New energy automobile power battery operation performance detection method

Abstract

The invention discloses a new energy automobile power battery operation performance detection method, and relates to the technical field of battery monitoring, and the method comprises the following steps: S1, collecting the dynamic temperature value of each cell in a power battery, classifying all cells, and obtaining the cell transmission value of the power battery; s2, according to the cell transfer value of the power battery, constructing a heat transfer influence function between each cell and the tab; s3, collecting a dynamic temperature value of the surface of the power battery, and determining a future temperature value of the surface of the power battery by using the heat transfer influence function between each battery cell and the tab; and S4, when the future temperature value exceeds the preset temperature of the power battery, determining that the performance of the power battery is abnormal. The future temperature value of the surface of the power battery can be accurately predicted, the potential overheating risk is found in advance, battery damage or safety accidents are avoided, and the signs of performance reduction or faults of the battery are found in time.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and in particular to a method for detecting the operating performance of a power battery for a new energy vehicle. Background Art

[0002] In recent years, with the rapid development of the new energy industry, people's perception of new energy vehicles has changed, and new energy vehicles have become increasingly recognized and favored by the general public; with the large-scale expansion of the electric vehicle industry, safety issues have become an issue that the industry urgently needs to solve.

[0003] During the operation of the power battery of new energy vehicles, the temperature distribution and changes of its internal cells are extremely complex. Due to the heat conduction between cells, the heat exchange between cells and tabs, and the influence of the external environment, the overall temperature distribution of the power battery presents highly dynamic and nonlinear characteristics. This complex thermal behavior not only affects the energy conversion efficiency of the battery, but may also cause safety issues such as battery thermal runaway and thermal abuse, which in turn has a serious impact on the performance and life of the power battery.

[0004] Traditional power battery performance testing methods mostly rely on real-time temperature measurement of sensors and are unable to analyze temperature data. They have obvious deficiencies in predicting future temperature trends of power batteries, promptly detecting performance anomalies, and preventing potential safety risks. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method for detecting the operating performance of a power battery of a new energy vehicle.

[0006] The technical solution of the present invention is: a method for detecting the operating performance of a power battery of a new energy vehicle comprises the following steps:

[0007] S1. Collect the dynamic temperature value of each battery cell in the power battery, classify all the battery cells, and obtain the battery cell transfer value of the power battery;

[0008] S2. According to the cell transfer value of the power battery, a heat transfer influence function is constructed between each cell and the tab;

[0009] S3. Collect the dynamic temperature value of the power battery surface, and determine the future temperature value of the power battery surface by using the heat transfer influence function between each battery cell and the tab;

[0010] S4. When the future temperature value exceeds a preset temperature of the power battery, it is determined that the power battery performance is abnormal.

[0011] Furthermore, S1 includes the following sub-steps:

[0012] S11, collecting the dynamic temperature value of each cell in the power battery, and calculating the spread entropy of all dynamic temperature values;

[0013] S12, splitting the dynamic temperature values ​​of all battery cells into two sample sets with the same sample size, and using the two sample sets to build a support vector machine model;

[0014] S13, using a support vector machine model to classify all battery cells to obtain a first battery cell set, a second battery cell set, and a third battery cell set;

[0015] S14. Based on the first battery cell set, the second battery cell set, and the third battery cell set, calculate the battery cell transfer value of the power battery using the distributed entropy.

[0016] The beneficial effect of the above further scheme is that in the present invention, each cell in the power battery has a dynamically changing temperature value at each moment, and by calculating the spread entropy of all dynamic temperature values, the complexity and unevenness of the cell temperature distribution can be quantified. Spread entropy is an indicator to measure the degree of data distribution disorder. Based on the classified cell set, the spread entropy is used to calculate the cell transfer value of the power battery, which can comprehensively consider the temperature distribution characteristics of the cell, avoid the defect of simply averaging all cells as a whole, and improve the accuracy of the cell transfer value calculation.

[0017] In S12, if the number of dynamic temperature values ​​is an odd number, a dynamic temperature value is randomly discarded.

