CN117538759B - Method for obtaining direct-current internal resistance high flux of lithium ion battery - Google Patents

Abstract

The invention relates to the technical field of lithium ion battery testing, in particular to a method for obtaining direct current internal resistance and high flux of a lithium ion battery, which comprises the following steps: determining at least one target change condition from preset experimental conditions, and taking the remaining conditions as control variables; selecting a target calculation model, and calculating the first direct current internal resistance of the lithium ion battery under different numerical combinations under different change conditions; performing experiments based on the target change conditions and the control variables to obtain second direct current internal resistance of the lithium ion battery under different numerical combinations of the target change conditions; optimizing parameters of the target calculation model; and obtaining other values or direct current internal resistances under other target change conditions based on the optimal parameters of the target calculation model in a high flux manner. The method aims to solve the problem that a large amount of experiments and time cost are required to be consumed for obtaining the direct current resistance of the lithium ion battery under all experimental conditions, and the direct current internal resistance data under other conditions is calculated through a small amount of direct current internal resistance data under different conditions.

Description

High-throughput acquisition method of DC internal resistance of lithium-ion batteries

Technical field

The present invention relates to the technical field of lithium-ion battery testing, and specifically to a high-throughput acquisition method of DC internal resistance of a lithium-ion battery.

Background technique

The DC internal resistance of lithium-ion batteries is the apparent internal resistance of lithium-ion batteries under constant current and is an important factor affecting battery rate performance and power performance. Compared with the AC internal resistance measured by electrochemical impedance spectroscopy, the DC internal resistance It more intuitively reflects the performance of lithium-ion batteries under working conditions. Therefore, by studying the DC internal resistance of lithium-ion batteries, it is of great significance to evaluate the battery capacity, power and battery aging status of lithium-ion batteries under different conditions.

The measurement of DC internal resistance of lithium-ion batteries is affected by many factors, including temperature during measurement, battery charge state, measurement current, and measurement time. Normally, obtaining the DC resistance of lithium-ion batteries under all experimental conditions requires a lot of experimental costs and time costs; therefore, it is necessary to use a small amount of lithium-ion DC internal resistance data under different conditions to deduce the DC resistance under other conditions. DC internal resistance.

Contents of the invention

The purpose of the present invention is to provide a high-throughput method for obtaining the DC internal resistance of a lithium-ion battery to solve the problem that obtaining the DC resistance of the lithium-ion battery under all experimental conditions requires a large amount of experimental cost and time cost, and achieves A small amount of lithium-ion DC internal resistance data under different conditions is used to estimate DC internal resistance data under other conditions.

The present invention provides a high-throughput method for obtaining the DC internal resistance of a lithium-ion battery, which includes the following steps:

Determine at least one target change condition from the preset experimental conditions, and use the remaining conditions as control variables;

Select the target calculation model based on the number of target changing conditions to calculate the first DC internal resistance of the lithium-ion battery under different numerical combinations of different changing conditions;

Conduct experiments based on target change conditions and control variables to obtain the second DC internal resistance of the lithium-ion battery under different numerical combinations of target change conditions;

Based on the first DC internal resistance of the lithium-ion battery and the second DC internal resistance of the lithium-ion battery, the parameters of the target calculation model are optimized to obtain the optimal parameters of the target calculation model;

Based on the optimal parameters of the target calculation model, the DC internal resistance under other values or other target control changing conditions is obtained through high-throughput.

Further, determine at least one target change condition from the preset experimental conditions, and use the remaining conditions as control variables, including:

Based on three preset experimental conditions: battery charge state, measurement current, and measurement time, at least one of the three preset experimental conditions is selected as the target change condition, and the remaining conditions are used as control variables.

Furthermore, a target calculation model is selected based on the number of target changing conditions to calculate the first DC internal resistance of the lithium-ion battery under different numerical combinations of different changing conditions, including:

Select two conditions from the battery charge state, measurement current, and measurement time as control variables to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining changing conditions.

Further, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> DC internal resistance under;/> Indicates that the changing condition is the measured current/> DC internal resistance under;/> Indicates that the change condition is the measurement time/> DC internal resistance under;/> are all model parameters.

