CN114398762A - Fitting method and device of high-precision energy storage element model based on goblet sea squirt algorithm - Google Patents

Fitting method and device of high-precision energy storage element model based on goblet sea squirt algorithm Download PDF

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CN114398762A
CN114398762A CN202111584480.7A CN202111584480A CN114398762A CN 114398762 A CN114398762 A CN 114398762A CN 202111584480 A CN202111584480 A CN 202111584480A CN 114398762 A CN114398762 A CN 114398762A
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周孟雄
纪捷
张芮
周海
朱跃伍
王夫诚
张佳钰
秦泾鑫
苏皎月
汤健康
郭仁威
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Huaiyin Institute of Technology
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Abstract

The invention relates to the technical field of battery energy optimization, and discloses a fitting method and a fitting device of a high-precision energy storage element model based on a goblet sea squirt algorithm, wherein the high-precision energy storage element model is firstly constructed, is a bipolar second-order model formed by coupling two circuit models and comprises a tracking battery SOC module and a transient response module, and the tracking battery SOC module consists of a resistor, a current controlled current source and a capacitor; the transient response template consists of an Rs resistance wire and two RC networks; and then obtaining parameters of the high-precision energy storage element model, establishing a battery basic model, initializing the model parameters, and optimizing the performance curve parameters of the battery basic model in the step 2 by using a goblet sea squirt algorithm. Compared with the prior art, the method has the advantages that the relation between the core parameters and the performance representation of the energy storage element is found through an intelligent method, so that the method has the capability of predicting the performance indexes of the materials of the same type while establishing an accurate energy storage element model.

Description

Fitting method and device of high-precision energy storage element model based on goblet sea squirt algorithm
Technical Field
The invention relates to the technical field of battery energy optimization, in particular to a method and a device for fitting a high-precision energy storage element model based on a goblet sea squirt algorithm.
Background
With the progress of society and the development of science and technology, the demand of human civilization on energy storage is increasing, non-renewable resources are exhausted for one day, and the energy conservation and storage utilization become one of important problems of the progress of society science and technology. And if the development of the energy storage equipment can adopt a digital modeling method to predict the performance, the research and development cost is greatly saved, and the development is promoted.
At present, lithium battery technology occupies a half-wall river mountain of energy storage technology, and is widely applied to portable consumer electronic products such as notebook computers and mobile systems at present. The common energy storage model has several models, and is the simplest model only considering a voltage source for an ideal model, wherein all internal parameters are ignored; the linear model includes an ideal cell with an open circuit voltage and an equivalent series resistance. However, the model ignores the cell internal impedance behavior as a function of state of charge (SOC) and electrolyte concentration. The Thevenin equivalent model consists of open-circuit voltage VOC, internal resistance Rs, a capacitor Cp and an overvoltage resistor Rp, and cannot well fit a performance curve of a battery; the traditional energy storage element can not well meet the characteristics of high energy and rated power, prolonged service life, prolonged discharge period and the like.
Therefore, in order to facilitate the prediction of the performance indexes of the same type of materials, how to well fit the performance curve of the battery and obtain the optimal battery performance parameters is an urgent problem to be solved.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a method and a device for fitting a high-precision energy storage element model based on a goblet sea squirt algorithm.
The technical scheme is as follows: the invention provides a fitting method of a high-precision energy storage element model based on a goblet sea squirt algorithm, which comprises the following steps:
step 1: constructing a high-precision energy storage element model which is a bipolar second-order model formed by coupling two circuit models and comprises a tracking battery SOC module and a transient response module, wherein the tracking battery SOC module is composed of a resistor and a current source I controlled by currentbAnd a capacitor C-usableThe resistor represents the energy loss of the battery when the battery is not used for a long time, and the current controlled current source can be used for the capacitor C-usableCarrying out charging and discharging; the transient response template consists of an Rs resistance wire and two RC networks, wherein the Rs resistance wire represents internal loss and is responsible for instantaneous voltage drop, and the two RC networks are responsible for transient response in a short time and a long time;
step 2: obtaining parameters of the high-precision energy storage element model, wherein the parameters comprise a resistor Rs in a transient response module and two RC networksThe network comprises a resistor RP1, a resistor RP2, a capacitor CP1 and a capacitor CP2, a basic battery model is established, and model parameters are initialized, wherein the parameter-related expression is determined by the following formula: VOC (SOC) ═ a0+a1SOC+a2SOC2+a3SOC3(ii) a Wherein, a0、a1、a2、a3Correlation parameters for correlating core parameters VOC and SOC;
and step 3: and (3) optimizing the performance curve parameters of the basic battery model in the step 2 by using a goblet sea squirt algorithm.