[0018] Furthermore, in S12, the kernel function of the support vector machine model is The expression is:

[0019] ;

[0020] In the formula, represents the index, represents the mean of all dynamic temperature values ​​in the first sample set, represents the mean of all dynamic temperature values ​​in the second sample set, Represents the bandwidth of the Gaussian radial basis kernel function of the support vector machine model.

[0021] The beneficial effect of the above further solution is: in the present invention, due to the use of the mean and , reducing the computational complexity and making the model more efficient during the training phase. Bandwidth parameter Adjustable, so that the model can be optimized according to the characteristics of the data and improve the classification accuracy.

[0022] Furthermore, in S14, the cell transfer value of the power battery The calculation formula is:

[0023] ;

[0024] In the formula, Represents the spread entropy of the dynamic temperature values ​​of all cells, Indicates the first battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the first cell set, Indicates the second battery cell concentration The first The dynamic temperature value at a moment, Indicates the number of cells in the second cell set, Indicates the second battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the third cell set, Indicates the mean symbol, Indicates the minimum value symbol, Indicates the maximum value symbol.

[0025] Furthermore, in S2, the heat transfer between the cell and the tab affects the function The expression is:

[0026] ;

[0027] In the formula, Indicates the cell transfer value of the power battery. represents the reaction activation energy of the power battery, Indicates the rated capacity of the power battery. The fundamental constant about temperature is Indicates the melting point of the tab material, represents a constant, Indicates the electric field non-uniformity coefficient of the power battery.

[0028] The basic constant regarding temperature is the Boltzmann constant.

[0029] Furthermore, S3 includes the following sub-steps:

[0030] S31. Constructing a transient dynamic matrix and a global dynamic matrix of the power battery according to the dynamic temperature values ​​of the power battery surface at each moment;

[0031] S32. Calculate the global loss value of the power battery according to the transient dynamic matrix and the global dynamic matrix of the power battery;

[0032] S33. Determine the future temperature value of the power battery surface according to the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery.

[0033] The beneficial effect of the above further scheme is: in the present invention, by constructing the transient dynamic matrix and the global dynamic matrix of the power battery, the dynamic temperature value changes of the power battery surface at each moment can be comprehensively analyzed. The matrix construction method not only considers the maximum and minimum values ​​of the temperature value, but also considers the average value of the temperature change, thereby more accurately reflecting the thermal behavior characteristics of the power battery. By calculating the global loss value of the power battery, the overall performance of the power battery in terms of thermal behavior is quantified, and the information of the transient dynamic matrix and the global dynamic matrix is ​​comprehensively considered, reflecting the energy situation of the power battery during the temperature change process.

[0034] By combining the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery, the future temperature value of the power battery surface can be predicted. Combining the heat transfer influence between the battery cell and the tab and the overall performance of the power battery in thermal behavior, the accuracy of the prediction can be improved.

[0035] Furthermore, in S31, the transient dynamic matrix of the power battery The expression is:

[0036] ;

[0037] In the formula, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates the minimum value symbol, Indicates the maximum value symbol;

[0038] In S31, the global dynamic matrix of the power battery The expression is:

[0039] ;

[0040] In the formula, Indicates the mean symbol, Represents all the time numbers.

[0041] Furthermore, in S32, the global loss value of the power battery The calculation formula is:

[0042] ;

[0043] In the formula, represents the transient dynamic matrix of the power battery, represents the global dynamic matrix of the power battery, Represents all the time numbers, Indicates the logarithmic sign.

[0044] Furthermore, in S33, the future temperature value of the power battery surface The calculation formula is:

[0045] ;

[0046] In the formula, Represents the global loss value of the power battery, Represents the mean value of the heat transfer influence function between all battery cells and the tabs.