Furthermore, the target calculation model is selected based on the number of target changing conditions, and the first DC internal resistance of the lithium-ion battery under different numerical combinations of different changing conditions is also included:

Select one condition from the battery charge state, measurement current, and measurement time as the control variable to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions.

Furthermore, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> and measuring current/> DC internal resistance below;

In the formula, Indicates that the changing condition is the battery charge state/> and measurement time/> DC internal resistance below;

In the formula, Indicates that the changing condition is the measured current/> and measurement time/> DC internal resistance under;/> are all model parameters.

Furthermore, the target calculation model is selected based on the number of target changing conditions, and the first DC internal resistance of the lithium-ion battery under different numerical combinations of different changing conditions is also included:

The three conditions of battery charge state, measurement current, and measurement time are all used as changing conditions, and the first DC internal resistance of the lithium-ion battery under different numerical combinations of the three changing conditions is obtained.

Furthermore, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of three changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> , measure current/> and measurement time/> DC internal resistance under,/> are all model parameters.

Furthermore, the parameters of the target calculation model were optimized based on the first DC internal resistance of the lithium-ion battery and the second DC internal resistance of the lithium-ion battery, and the optimal parameters were obtained, including:

Calculate the root mean square error between the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery, and use the minimum value between the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery. The root mean square error is taken as the target, and the parameters of the target calculation model are used as the optimization object for optimization to obtain the optimal parameters of the target calculation model.

Furthermore, the DC internal resistance under other target control changing conditions is obtained through high-throughput based on the optimal parameters of the target calculation model, including:

Bring the optimal parameters into the target calculation model to obtain the optimized target calculation model;

Enter the numerical combination of any changing conditions within the preset experimental conditions, and obtain the DC internal resistance of the lithium-ion battery within the full range of experimental conditions with high throughput.

The technical solutions of the embodiments of the present invention have at least the following advantages and beneficial effects:

The high-throughput acquisition method of lithium-ion battery DC internal resistance provided by the present invention obtains the lithium-ion battery under different numerical combinations of target changing conditions by calculating the first DC internal resistance of the lithium-ion battery under different changing conditions and different numerical combinations. second DC internal resistance; and then optimize the parameters of the target calculation model based on the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery, obtain the optimal parameters of the target calculation model and obtain other parameters with high throughput DC internal resistance under changing conditions of numerical or other target control; this method can obtain other DC internal resistance data under changing conditions of vertical or target control that have not been tested through high-throughput DC internal resistance data under a small amount of changing conditions. This saves experiment and time costs; on the other hand, the calculation model conforms to the internal physical mechanism of lithium-ion batteries, achieving high-throughput acquisition of the DC internal resistance of lithium-ion batteries with high precision and simple operation.

Description of the drawings

Figure 1 is a schematic flowchart of a high-throughput acquisition method for DC internal resistance of a lithium-ion battery provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, rather than all embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

It will be understood that the terms "system", "apparatus", "unit" and/or "module" as used herein are a means of distinguishing between different components, elements, parts, portions or assemblies at different levels. However, said words may be replaced by other expressions if they serve the same purpose.

As shown in this specification and claims, words such as "a", "an", "an" and/or "the" do not specifically refer to the singular and may include the plural unless the context clearly indicates an exception. Generally speaking, the terms "comprising" and "comprising" only imply the inclusion of clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or apparatus may also include other steps or elements.

Flowcharts are used in this specification to illustrate operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. At the same time, you can add other operations to these processes, or remove a step or steps from these processes.

