CN114924191A - Method and device for estimating battery capacity on line - Google Patents
Method and device for estimating battery capacity on line Download PDFInfo
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- CN114924191A CN114924191A CN202210523800.6A CN202210523800A CN114924191A CN 114924191 A CN114924191 A CN 114924191A CN 202210523800 A CN202210523800 A CN 202210523800A CN 114924191 A CN114924191 A CN 114924191A
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- 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 provides a method and a device for estimating battery capacity on line, comprising the following steps: constructing an RC equivalent circuit model of the battery based on the obtained online terminal voltage and current of the battery, and processing the RC equivalent circuit model to obtain parameters to be estimated; setting a first set threshold, and processing the open-circuit voltage based on the first set threshold to obtain a first estimated value sequence of the open-circuit voltage; setting a second set threshold, and processing the open-circuit voltage based on the second set threshold to obtain a second estimated value sequence of the open-circuit voltage; determining a first SOC estimation value sequence corresponding to the first estimation value sequence of the open-circuit voltage and a second SOC estimation value sequence corresponding to the second estimation value sequence of the open-circuit voltage based on a pre-calibrated SOC-OCV relation; and processing the first SOC estimation value sequence and the second SOC estimation value sequence according to the linear relation between the battery electric quantity change and the SOC change to obtain the battery capacity. The invention can estimate the battery capacity more accurately.
Description
Technical Field
The present invention relates to battery technologies, and in particular, to a method and an apparatus for online estimation of battery capacity.
Background
Because energy shortage and environmental pollution become the focus of attention of all countries in the world, the lithium battery becomes the first choice of new energy automobiles, mobile phone communication and smart power grids by virtue of various advantages such as high energy density, long cycle service life and the like, but with frequent use of the battery, the battery can have the phenomena of capacity attenuation and internal resistance increase, so that a series of safety problems are caused, and therefore, the accurate estimation of the capacity of the battery has important significance for improving the use safety of the battery.
The current battery capacity estimation method is mainly divided into three categories: direct detection methods, model identification methods, and data-driven methods.
The direct detection method mainly carries out offline calibration of the capacity, and consumes time and labor;
the model identification method mainly carries out capacity estimation by identifying parameters of a model, and the problem of parameter mismatch can occur in the estimation process;
the data driving method generally adopts the voltage, current, temperature and the like in the charging process as the input of a neural network so as to estimate the capacity of the battery, but the method needs a large amount of data, has serious time sequence dependence and is difficult to model.
Therefore, it is necessary to solve the problem of inaccurate battery capacity estimation in the offline state in the prior art.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method and an apparatus for online estimating battery capacity, which are used to solve the problem of inaccurate estimation of battery capacity in offline state in the prior art.
To achieve the above and other related objects, the present invention provides an online estimation method for battery capacity, comprising the steps of:
constructing an RC equivalent circuit model of the battery based on the obtained online terminal voltage and current of the battery, and processing the RC equivalent circuit model to obtain a parameter to be estimated; the parameter to be estimated comprises an open circuit voltage;
setting a first set threshold, and processing the open-circuit voltage based on the first set threshold to obtain a first estimated value sequence of the open-circuit voltage;
setting a second set threshold, and processing the open-circuit voltage based on the second set threshold to obtain a second estimated value sequence of the open-circuit voltage;
determining a first SOC estimated value sequence corresponding to the first open-circuit voltage estimated value sequence and a second SOC estimated value sequence corresponding to the second open-circuit voltage estimated value sequence based on a pre-calibrated SOC-OCV relationship;
and processing the first SOC estimation value sequence and the second SOC estimation value sequence according to the linear relation between the battery electric quantity change and the SOC change to obtain the battery capacity.
Preferably, the process of processing the RC equivalent circuit model to obtain the parameter to be estimated includes:
processing the RC equivalent circuit model to obtain a transfer function;
and performing online identification on the transfer function by adopting a least square method with a forgetting factor to estimate the parameters to be estimated of the battery.
Preferably, the RC equivalent circuit model is:
wherein, U 1 To polarize the voltage, U t Terminal voltage, I current, OCVOpen circuit voltage, R 1 For polarizing internal resistance, R 0 τ is the time constant for ohmic internal resistance.
