is used for selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model.
The specific manner in which the respective unit modules perform the operations in the above-described embodiments has been described in detail in relation to the embodiments of the method, and will not be described in detail herein.
Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the multi-working condition SOH estimation method based on the local phase charging data when executing the computer program.
Based on the same inventive concept, the embodiment of the invention further provides a computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and the computer executable instructions realize the multi-working condition SOH estimation method based on the local phase charging data when being executed.
Examples: taking a lithium iron phosphate battery (LiFePO 4, LFP) with the nominal capacity of 40Ah as an experimental object, carrying out the method step S1 under the condition of fixed temperature, and establishing a battery aging cycle database under different charge and discharge multiplying power and discharge depth.
And then, fitting a capacity calibration circulation curve by adopting a spline interpolation function to obtain standard capacities under different charge and discharge depths, and then fitting a high SOC stage current curve and each constant current stage IC curve by adopting a nonlinear least square method by adopting a Sigmoid function to obtain health factors of the battery under corresponding working conditions, thereby forming a mapping parameter library of the health factors and the health state of the lithium battery.
Then, according to the mapping parameter library, establishing a neural network-based health state estimation model corresponding to the working condition; and estimating the health state of the appointed battery to be detected by using a health state estimation model, wherein the charge upper limit cutoff voltage of the constant-current stage of the battery to be detected is 3.65V, the charge lower limit cutoff current of the constant-voltage stage is 2.0A, the discharge current of the constant-current stage of the discharge test is the appointed discharge multiplying power, and the discharge stage cutoff voltage is 2.0V. If the constant current phase data is normal, the method can be used for analyzing the CC phase and estimating the SOH of the battery. If the data in the constant current stage is missing, and when the high SOC stage of the battery to be tested has large fluctuation, for example, when the constant current in the high SOC stage is too long, the health factor can be obtained according to the high SOC current change of the last stage, and then the health state estimation is carried out.
In the embodiment, the abnormal change of the charging current in the high SOC stage is shown in fig. 10, and the estimation accuracy of the health state in this case is shown in fig. 11, and the rms error rmse= 0.8513 of the health state under the corresponding working condition estimated according to the health state estimation model, that is, the rms error of the estimated value and the true value is within 1%. Therefore, the invention can estimate the monitoring state of the lithium battery based on the high SOC charging data under multiple working conditions, can better cope with special conditions, and has low calculation complexity, less data requirement, wide application range and strong anti-fluctuation capability under the condition of meeting the precision requirement.
According to the invention, by establishing charge and discharge test databases under different multiplying powers and different discharge depth working conditions, a constant current stage and a data model of a high SOC working condition under different working conditions are established, capacity increment analysis is carried out on CC stage data by Gauss filtering under the conditions of complete charge stage, partial constant current stage and data under the high SOC working condition only, current change of the high SOC working condition is analyzed by adopting a Sigmoid filtering function, corresponding health factors are extracted, and then direct mapping relation between the health factors and the SOH is established by a neural network algorithm to estimate the SOH; the method provided by the invention is easy to realize, solves the limitation that the prior method needs complete charging data, can realize accurate estimation of SOH under multiple working conditions for complex conditions of multiple working conditions, and has stronger anti-fluctuation capability.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (13)
1. The multi-working condition SOH estimation method based on the local stage charging data is characterized by comprising the following steps of:
s1: under different working conditions, carrying out a cyclic charge-discharge experiment on the lithium battery to obtain a battery aging cyclic database under different working conditions;
s2: extracting health factors based on the battery aging cycle database, and acquiring a mapping parameter library of the health factors and the health state of the lithium battery;
s3: establishing a health state estimation model of the lithium battery based on a neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions;
S4: and selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model.
2. The multi-condition SOH estimation method based on local phase charging data according to claim 1, wherein S1 is that under different conditions, a lithium battery is subjected to a cyclic charge-discharge experiment to obtain a battery aging cyclic database under different conditions, and the method comprises the following steps:
s101: at a fixed temperature, carrying out a charging experiment on the lithium battery by adopting a constant-current and constant-voltage charging method at a preset charging multiplying power, so that the current of the lithium battery reaches a preset cut-off current; after the lithium battery is kept stand for a first preset time, a discharge experiment of a constant-current discharge method is adopted for the lithium battery at a preset discharge rate, so that the voltage of the lithium battery reaches a preset cut-off voltage; wherein the predetermined charge rate and the predetermined discharge rate are identical;
s102: firstly, performing constant-current and then constant-voltage charging experiments on the lithium battery according to the required charging multiplying power, namely, performing constant-current charging experiments on the lithium battery until the voltage reaches a preset cut-off voltage, and performing constant-voltage charging experiments until the current reaches a preset cut-off current;
S103: performing a constant-current discharge experiment on the lithium battery according to the required discharge multiplying power to ensure that the discharge depth of the lithium battery reaches the required discharge depth;
s104: repeating the steps S102-S103 until the battery capacity of the lithium battery is reduced to a preset capacity, and executing the step S101 once and performing primary battery capacity calibration when the first preset times are repeated;
s105: according to the experimental result of the cyclic charge-discharge experiment, a battery aging cyclic database corresponding to the working condition at a fixed temperature is established;
wherein, different working conditions represent different charge-discharge depths and different charge-discharge multiplying powers.