At present, the high-throughput acquisition method of lithium-ion battery DC internal resistance in the existing technology is usually based on empirical estimation, lacks theoretical foundation, and has very low accuracy. Secondly, the use of data algorithms such as electrochemical model simulation or machine learning can achieve certain results within a certain range. However, both methods rely on a large amount of measured data for model verification or algorithm learning, and the measurement cost increases significantly. The invention is based on the internal physical mechanism of the lithium-ion battery, and realizes the DC internal resistance test data under a small number of conditions, thereby obtaining the DC internal resistance under other conditions with high throughput. To this end, please refer to Figure 1. The present invention provides a high-throughput method for obtaining the DC internal resistance of a lithium-ion battery, which includes the following steps:

Step S100: Determine at least one target change condition from the preset experimental conditions, and use the remaining conditions as control variables;

Step S100 specifically includes:

Step S110: Based on the three preset experimental conditions of battery charge state, measurement current, and measurement time, select at least one of the three preset experimental conditions as the target change condition, and use the remaining conditions as the control variables; specifically, the target change condition can be It is any one of battery charge state, measurement current, measurement time or their combination. The target change condition is the factor that is expected to change in measuring the DC internal resistance, and the control variable is the factor that remains unchanged; when selecting the battery charge state Before measuring the three preset experimental conditions of current, measurement time and current, the condition range of the target lithium-ion battery DC internal resistance needs to be determined in advance. In this embodiment, in order to improve the measurement accuracy, the acquisition of DC internal resistance under different temperature conditions is not considered; Taking the three preset experimental conditions of battery charge state, measurement current, and measurement time as target change conditions as an example, it can be determined that the range of preset experimental conditions at the target temperature includes: battery charge state range , measuring current range , measurement time range/> ;

Step S200: Select a target calculation model based on the number of target changing conditions, and calculate the first DC internal resistance of the lithium-ion battery under different numerical combinations of different changing conditions; specifically, according to the number of target changing conditions, preset experimental conditions range to set a combination of multiple experimental condition values. All conditions in the experimental condition combination need to have numerical changes and cover the edges of the condition value range as much as possible. The same three changing conditions are battery charge state, measurement current, and measurement time. For example, the following 15 condition combinations can be set: ,/> , ,/> ,/> ,/> ,/> ,/> , ,/> ,/> ,/> ,/> ,/> , ;wherein,/> ,/> , ;The number of experimental condition combinations can be determined according to the actual situation. The more combinations, the more accurate the subsequent model calculation results will be;

Step S200 specifically includes:

Step S210: Select two conditions from the battery charge state, measurement current, and measurement time as control variables to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining changing conditions; for example, we can select the battery The charge state and measured current are used as control variables, the measurement time is used as the target change condition, and a variety of different condition combinations are also set;

Among them, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> DC internal resistance under;/> Indicates that the changing condition is the measured current/> DC internal resistance under;/> Indicates that the change condition is the measurement time/> DC internal resistance under;/> are all model parameters.

Step S220: Select a condition from the battery charge state, measurement current, and measurement time as a control variable to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions; for example, select the battery charge state As control variables, different measurement current and measurement time values can be set, and different numerical combination conditions can be generated;

Among them, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> and measuring current/> DC internal resistance below;

In the formula, Indicates that the changing condition is the battery charge state/> and measurement time/> DC internal resistance below;

In the formula, Indicates that the changing condition is the measured current/> and measurement time/> DC internal resistance under;/> are all model parameters;

Step S230: The three conditions of battery charge state, measurement current, and measurement time are all used as changing conditions to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the three changing conditions; that is, perform different numerical values on the three conditions changes in combination;

Among them, the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of three changing conditions is:

In the formula, Indicates that the changing condition is the battery charge state/> , measure current/> and measurement time/> DC internal resistance under,/> are all model parameters.

Step S300: Conduct experiments based on the target change conditions and control variables to obtain the second DC internal resistance of the lithium-ion battery under different numerical combinations of the target change conditions; specifically, select the battery charge state, measurement current, and measurement time based on the target change conditions. For example, in step S300, it is necessary to experimentally measure the second DC internal resistance of the lithium-ion battery under the above 15 different combinations of numerical conditions. Specifically, experimental data can be obtained through existing current step experimental measurements;

Step S400: Optimize the parameters of the target calculation model based on the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery to obtain the optimal parameters of the target calculation model; the purpose is to make the optimized parameters more accurate. Consistent with actual experimental data;