Preferably, if a first set condition is satisfied within a set sampling length, a sequence of first estimated values of the open-circuit voltage satisfying the first set condition is solved in the open-circuit voltage,
the first setting condition is:
where Δ T is the sampling time interval, R 1 For polarizing internal resistance, C 1 N is a first set threshold value for the polarization capacitance.
Preferably, the processing the open-circuit voltage based on the second set threshold to obtain the sequence of second estimated open-circuit voltages includes:
determining a second setting condition based on a second setting threshold; determining at least two groups of data sections meeting a second set condition in the open-circuit voltage; the second setting condition is as follows:
wherein m is a second set threshold;
processing each group of data segments to obtain a second estimation value group of the open-circuit voltage corresponding to each group of data segments;
and obtaining a second estimated value sequence of the open-circuit voltage according to a plurality of second estimated value groups of the open-circuit voltage.
Preferably, the step of processing each group of data segments to obtain the second estimated value set of open-circuit voltages corresponding to each group of data segments includes:
taking the two data points of each group as a second initial estimation value of the open-circuit voltage corresponding to the initial point of each group and a second end estimation value of the open-circuit voltage corresponding to the end point of each group respectively;
and performing linear interpolation according to the second initial estimation value of the open-circuit voltage and the second ending estimation value of the open-circuit voltage of each group of data segments to obtain a second estimation value group of the open-circuit voltage corresponding to each group of data segments.
Preferably, the step of processing the first SOC estimation value sequence and the second SOC estimation value sequence to obtain the battery capacity according to the linear relationship between the battery power change and the SOC change includes:
processing the first SOC estimation value sequence to obtain a first capacity value and processing the second SOC estimation value sequence to obtain a second capacity value according to the linear relation between the battery electric quantity change and the SOC change;
and weighting the first capacity value and the second capacity value to obtain the battery capacity.
Preferably, the linear relation between the battery capacity change and the SOC change is an SOC-capacity gain method; the SOC-electric-quantity gain method calculates the battery capacity by the ratio of the charge/discharge electric-quantity variation to the corresponding SOC variation.
In order to achieve the above objects and other related objects, the present invention further provides an online estimation apparatus for battery capacity, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the steps of the online estimation method for battery capacity when executing the program.
As described above, the method and apparatus for estimating battery capacity online according to the present invention have the following advantages:
according to the online estimation method and device for the battery capacity, disclosed by the invention, the online estimation of the battery capacity is realized by the RC equivalent circuit model constructed by the acquired online electrical parameters of the battery and the weighting processing of the processing result of the parameters to be estimated under different conditions, the online estimation precision of the battery capacity can be obviously improved, and the online estimation method and device are more suitable for the actual working condition of the battery.
Drawings
FIG. 1 is a flow chart of the method for estimating battery capacity online according to the present invention.
FIG. 2 is a schematic diagram of an online estimation apparatus for battery capacity according to the present invention.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
Please refer to fig. 1-2. It should be noted that the drawings provided in this embodiment are only for schematically illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings and not drawn according to the number, shape and size of the components in actual implementation, and the form, quantity and proportion of each component in actual implementation may be arbitrarily changed, and the component layout may be more complicated.
The online estimation method for the battery capacity provided by the invention can be used for carrying out online estimation on the battery capacity by combining the model identification and the data driving method, can be used for obviously improving the online estimation precision of the capacity, can be obviously suitable for the actual working condition, and has certain practicability.
Based on the technical concept, the invention provides a method and a device for estimating the capacity of a battery on line.
The method comprises the following steps:
fig. 1 is a schematic flow chart of the method for estimating battery capacity online according to the present invention, and the method for estimating battery capacity online according to the present invention is described in detail with reference to fig. 1:
s1, constructing an RC equivalent circuit model of the battery based on the obtained on-line terminal voltage and current of the battery, and processing the RC equivalent circuit model to obtain parameters to be estimated;
in the embodiment of the invention, the constructed RC equivalent circuit model is a first-order RC model.
Wherein, U 1 To polarize the voltage, U t Terminal voltage, I current, OCV open circuit voltage, R 1 For polarizing internal resistance, R 0 τ is the time constant for ohmic internal resistance.