3. The method of claim 2, wherein,
in the steps S102 and S103, the multiplying power of the lithium battery in the constant-current charging or constant-current discharging stage under the same working condition is consistent, and the preset cut-off current of the lithium battery in the constant-voltage charging stage and the preset cut-off voltage of the constant-current charging stage in different cycles are respectively kept consistent;
in step S105, after performing data preprocessing of filling the missing value and correcting the error value on the experimental result of the cyclic charge-discharge experiment, the battery aging cyclic database is established;
The experimental result is current information, voltage information and temperature information of the lithium battery in the cyclic charge and discharge experimental process;
the missing value is a position where a current value or a voltage value or a temperature value or a capacity value at a certain moment is in a vacancy, and the error value is a position where the current value or the voltage value or the temperature value or the capacity value at a certain moment is compared with the average value of the front data and the back data to exceed the preset number;
the data preprocessing comprises the following steps: spline interpolation function, average value function, correct and smooth missing value and error value.
4. The multi-working SOH estimation method based on local phase charging data according to claim 1 or 2, wherein S2: extracting health factors based on the battery aging cycle database, and obtaining a mapping parameter library of the health factors and the health state of the lithium battery, wherein the method comprises the following steps:
s201, fitting battery capacity calibration cycles under each working condition based on the battery aging cycle database, obtaining accurate capacity of each cycle, and fitting capacity increment curves or current curves of each stage;
s202, repeatedly executing the step S201 to obtain standard capacity of all cycles under each working conditionAnd carrying out parameter identification on the capacity increment curve or the current curve of each stage to obtain a health factor parameter set under each working condition, namely a mapping parameter library of the health factors and the health states of the lithium battery.
5. The method for estimating SOH under multiple conditions based on local stage charge data according to claim 4, wherein S201, based on said battery aging cycle database, fitting a capacity calibration cycle under each condition to obtain accurate capacities of each cycle, and fitting a capacity increment curve or a current curve of each stage comprises:
selecting data with the same working condition from the battery aging cycle database, and extracting the charge and discharge capacity in each cycle by using an ampere-hour integration methodCharging capacity at constant current stage->Charge capacity during constant voltage phase>；
Selecting capacity calibration circulation under the same working condition from the battery aging circulation database to obtain capacity calibration circulation capacity；
Calibrating the capacity to the circulating capacityAdopting cubic spline interpolation to obtain standard capacity of all cycles under a specified working condition>；
Processing charging data of a complete constant current stage and a partial constant current stage in the battery aging cycle database by using a Gauss filter function, and constructing a corresponding capacity increment curve;
selecting a peak value of a corresponding capacity increment curve, and taking the peak value position and the peak value width as health factors;
fitting a current curve of a high SOC stage by using a Sigmoid function and a least square method based on constant voltage stage data in the battery aging cycle database;
The complete constant current stage represents a charge-discharge cycle process of a lithium battery with a first preset charge-discharge depth, the partial constant current stage represents a charge-discharge cycle process of the lithium battery with a second preset charge-discharge depth, and the first preset charge-discharge depth is larger than the second preset charge-discharge depth;
the high SOC stage represents a charge-discharge cycle process in which the lithium battery is at a third predetermined charge-discharge depth, the third predetermined charge-discharge depth being not less than 20% of the charge-discharge depth.
6. The method of claim 5, wherein,
the health factor is represented by the formula:
；
wherein ,indicating battery capacity,/->Represents lithium battery voltage in a single cycle, +.>Representing a voltage sequence of the lithium battery in a single cycle; />Indicate->Peak value,/->Representing the number of peaks; />Representing natural constant->Indicate->Peak positions;indicate->The peak width.
7. The method for estimating SOH of multiple operating conditions based on local phase charge data according to claim 5, wherein the normalizing the high SOC phase data before fitting the current curve of the high SOC phase comprises:
When the duration value isIn case of fluctuation greater than the second predetermined time, the end time of the high SOC phase is +.>Is standard; the normalized high SOC stage interval is:
；
wherein ,representing a normalized high SOC phase interval, +.>Indicates the end time of the high SOC phase, +.>Representing a second predetermined time; or alternatively, the first and second heat exchangers may be,
when the duration value isGreater than a second predetermined time and a duration value +.>In case of fluctuation of less than or equal to the second predetermined time, the high SOC stage end time +.>As the starting point position of the high SOC stage in each cycle under each working condition, normalizing the original point position to obtain a normalized high SOC stage section as follows:
；
wherein ,indicates the start time of the high SOC phase current, +.>Indicating the duration of the high SOC phase.