Step S410: Calculate the root mean square error between the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery. The minimum root mean square error between the two parameters is taken as the goal, and the parameters of the target calculation model are used as the optimization object for optimization to obtain the optimal parameters of the target calculation model;

Specifically, for example, a genetic algorithm can be used to obtain the optimal parameters of the target calculation model, including:

Step S411: Generate initial population:

Preset the population number, number of iterations, mutation probability, and crossover probability, and generate the initial chromosome population (a possible solution) in binary coding. For example, the population number is 100, the number of iterations is 5000, the mutation probability is 0.05, and the crossover probability is 0.7;

Step S411: Heredity and mutation:

Calculate the fitness of each individual in the population according to the parameters of the target calculation model (that is, the degree of fit between the calculation model and the actual data), use the roulette method to screen individuals with high fitness according to the preset probability, and then select the high fitness individuals Among the individuals, the single-point crossover method and the single-point mutation method are used to generate new individuals according to the mutation probability, and generate a new population;

Step S411: Loop iteration:

Determine whether the maximum number of iterations has been reached. If the set number of iterations has not been reached, the individual fitness calculation and genetic variation will be iteratively performed in the new population; if the maximum number of iterations has been reached, the genetic code of the individual with the current maximum fitness will be output. , after decoding, the optimal parameter combination of the target calculation model is obtained; through genetic algorithm optimization, the parameter combination of the calculation model that is most suitable for the actual data can be found, thereby improving the fitting accuracy of the calculation model and the accuracy of calculating the DC internal resistance; it is required It should be noted that this embodiment provides a method for optimizing coefficients using a genetic algorithm, and optimization algorithms such as cuckoo algorithm, particle swarm algorithm, or other machine learning algorithms can also be used to optimize coefficients.

Step S500: Obtain the DC internal resistance under other target control changing conditions through high-throughput based on the optimal parameters of the target calculation model.

Step S500 specifically includes:

Step S510: Bring the optimal parameters into the target calculation model to obtain the optimized target calculation model; specifically, you can bring the optimal parameters into the above three different target calculation models respectively to obtain the optimized target calculation model. Accurately predict the DC internal resistance of lithium-ion batteries;

Step S520: Input any combination of changing conditions within the preset experimental conditions, and obtain the DC internal resistance of the lithium-ion battery within the full range of experimental conditions with high throughput; specifically, after obtaining the optimal coefficients of the target calculation model, for example, we obtain After calculating the optimal coefficients of the model under full variation conditions, the battery charge state range can be , measuring current range/> , measurement time range/> The condition value of any combination of conditions within can quickly calculate the DC internal resistance of the lithium-ion battery, achieving high-throughput acquisition of the DC internal resistance within the full range of conditions; if you need to obtain the DC internal resistance under other target changing conditions, you only need to set the corresponding Control variables can be used to obtain high-throughput DC internal resistance within the entire experimental range under different changing conditions.

In summary, the high-throughput acquisition method of lithium-ion battery DC internal resistance provided in this application achieves high-throughput acquisition of other untested values or untested target control changes through DC internal resistance data under a small amount of changing conditions. The DC internal resistance data under conditions can quickly and high-throughput obtain the DC internal resistance of lithium-ion batteries within the full range of experimental conditions, and the DC internal resistance under any combination of changing conditions can be quickly calculated, saving experiments and time costs; in addition, On the one hand, the calculation model conforms to the internal physical mechanism of lithium-ion batteries, achieving high-throughput acquisition of DC internal resistance of lithium-ion batteries with high precision and simple operation; compared with the high-pass DC internal resistance of lithium-ion batteries in the existing technology, The quantity acquisition method is usually based on empirical estimation, which solves the problems of lack of theoretical foundation and low accuracy; compared with the existing technology that uses data algorithms such as electrochemical model simulation or machine learning, there is no need to rely on a large amount of measured data to perform Model verification or algorithm learning provides important support for lithium-ion battery performance evaluation and optimized design.

The basic concepts have been described above. It is obvious to those skilled in the art that the above detailed disclosure is only an example and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification, and therefore such modifications, improvements, and corrections remain within the spirit and scope of the exemplary embodiments of this specification.