The process of processing the RC equivalent circuit model to obtain the parameters to be estimated comprises the following steps:
s11, processing the RC equivalent circuit model to obtain a transfer function;
processing the RC equivalent circuit model based on bilinear transformation in the frequency domain analysis to obtain a transfer function;
firstly, carrying out bilinear transformation on a transfer function of an RC series circuit during frequency domain analysis to obtain a transfer function model;
where Δ T is the sampling time interval, C 1 Is a polarization capacitance.
Then, combining a first-order RC equivalent circuit model, converting the transfer function model to obtain a transfer function as follows:
order:
the simplified transfer function is:
and S12, performing online identification on the transfer function by adopting a least square method with a forgetting factor, and estimating the parameters to be estimated of the battery.
The parameters to be estimated comprise open-circuit voltage, ohmic internal resistance, polarization internal resistance and time constant;
discretizing the transfer function to obtain:
U t,k ＝bU t,k-1 +(1-b)OCV+cI k +dI k-1
wherein, U t,k For terminal voltage at time k in the sampling time t, U t,k-1 For terminal voltage at time k-1 in the sampling time t, I k Is the current at time k, I k-1 Is the current at time k-1.
The vector expressions of an observation vector, an output vector and a vector to be estimated in a least squares with Forgetting Factor (FFRLS) system are as follows:
y k ＝U t,k
φ k ＝[1,U t,k-1 ,I k ,I k-1 ] T
θ k ＝[(1-b)OCV k ,b,c,d]
wherein, y k Is an output vector, phi k To observe the vector, θ k Is the vector to be estimated.
By calculation of a least square method (FFRLS) with forgetting factors, the open-circuit voltage and the ohmic internal resistance R are identified on line 0 Internal polarization resistance R 1 And time constant τ 1 ；
Wherein the ohmic resistance R 0 And a polarization resistor R 1 And time constant τ 1 Respectively as follows:
s2, setting a first set threshold, and processing the open-circuit voltage based on the first set threshold to obtain a first estimated value sequence of the open-circuit voltage;
the method comprises the steps that a first set threshold value is n, a sampling length is set (the sampling time is more than or equal to t seconds), if a first set condition is met within the set sampling length, a first estimated value sequence of open-circuit voltage meeting the first set condition is solved from the open-circuit voltage, and the first estimated value sequence is a corresponding first estimated value of the open-circuit voltage of each sampling point within the set sampling length;
the first setting condition is: b is less than or equal to n, that is,
in the embodiment of the present invention, the first set threshold is n, and the first set threshold is set according to the electric core and the actual working condition of the battery, that is, the first set thresholds of the electric cores of different types and the working conditions may be different.
Taking a 174Ah ternary battery as an example, under the NEDC working condition, n is set to be 0.96, the time length is set to be 3000s, when the value of n is less than or equal to 0.96 in the duration time and the duration time is greater than or equal to 3000s, first estimated values of open-circuit voltages of all sampling points in the time length are solved, and the first estimated values are respectively OCV 1,1 、OCV 1,2 、OCV 1,3 、OCV 1,4 、……。
S3, setting a second set threshold, and processing the open-circuit voltage based on the second set threshold to obtain a second estimated value sequence of the open-circuit voltage;
in the present invention, the processing of the open circuit voltage based on the second set threshold to obtain the second estimated value sequence of the open circuit voltage comprises:
s31, determining a second setting condition based on the second setting threshold; determining at least two groups of data segments meeting a second set condition in the open-circuit voltage;
the second setting condition is: 1-b | ≦ m, that is:
the second set threshold of the invention is m, and the second set threshold is set according to the battery core and the actual working condition of the battery, that is, the second set thresholds of the battery cores of different types and the working conditions can be different. The method comprises the step of finding out multiple groups of data points meeting a second set condition in the open-circuit voltage.
In the embodiment of the invention, taking a 174Ah ternary battery as an example, in the working condition of NEDC, m is made to be 0.002, and sampling points meeting the condition that |1-b | is less than or equal to m are found out, so that a plurality of groups of data segments are obtained.