Further, the method for optimizing the performance curve parameters by using the kava algorithm in the step 3 specifically comprises the following steps:
step 3.1: initializing VOC (volatile organic compounds), SOC (state of charge) values, emptying algorithm acceleration a and initial speed v for a search agent and an optimal candidate solution of a high-precision energy storage element model, and recording an error value of a current target parameter fitting result;
step 3.2: a of input battery0、a1、a2、a3Calculating the initial fitness value of the N goblet ascidians;
step 3.3: selecting food positions, sequencing the goblet sea squirts according to fitness values, wherein the optimal goblet sea squirt position arranged at the head position is the set position;
step 3.4: selecting a leader and a follower; after the food position is selected, the rest N-1 goblet ascidians in the group take the goblet ascidians in the first half as the leader and the rest goblet ascidians as the followers;
step 3.5: updating the position of the population:
the leader location is updated first, followed by further updates to the follower location, with the leader location update formula as follows:
Figure BDA0003427431520000021
wherein the content of the first and second substances,
Figure BDA0003427431520000022
display devicePosition of first goblet sea squirt, FjThe position of the food source in the j dimension, ubjIs the position in the j dimension, lbjIs a lower bound of the j-th dimension, c1、c2、c3Is a random number; c. C1Is the most important parameter in the leader, which is the convergence factor; updating the follower position, we propose the following formula:
Figure BDA0003427431520000023
wherein, when i is more than or equal to 2,
Figure BDA0003427431520000024
represents the position of the first follower, t represents time, v represents velocity; a. the formula for v is expressed as:
Figure BDA0003427431520000025
vfinalis the final velocity, x is the final position coordinate, x0Is the initial coordinate;
step 3.6: comparing the updated individual fitness value of each goblet ascidian with the current food fitness value, and if the updated fitness value of each goblet ascidian is superior to the food, taking the goblet ascidian position with the optimal fitness value as a new food position;
step 3.7: and judging whether the iteration times reach the set target times, if not, returning to the step 3.5, and if so, outputting all relation parameters.
Further, the convergence factor in step 3.5 is as follows:
Figure BDA0003427431520000031
wherein L is the current iteration, L is the maximum iteration number, c2And c3In [0,1 ]]Random number within interval, control parameter c2And c3In [0,1 ]]Random numbers within the interval.
Further, the step 3.5 sets no speed, and the specific formula for updating the position of the follower is as follows:
Figure BDA0003427431520000032
wherein i is more than or equal to 2,
Figure BDA0003427431520000033
representing the position of two goblet ascidians in close proximity to each other in the d dimension.
The invention also discloses a fitting device of the high-precision energy storage element model based on the goblet sea squirt algorithm, which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the computer program realizes the fitting method of the high-precision energy storage element model based on the goblet sea squirt algorithm when being loaded to the processor.
Has the advantages that:
according to the invention, a bipolar second-order model formed by coupling two circuit models can be used for fitting the performance curve of the battery, the performance curve of the battery can be better optimized by combining a goblet sea squirt algorithm, and the optimal solution of the output of a calculation module is obtained after the high-precision energy storage model is optimized, so that the performance curve of the battery is closer to an actual value; the total utilization rate of the energy is obviously improved by optimizing the energy storage technology, and the optimized performance curve can be used for predicting the performance transition trend of the novel equipment under the condition of parameter change.