8. The multi-condition SOH estimation method based on local phase charging data according to claim 1, wherein S3: establishing a health state estimation model of the lithium battery based on a neural network, and establishing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions, wherein the method comprises the following steps:
s301: constructing an input layer, an hidden layer and an output layer as a topological structure of the neural network;
S302: learning an initial estimation model of the lithium battery health state by using a neural network, and adjusting the weight of each node according to the error;
s303: training the initial estimation model, and selecting the initial estimation model with the minimum error value in the second preset number of iterations as a final estimation model, namely a lithium battery health state estimation model under the corresponding working condition;
s304: repeating the steps S301-S303 to obtain the health state estimation models under different working conditions; and constructing a mapping model library of the health factors and the health states of the lithium battery according to the health state estimation models and the mapping parameter library under different working conditions.
9. The multi-operating SOH estimation method based on local phase charging data according to claim 8, wherein S302: learning an initial estimation model of the lithium battery health state by using a neural network, and adjusting the weight of each node according to the error, wherein the learning comprises the following steps:
in the learning process of the initial estimation model, the node weight of the neural network is corrected to minimize and output the overall error, so that the adjustment of the node weight is realized;
acquiring the correction quantity of each node weight through gradient descent;
In the learning process of the initial estimation model, a node is outputIs>The error of the data points is expressed as:
；
in the formula ,representing the output node +.>Is>Error of data points, ++>Representing the output node +.>Is>Data point target value, target value is the lithium battery health state in the mapping model library, +.>Representing a predicted value of the initial estimation model on the state of health of the lithium battery;
the node weight minimization output overall error by correcting the neural network is:
；
in the formula ,representing the overall error;
the correction amount of each node weight is as follows:
；
in the formula ,indicate->The (th) of the number of layers>Correction of the weight of individual nodes, +.>Indicate->The (th) of the number of layers>Weights of individual nodes, weight->Indicate->The output of the previous neuron in the number of layers,/->Is the learning rate.
10. The multi-working SOH estimation method based on local phase charging data according to claim 1 or 9, wherein S4: selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by using the corresponding health state estimation model, wherein the method comprises the following steps:
according to the charge-discharge multiplying power and the charge-discharge depth in the actual charge-discharge process of the lithium battery, extracting health factors according to the step S2;
Selecting a corresponding health estimation model from the mapping model library, and estimating the health state of the lithium battery by using the corresponding health estimation model to obtain a health state estimation value of the lithium battery;
wherein, the state of health estimate of lithium cell is:
；
in the formula ,represents an estimated state of health value of the lithium battery, +.>An estimated value representing the capacity of the lithium battery; />Representing the calibration capacity of the lithium battery;
wherein, the estimated value of the lithium battery capacity is:
；
in the formula ,estimated value representing lithium battery capacity, +.>Express multiplying power->Representing the time constant +.>Representing the time offset, +.>Representing a constant.
11. A multi-condition SOH estimation system based on local phase charging data, comprising: an acquisition unit, an extraction acquisition unit, a construction unit and an estimation unit,
the acquisition unit is used for carrying out a cyclic charge-discharge experiment on the lithium battery under different working conditions to acquire a battery aging cyclic database under different working conditions;
the extraction and acquisition unit is used for extracting health factors based on the battery aging cycle database and acquiring a mapping parameter library of the health factors and the health state of the lithium battery;
the construction unit is used for establishing a health state estimation model of the lithium battery based on the neural network, and constructing a mapping model library of health factors and the health state of the lithium battery according to the health state estimation model and the mapping parameter library under different working conditions;
The estimation unit is used for selecting a corresponding health state estimation model from the mapping model library according to the actual working condition of the lithium battery, and estimating the health state of the lithium battery by utilizing the corresponding health state estimation model.
12. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing a multi-condition SOH estimation method based on local phase charging data according to any one of claims 1-10 when executing the computer program.
13. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and when executed, implement a multi-working condition SOH estimation method based on local phase charging data according to any one of claims 1-10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572243A (en) * | 2023-11-08 | 2024-02-20 | 北京天易数聚科技有限公司 | Method and system for predicting cycle life of energy storage battery |
| CN117572243B (en) * | 2023-11-08 | 2024-06-04 | 北京天易数聚科技有限公司 | Method and system for predicting cycle life of energy storage battery |
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