S32, processing each group of data segments to obtain a second estimated value group of open-circuit voltage corresponding to each group of data segments;
in the invention, the processing of each group of data segments to obtain the second estimated value group of the open-circuit voltage corresponding to each group of data segments comprises:
s321, taking the two data points of each group as a second initial estimation value of the open-circuit voltage corresponding to the initial point of each group and a second ending estimation value of the open-circuit voltage corresponding to the ending point of each group respectively;
in the invention, the most advanced time in each group of data segments is taken as a starting point, the corresponding data is the second starting estimated value of the group of open-circuit voltage, and the most advanced time in each group of data segments is taken as an ending point, and the corresponding data is the second ending estimated value of the open-circuit voltage.
And S322, performing linear interpolation according to the second initial estimation value of the open-circuit voltage and the second ending estimation value of the open-circuit voltage of each group of data segments to obtain a second estimation value group of the open-circuit voltage corresponding to each group of data segments.
The step is to perform linear interpolation processing on each group of data segments meeting a second setting condition to obtain a plurality of intermediate second open-circuit voltage estimated values, and obtain a group of second open-circuit voltage estimated values corresponding to each group of data segments according to the second initial open-circuit voltage estimated value, the second end open-circuit voltage estimated value and the plurality of second intermediate open-circuit voltage estimated values.
Deleting a data point between the second initial estimation value of the open circuit voltage and the second ending estimation value of the open circuit voltage in each group of data segments, taking the second initial estimation value of the open circuit voltage and the second ending estimation value of the open circuit voltage as endpoint values of each group again, and interpolating data in the endpoint values, wherein the step is to prevent the open circuit voltage OCV from causing a large error when the b value is close to 1.
And S33, obtaining a second estimated value sequence of the open-circuit voltage according to the second estimated value groups of the open-circuit voltage corresponding to all the data segments.
In the embodiment of the invention, the plurality of open-circuit voltage second estimation value groups are collected according to the sampling time to obtain the open-circuit voltage second estimation value sequence.
In the embodiment of the present invention, the second estimated values of the open circuit voltages at the sampling points included in the second estimated value sequence are respectively OCVs 2,1 、OCV 2,2 、OCV 2,3 、OCV 2,4 、……。
S4, determining a first SOC sequence corresponding to the first estimation value sequence of the open-circuit voltage and a second SOC estimation value sequence corresponding to the second estimation value sequence of the open-circuit voltage based on a pre-calibrated SOC-OCV relation;
the SOC-OCV relationship of the invention is calibrated according to experiments made in advance. The SOC-OCV relationship may be represented by a table or a graph.
In the embodiment of the invention, the SOC-OCV relationship is embodied in the form of an SOC-OCV relationship table for calibrating the battery cell at different temperatures, and SOC (State of Charge, battery Charge State) corresponding to different open-circuit voltages can be obtained by checking the SOC-OCV relationship table;
specifically, in the embodiment of the present invention, in an SOC-OCV relationship table of different temperature calibration battery cells, under the NEDC working condition:
and comparing each open-circuit voltage first estimation value of the open-circuit voltage first estimation value sequence in an SOC-OCV relation table to obtain a plurality of first SOC estimation values at the current temperature, thereby forming a first SOC estimation value sequence.
And comparing each open-circuit voltage second estimation value of the open-circuit voltage second estimation value sequence in an SOC-OCV relation table to obtain a plurality of second SOC estimation values at the current temperature, thereby forming a second SOC estimation value sequence.
And S5, processing the first SOC estimation value sequence and the second SOC estimation value sequence according to the linear relation between the battery electric quantity change and the SOC change to obtain the battery capacity.
In the embodiment of the invention, the linear relation between the battery capacity change and the SOC change is an SOC-capacity gain method;
the SOC-electric quantity gain method is to calculate the capacity by the ratio of the charge/discharge electric quantity variation to the corresponding SOC variation, and the calculation formula of the capacity is as follows:
wherein C is the battery capacity.