Drawings
FIG. 1 is a schematic structural diagram of a high-precision energy storage element model according to the present invention;
FIG. 2 is a flow chart of a performance curve optimization algorithm of the present invention;
FIG. 3 is a graph comparing a simulated curve of SOC with time and an actual curve of the battery according to the present invention;
FIG. 4 is a graph comparing a simulation curve of available capacity of a battery according to the present invention with an actual curve;
FIG. 5 is a graph comparing a simulation curve of VOC of a battery according to the present invention with an actual curve of SOC of the battery;
FIG. 6 is a comparison graph of fitting error analysis for SOC, available capacity, VOC data according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention discloses a fitting method and a fitting device of a high-precision energy storage element model based on a zun sea squirt algorithm, as shown in figure 1, the high-precision energy storage element model is a bipolar second-order model formed by coupling two circuit models, and comprises a tracking battery SOC module and a transient response module, wherein the tracking battery SOC module is composed of a resistor and a current source I controlled by currentbAnd a capacitor C-usableThe resistor represents the energy loss when the battery is not used for a long time, and the current source controlled by the current can charge and discharge the capacitor; the transient response template consists of a resistance wire Rs and two RC networks, wherein the two RC networks comprise a resistor RP1, a resistor RP2, a capacitor CP1 and a capacitor CP2, the Rs represents internal loss and is responsible for instantaneous voltage drop, and the two RC networks are responsible for transient response of short time and long time afterwards.
The invention discloses a fitting method of a high-precision energy storage element model based on a goblet sea squirt algorithm, which is shown in the attached figure 2 and specifically comprises the following steps:
(1) obtaining parameters of the high-precision energy storage element model, wherein the parameters comprise a resistor Rs and two RC networks in a transient response module, the two RC networks comprise a resistor RP1, a resistor RP2, a capacitor CP1 and a capacitor CP2, establishing a battery basic model, initializing the parameters of the model, and determining a parameter-related expression through the following formula:
VOC(SOC)＝a0+a1 SOC+a2SOC2+a3SOC3。
(2) optimizing the performance curve of the model in the step (1) by using a goblet sea squirt algorithm, and specifically comprising the following steps:
(21) initializing VOC and SOC values of a search agent and an optimal candidate solution of a high-precision energy storage element model, emptying algorithm acceleration a and initial speed v, and recording an error value of a current target parameter fitting result; in the step (21), the fitting accuracy of the target parameters is input to meet the error requirement range of the target model.
(22) A of input battery0、a1、a2、a3And calculating the initial fitness. Calculating the fitness value of the N goblet ascidians; finding the Salp with the best fitness and assigning the best position to the source food under the variable as the chase of the chain of Salp.
(23) Selecting food positions, sequencing the goblet sea squirts according to fitness values, wherein the optimal goblet sea squirt position arranged at the head position is the set position;
(24) selecting a leader and a follower, and after selecting a good food position, regarding the rest N-1 goblet ascidians in the group as the leader and regarding the goblet ascidians in the first half as followers;
(25) updating the position of the goblet sea squirt; the population is divided into two groups: a leader and a follower. In the process of moving and foraging of the goblet sea squirt chain, updating the position of the leader by using the convergence factor and the weight factor, firstly updating the position of the leader, and then further updating the position of the follower, wherein the updating formula of the position of the leader is as follows:
Figure BDA0003427431520000041
wherein the content of the first and second substances,
Figure BDA0003427431520000051
showing the location of the first sea squirt, FjThe position of the food source in the j dimension, ubjIs the position in the j dimension, lbjIs a lower bound of the j-th dimension, c1、c2、c3Is a random number; indicating that the Leader only updates its position relative to the food source, c1Is the most important parameter in the leader, which is the convergence factor:
Figure BDA0003427431520000052
wherein L is the current iteration, L is the maximum iteration number, c2And c3In [0,1 ]]Random number within interval, control parameter c2And c3In [0,1 ]]Random numbers in the interval are used for enhancing the randomness of the groups and improving the diversity of the chain groups.