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Accordingly, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to those expressly introduced and described in this specification.

Claims (8)

1. A high-throughput acquisition method for DC internal resistance of lithium-ion batteries, which is characterized by including the following steps: Determine at least one target change condition from the preset experimental conditions, and use the remaining conditions as control variables; Select the target calculation model based on the number of target changing conditions to calculate the first DC internal resistance of the lithium-ion battery under different changing conditions under different numerical combinations, including: selecting two conditions from the battery charge state, measurement current, and measurement time as control Variable, obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining changing conditions. The calculation formula is: In the formula, Indicates that the changing condition is the battery charge state/> DC internal resistance under;/> Indicates that the changing condition is the measured current/> DC internal resistance under;/> Indicates that the change condition is the measurement time/> DC internal resistance under;/> are all model parameters; Conduct experiments based on target change conditions and control variables to obtain the second DC internal resistance of the lithium-ion battery under different numerical combinations of target change conditions; Based on the first DC internal resistance of the lithium-ion battery and the second DC internal resistance of the lithium-ion battery, the parameters of the target calculation model are optimized to obtain the optimal parameters of the target calculation model; Based on the optimal parameters of the target calculation model, the DC internal resistance under other values or other target control changing conditions is obtained through high-throughput. 2. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to claim 1, characterized in that at least one target change condition is determined from the preset experimental conditions, and the remaining conditions are used as control variables, including: Based on three preset experimental conditions: battery charge state, measurement current, and measurement time, at least one of the three preset experimental conditions is selected as the target change condition, and the remaining conditions are used as control variables. 3. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to claim 2, characterized in that the target calculation model is selected based on the number of target changing conditions, and the lithium-ion battery's number of different changing conditions under different numerical combinations is calculated. DC internal resistance also includes: Select one condition from the battery charge state, measurement current, and measurement time as the control variable to obtain the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions. 4. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to claim 3, characterized in that the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of the remaining two changing conditions is: : In the formula, Indicates that the changing condition is the battery charge state/> and measuring current/> DC internal resistance below; In the formula, Indicates that the changing condition is the battery charge state/> and measurement time/> DC internal resistance below; In the formula, Indicates that the changing condition is the measured current/> and measurement time/> DC internal resistance under;/> are all model parameters. 5. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to claim 2, characterized in that the target calculation model is selected based on the number of target changing conditions, and the lithium-ion battery's number of different changing conditions under different numerical combinations is calculated. DC internal resistance also includes: The three conditions of battery charge state, measurement current, and measurement time are all used as changing conditions, and the first DC internal resistance of the lithium-ion battery under different numerical combinations of the three changing conditions is obtained. 6. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to claim 5, characterized in that the calculation formula for obtaining the first DC internal resistance of the lithium-ion battery under different numerical combinations of three changing conditions is: In the formula, Indicates that the changing condition is the battery charge state/> , measure current/> and measurement time/> DC internal resistance under,/> are all model parameters. 7. The high-throughput acquisition method of lithium-ion battery DC internal resistance according to any one of claims 1 to 6, characterized in that, based on the first DC internal resistance of the lithium-ion battery and the second DC internal resistance of the lithium-ion battery, The parameters of the target calculation model are optimized to obtain the optimal parameters, including: Calculate the root mean square error between the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery, and use the minimum value between the first DC internal resistance of the lithium ion battery and the second DC internal resistance of the lithium ion battery. The root mean square error is taken as the target, and the parameters of the target calculation model are used as the optimization object for optimization to obtain the optimal parameters of the target calculation model. 8. The high-throughput acquisition method of DC internal resistance of lithium-ion battery according to claim 7, characterized in that the high-throughput acquisition of DC internal resistance under other target control changing conditions based on the optimal parameters of the target calculation model includes: Bring the optimal parameters into the target calculation model to obtain the optimized target calculation model; Enter the numerical combination of any changing conditions within the preset experimental conditions, and obtain the DC internal resistance of the lithium-ion battery within the full range of experimental conditions with high throughput.