In the present invention, the step of processing the first SOC estimation value sequence and the second SOC estimation value sequence to obtain the battery capacity according to the linear relationship between the battery power change and the SOC change includes:
s51, processing the first SOC estimation value sequence to obtain a first capacity value and processing the second SOC estimation value sequence to obtain a second capacity value according to the linear relation between the battery electric quantity change and the SOC change;
in this embodiment of the present invention, the step of processing the first SOC estimation value sequence to obtain a first capacity value includes:
first, the capacity calculation formula is converted into a matrix pattern:
order to
y i ＝SOC(t 2 )-SOC(t 1 )
Then, Y is equal to X.H + V
Wherein: h ═ k 1 ,k 2 ] T For the coefficient to be solved, C is 1/k 1 And V represents a random noise, and the noise is random noise,
then, fitting the matrix pattern by a least square method to solve and determine a coefficient to be solved in the matrix pattern;
specifically, the coefficient H to be obtained is obtained by a least square method in order to further obtain the battery capacity C.
The principle of the least squares algorithm is to determine the parameter vector value H by minimizing the residual squares S of all observations, whose sum of residual squares is:
S＝(Y-XH) T (Y-XH)
based on the first SOC estimation value sequence, a minimum binary estimation value of the coefficient H to be solved is obtained by deriving the above formula and making the partial derivative equal to zero:
H＝(X T X) -1 X T Y
obtaining a first estimated value of a coefficient H to be solved by a least square method;
and finally, obtaining a first battery capacity value according to the relation between the coefficient to be solved and the battery capacity.
The relation between the first estimation value of the coefficient H to be solved and the battery capacity is as follows:
H＝[k 1 ,k 2 ] T and C1 ═ 1/k 1 ；
Solving to obtain a first battery capacity value C1 ═ 1/k 1 。
In the embodiment of the present invention, the step of processing the second SOC estimation value sequence to obtain the second capacity value is the same as the step of processing the first SOC estimation value sequence to obtain the first capacity value;
suppose the coefficient to be solved is H ═ k 3 ,k 4 ] T Determining a second battery capacity value C2 of 1/k for the coefficient to be determined 3 。
And S52, weighting the first capacity value and the second capacity value to obtain the battery capacity.
Since the ranges and accuracies of the two OCV sequence values obtained in step S2 and step S3 are different from each other, the weights of the first capacity value and the second capacity value are different from each other when calculating the battery capacity.
In the embodiment of the invention, the first battery capacity value C1 and the second battery capacity value C2 are respectively obtained based on a least square method, and since the OCV estimated value of the first battery capacity value C1 is reasonable in value range of the parameter b and high in accuracy, the weight of C1 is 0.6, the weight of C2 is 0.4, and the final estimated value of capacity C is C1 × 0.6+ C2 × 0.4.
The embodiment of the device comprises:
the present invention also provides an online estimation apparatus of battery capacity, as shown in fig. 2, including a memory, a processor and a program stored in the memory and operable on the processor, wherein when the processor executes the program, the steps of the online estimation method of battery capacity are implemented.
The detailed procedures of the steps of the online estimation method of the battery capacity are described in detail in the method embodiments, and are not described herein again.
Has the advantages that:
the first-order RC equivalent circuit is used as a model, terminal voltage and current are used as observation values to perform online identification of parameters to be estimated (OCV, R0, R1 and tao1), the model is simple, the calculated amount is small, the waste of time cost caused by long-time shelving is avoided by online identification of the OCV, and the method has practical application significance;
after the screening of the first set threshold and the second set threshold is set, the estimation precision of the effective OCV is improved, and the precision of the SOC obtained through OCV table lookup is improved in the phase-change data preprocessing stage, so that the accuracy of capacity estimation is improved;
by distributing the weight to the first capacity value C1 and the second capacity value C2, the accuracy of the battery capacity can be continuously improved on the original basis, and the robustness of capacity estimation is guaranteed under the condition that a certain capacity value meets the condition that the data is small.
In summary, the invention provides an online estimation method for battery capacity, which combines a model and a data-driven method to perform online estimation of battery capacity, can significantly improve the accuracy of online estimation of capacity, can significantly adapt to actual working conditions, and has certain practicability. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present invention as set forth in the appended claims.