In the process of moving and foraging of the goblet sea squirt chain, the followers pass through the influence between the front and the rear individuals, the displacement of the followers accords with Newton's law of motion, and in order to update the positions of the followers, the following formula is provided:
Figure BDA0003427431520000053
wherein, when i is more than or equal to 2,
Figure BDA0003427431520000054
represents the position of the first follower, t represents time, v represents velocity; a. the formula for v is expressed as:
Figure BDA0003427431520000055
vfinalis the final velocity, x is the final position coordinate, x0Is the initial coordinate; to explain the meaning of v: since the final velocity v is required to solve for afinalTo solve for vfinalThe formula is needed
Figure BDA0003427431520000056
And (6) solving.
Since the optimization time is iterative and the difference between iterations is 1, consider v0When 0, the formula is:
Figure BDA0003427431520000057
wherein i is more than or equal to 2,
Figure BDA0003427431520000058
representing the position of two goblet ascidians in close proximity to each other in the d dimension.
(26) And finding out the optimal individual fitness value to update the food position. And comparing the updated individual fitness value of each goblet ascidian with the current food fitness value, and if the updated fitness value of each goblet ascidian is superior to that of the food, taking the goblet ascidian position with the optimal fitness value as a new food position (by comparing the existing database with the relationship parameter information contained in the current position, the current VOC can be obtained, and the smaller the difference value is, the larger the fitness is by utilizing the VOC and the difference value in the database).
(27) And (5) judging whether the iteration times reach the set target times, if so, returning to the step (25), and if so, outputting all relation parameters.
The complexity relationship of the above algorithm is as follows:
O(l(d*n+Cof*n))
where l represents the number of iterations, d is a variable (dimension), n is the number of solutions, and Cof represents the cost of the objective function.
The invention also discloses a fitting device of the high-precision energy storage element model based on the goblet sea squirt algorithm, which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the computer program realizes the fitting method of the high-precision energy storage element model based on the goblet sea squirt algorithm when being loaded to the processor.
The algorithm used in the optimization process is bionic optimization of the goblet sea squirt algorithm applied to engineering design problems, the algorithm can effectively improve the random solution of initial data and converges towards the optimal direction, the technology similar to that of other groups is adopted, the position of Salp is defined as performing mathematical modeling on Salp chains, and the groups are divided into two groups of leader and follower. The leading is the goblet ascidians at the front end of the food chain, while the remaining goblet ascidians are considered as followers, leading the leading group, and the followers follow each other (direct or indirect leader).
The embodiment sets the objective function as the energy storage element performance optimization objective. And the performance optimization target is to fit the load change operation condition of the energy storage element during power supply operation. The performance optimization target in the system is the charge state, the charge-discharge state and the voltage change condition of the energy storage equipment along with time; the available capacity varies with the cycle number; the internal voltage and state of charge of the energy storage element. Parameters are output by each module in the system, the purpose of optimizing a performance curve is achieved through calculation of an optimization target model, and then the parameters are input into an algorithm for optimization.
A high-precision energy storage element model of a goblet sea squirt algorithm. The load changes the operation condition when the energy storage element supplies power. And (4) inputting the output meeting the conditions, and optimizing the algorithm. And inputting all output forces meeting the conditions under the condition of meeting the optimization target model, optimizing through the goblet sea squirt algorithm, and outputting an optimal solution. With the process, the purpose of optimizing and maximizing the performance curve is achieved.
After the high-precision energy storage element model fitting method is adopted, the simulated performance curve of the load curve of the lithium battery to be used NCR18650PF is compared with the actual performance curve, and then the graphs shown in the figures 3-6 are obtained.
As shown in FIG. 3, in the comparison of the simulated performance curve and the actual performance curve, the simulated performance curve of the battery SOC of the high-precision element fitting model along with the change of the time is basically the same as the actual performance curve trend of the battery SOC along with the change of the time, and the lowest battery state of charge is basically coupled with 0.33, so that the simulated performance curve and the actual performance curve conform to the actual state of charge of the battery.
As shown in FIG. 4, in the comparison between the simulated performance curve and the actual performance curve, the simulated performance curve of the available capacity of the battery of the high-precision element fitting model along with the change of the available capacity of the battery along with time has the same trend as the actual performance curve of the available capacity of the battery along with the change of the available capacity of the battery along with time, and the trends gradually decrease, which conforms to the available capacity state of the battery.