Claims (9)
1. An online estimation method for battery capacity is characterized by at least comprising the following steps:
constructing an RC equivalent circuit model of the battery based on the obtained online terminal voltage and current of the battery, and processing the RC equivalent circuit model to obtain a parameter to be estimated; the parameter to be estimated comprises an open circuit voltage;
setting a first set threshold, and processing the open-circuit voltage based on the first set threshold to obtain a first estimated value sequence of the open-circuit voltage;
setting a second set threshold, and processing the open-circuit voltage based on the second set threshold to obtain a second estimated value sequence of the open-circuit voltage;
determining a first SOC estimation value sequence corresponding to the first open-circuit voltage estimation value sequence and a second SOC estimation value sequence corresponding to the second open-circuit voltage estimation value sequence based on a pre-calibrated SOC-OCV relation;
and processing the first SOC estimation value sequence and the second SOC estimation value sequence according to the linear relation between the battery electric quantity change and the SOC change to obtain the battery capacity.
2. The method according to claim 1, wherein the step of processing the RC equivalent circuit model to obtain the parameter to be estimated comprises:
processing the RC equivalent circuit model to obtain a transfer function;
and performing online identification on the transfer function by adopting a least square method with a forgetting factor to estimate the parameters to be estimated of the battery.
3. The method according to claim 2, wherein the RC equivalent circuit model is:
wherein, U 1 To polarize the voltage, U t Terminal voltage, I current, OCV open circuit voltage, R 1 For polarizing internal resistance, R 0 τ is the time constant for ohmic internal resistance.
4. The method according to claim 2, wherein if a first predetermined condition is satisfied within a predetermined sampling length, a sequence of first estimated values of the open-circuit voltage satisfying the first predetermined condition is solved from the open-circuit voltage;
the first setting condition is:
where Δ T is the sampling time interval, R 1 For polarizing internal resistance, C 1 In order to be a polarization capacitance, the polarization capacitance,n is a first set threshold.
5. The method for on-line estimation of battery capacity according to claim 2, wherein the processing of the open circuit voltage based on the second set threshold to obtain the sequence of second estimated open circuit voltages comprises:
determining a second setting condition based on a second setting threshold; determining at least two groups of data segments meeting a second set condition in the open-circuit voltage; the second setting condition is as follows:
wherein m is a second set threshold;
processing each group of data segments to obtain a second estimation value group of the open-circuit voltage corresponding to each group of data segments;
and obtaining a second estimated value sequence of the open-circuit voltage according to the plurality of second estimated value groups of the open-circuit voltage.
6. The method of claim 5, wherein the step of processing each group of data segments to obtain the second estimated value set of open-circuit voltages corresponding to each group of data segments comprises:
taking the two data points of each group as a second initial estimation value of the open-circuit voltage corresponding to the initial point of each group and a second ending estimation value of the open-circuit voltage corresponding to the ending point of each group respectively;
and performing linear interpolation according to the second initial estimation value of the open-circuit voltage and the second end estimation value of the open-circuit voltage of each group of data segments to obtain a second estimation value group of the open-circuit voltage corresponding to each group of data segments.
7. The method for on-line estimation of battery capacity according to claim 1, wherein the step of processing the first SOC estimation value sequence and the second SOC estimation value sequence to obtain the battery capacity according to the linear relationship between the battery capacity variation and the SOC variation comprises:
processing the first SOC estimation value sequence to obtain a first capacity value and processing the second SOC estimation value sequence to obtain a second capacity value according to the linear relation between the battery electric quantity change and the SOC change;
and weighting the first capacity value and the second capacity value to obtain the battery capacity.
8. The method of claim 1, wherein the linear relationship between the change of battery capacity and the change of SOC is a SOC-capacity gain method; the SOC-electric-quantity gain method calculates the battery capacity by the ratio of the charge/discharge electric-quantity variation to the corresponding SOC variation.
9. An online estimation device of battery capacity, comprising a memory, a processor and a program stored in the memory and operable on the processor, wherein the processor executes the program to implement the steps of the online estimation method of battery capacity according to any one of claims 1 to 8.
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| CN116500458B (en) * | 2023-06-27 | 2023-09-22 | 中国第一汽车股份有限公司 | Power battery capacity evaluation method and device, vehicle and electronic device |
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