As shown in fig. 5, in the comparison between the simulated performance curve and the actual performance curve, the simulated performance curve of the internal voltage VOC of the battery of the high-precision element fitting model along with the change of the state of charge SOC has the same trend as the actual performance curve of the internal voltage VOC along with the change of the state of charge SOC, and both of the curves have a linear rising trend, and the curves conform to the actual voltage and charge trend of the battery.
As shown in FIG. 6, the model battery fitting method is an error analysis comparison graph of SOC, available capacity and VOC data, and in comparison of a simulation performance curve and an actual performance curve, the accuracy of the model battery fitting method is as high as more than 99.5%, the error is only 0.5%, the fitting degree is high, and the effect is good.
In summary, the fitting of the high-precision energy storage element model to the energy storage element performance curve of the invention utilizes the coupling relation between the technology and the known device for searching the core parameters, and contributes to the design and performance exploration of the energy storage device. The method is applied to power supply and fitting scenes, can accurately fit a performance curve when the energy storage element is powered on and operates, and is good in fitting effect and excellent in method. The precision is as high as more than 99.5%, the error is only 0.5%, and the fitting efficiency is obviously improved compared with that of other existing energy storage element models. The method is used for predicting the performance transition trend of the novel equipment under the condition of parameter change by utilizing the innovative method for performance characterization and exploration of the novel energy storage equipment.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.

Claims (5)

1. A fitting method of a high-precision energy storage element model based on a goblet sea squirt algorithm is characterized by comprising the following steps:
step 1: constructing a high-precision energy storage element model which is a bipolar second-order model formed by coupling two circuit models and comprises a tracking battery SOC module and a transient response module, wherein the tracking battery SOC module is composed of a resistor and a current source I controlled by currentbAnd a capacitor C-usableThe resistor represents the energy loss of the battery when the battery is not used for a long time, and the current controlled current source can be used for the capacitor C-usableCarrying out charging and discharging; the transient response template consists of an Rs resistance wire and two RC networks, wherein the Rs resistance wire represents internal loss and is responsible for instantaneous voltage drop, and the two RC networks are responsible for subsequent instantaneous voltage dropShort and long transient response;
step 2: obtaining parameters of the high-precision energy storage element model, wherein the parameters comprise a resistor Rs and two RC networks in a transient response module, the two RC networks comprise a resistor RP1, a resistor RP2, a capacitor CP1 and a capacitor CP2, establishing a battery basic model, and initializing model parameters, and the parameter-related expression is determined by the following formula: VOC (SOC) ═ a0+a1SOC+a2SOC2+a3SOC3(ii) a Wherein, a0、a1、a2A3 is a correlation parameter correlating core parameters VOC and SOC;
and step 3: and (3) optimizing the performance curve parameters of the basic battery model in the step 2 by using a goblet sea squirt algorithm.
2. The method as claimed in claim 1, wherein the method for optimizing the performance curve parameters by using the ascidian algorithm in step 3 is specifically as follows:
step 3.1: initializing VOC (volatile organic compounds), SOC (state of charge) values, emptying algorithm acceleration a and initial speed v for a search agent and an optimal candidate solution of a high-precision energy storage element model, and recording an error value of a current target parameter fitting result;
step 3.2: a of input battery0、a1、a2、a3Calculating the initial fitness value of the N goblet ascidians;
step 3.3: selecting food positions, sequencing the goblet sea squirts according to fitness values, wherein the optimal goblet sea squirt position arranged at the head position is the set position;
step 3.4: selecting a leader and a follower; after the food position is selected, the rest N-1 goblet ascidians in the group take the goblet ascidians in the first half as the leader and the rest goblet ascidians as the followers;
step 3.5: updating the position of the population:
the leader location is updated first, followed by further updates to the follower location, with the leader location update formula as follows:
Figure FDA0003427431510000021
wherein the content of the first and second substances,
Figure FDA0003427431510000022
showing the location of the first sea squirt, FjThe position of the food source in the j dimension, ubjIs the position in the j dimension, lbjIs a lower bound of the j-th dimension, c1、c2、c3Is a random number; c. C1Is the most important parameter in the leader, which is the convergence factor; updating the follower position, we propose the following formula:
Figure FDA0003427431510000023
wherein, when i is more than or equal to 2,
Figure FDA0003427431510000024
represents the position of the first follower, t represents time, v represents velocity; a. the formula for v is expressed as:
Figure FDA0003427431510000025
wherein v isfinalIs the final velocity, x is the final position coordinate, x0Is the initial coordinate;
step 3.6: comparing the updated individual fitness value of each goblet ascidian with the current food fitness value, and if the updated fitness value of each goblet ascidian is superior to the food, taking the goblet ascidian position with the optimal fitness value as a new food position;
step 3.7: and judging whether the iteration times reach the set target times, if not, returning to the step 3.5, and if so, outputting all relation parameters.
3. A method as claimed in claim 2, wherein the convergence factor in step 3.5 is as follows:
Figure FDA0003427431510000026
wherein L is the current iteration, L is the maximum iteration number, c2And c3In [0,1 ]]Random number within interval, control parameter c2And c3In [0,1 ]]Random numbers within the interval.
4. A method as claimed in claim 2, wherein the step 3.5 of setting no velocity and updating the specific formula of the follower position is as follows:
Figure FDA0003427431510000027
wherein i is more than or equal to 2,
Figure FDA0003427431510000028
representing the position of two goblet ascidians in close proximity to each other in the d dimension.
5. A fitting device of a high-precision energy storage element model based on a goblet sea squirt algorithm, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the fitting method of the high-precision energy storage element model based on the goblet sea squirt algorithm according to any one of claims 1 to 4 when being loaded on the processor.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114626765A (en) * 2022-05-07 2022-06-14 河南科技学院 Intelligent scheduling method for formation of power lithium battery

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104965179A (en) * 2015-07-06 2015-10-07 首都师范大学 Lithium ion storage battery temperature combinational circuit model and parameter identification method thereof
US20150316618A1 (en) * 2014-05-05 2015-11-05 Apple Inc. Methods and apparatus for battery power and energy availability prediction
CN109284860A (en) * 2018-08-28 2019-01-29 温州大学 A kind of prediction technique based on orthogonal reversed cup ascidian optimization algorithm
CN111126549A (en) * 2019-12-24 2020-05-08 昆明理工大学 Double-star spectrum fitting method based on strategy improved goblet and sea squirt intelligent algorithm
CN113037213A (en) * 2021-03-04 2021-06-25 国网新疆电力有限公司信息通信公司 Photovoltaic cell model parameter identification method and device based on goblet sea squirt group algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150316618A1 (en) * 2014-05-05 2015-11-05 Apple Inc. Methods and apparatus for battery power and energy availability prediction
CN104965179A (en) * 2015-07-06 2015-10-07 首都师范大学 Lithium ion storage battery temperature combinational circuit model and parameter identification method thereof
CN109284860A (en) * 2018-08-28 2019-01-29 温州大学 A kind of prediction technique based on orthogonal reversed cup ascidian optimization algorithm
CN111126549A (en) * 2019-12-24 2020-05-08 昆明理工大学 Double-star spectrum fitting method based on strategy improved goblet and sea squirt intelligent algorithm
CN113037213A (en) * 2021-03-04 2021-06-25 国网新疆电力有限公司信息通信公司 Photovoltaic cell model parameter identification method and device based on goblet sea squirt group algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
俞聪 等: "电动汽车电池测试系统建模与仿真", 《机电工程》, vol. 30, no. 7, pages 862 - 865 *
赵沁峰 等: "锂离子电池全生命周期剩余使用寿命预测", 《电源学报》, pages 14 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114626765A (en) * 2022-05-07 2022-06-14 河南科技学院 Intelligent scheduling method for formation of power lithium battery
CN114626765B (en) * 2022-05-07 2022-09-16 河南科技学院 Intelligent scheduling method for formation of power lithium battery

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