[0047] The beneficial effects of the present invention are as follows: the present invention discloses a method for detecting the operating performance of a power battery for a new energy vehicle, and establishes a personalized influence function for the heat transfer between each battery cell and the tab according to the battery cell transfer value of the power battery, and comprehensively considers the complexity and diversity of the heat transfer between the battery cell and the tab, and an accurate heat transfer influence function provides a basis for optimizing the monitoring of the power battery; then, the present invention utilizes the heat transfer influence function between each battery cell and the tab, combined with the dynamic temperature value of the power battery surface, to accurately predict the future temperature value of the power battery surface, discover potential overheating risks in advance, avoid battery damage or safety accidents, and promptly discover signs of battery performance degradation or failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a flow chart of the method for testing the operating performance of power batteries for new energy vehicles. DETAILED DESCRIPTION

[0049] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0050] like Figure 1 As shown, the present invention provides a method for detecting the operating performance of a power battery of a new energy vehicle, comprising the following steps:

[0051] S1. Collect the dynamic temperature value of each battery cell in the power battery, classify all the battery cells, and obtain the battery cell transfer value of the power battery;

[0052] S2. According to the cell transfer value of the power battery, a heat transfer influence function is constructed between each cell and the tab;

[0053] S3. Collect the dynamic temperature value of the power battery surface, and determine the future temperature value of the power battery surface by using the heat transfer influence function between each battery cell and the tab;

[0054] S4. When the future temperature value exceeds a preset temperature of the power battery, it is determined that the power battery performance is abnormal.

[0055] In this embodiment of the present invention, S1 includes the following sub-steps:

[0056] S11, collecting the dynamic temperature value of each cell in the power battery, and calculating the spread entropy of all dynamic temperature values;

[0057] S12, splitting the dynamic temperature values ​​of all battery cells into two sample sets with the same sample size, and using the two sample sets to build a support vector machine model;

[0058] S13, using a support vector machine model to classify all battery cells to obtain a first battery cell set, a second battery cell set, and a third battery cell set;

[0059] S14. Based on the first battery cell set, the second battery cell set, and the third battery cell set, calculate the battery cell transfer value of the power battery using the distributed entropy.

[0060] In the present invention, each cell in the power battery has a dynamically changing temperature value at each moment. By calculating the spread entropy of all dynamic temperature values, the complexity and unevenness of the cell temperature distribution can be quantified. Spread entropy is an indicator to measure the degree of data distribution disorder. Based on the classified cell set, the spread entropy is used to calculate the cell transfer value of the power battery, which can comprehensively consider the temperature distribution characteristics of the cell, avoid the defect of simply averaging all cells as a whole, and improve the accuracy of the cell transfer value calculation.

[0061] The first battery cell set may be a low-temperature battery cell set, the second battery cell set may be a medium-temperature battery cell set, and the third battery cell set may be a high-temperature battery cell set.

[0062] In S12, if the number of dynamic temperature values ​​is an odd number, a dynamic temperature value is randomly discarded.

[0063] In the embodiment of the present invention, in S12, the kernel function of the support vector machine model The expression is:

[0064] ;

[0065] In the formula, represents the index, represents the mean of all dynamic temperature values ​​in the first sample set, represents the mean of all dynamic temperature values ​​in the second sample set, Represents the bandwidth of the Gaussian radial basis kernel function of the support vector machine model.

[0066] In the present invention, due to the use of the mean and , reducing the computational complexity and making the model more efficient during the training phase. Bandwidth parameter Adjustable, so that the model can be optimized according to the characteristics of the data and improve the classification accuracy.

[0067] In the embodiment of the present invention, in S14, the cell transfer value of the power battery The calculation formula is:

[0068] ;

[0069] In the formula, Represents the spread entropy of the dynamic temperature values ​​of all cells, Indicates the first battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the first cell set, Indicates the second battery cell concentration The first The dynamic temperature value at a moment, Indicates the number of cells in the second cell set, Indicates the second battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the third cell set, Indicates the mean symbol, Indicates the minimum value symbol, Indicates the maximum value symbol.

[0070] In the embodiment of the present invention, in S2, the heat transfer between the battery cell and the tab affects the function The expression is:

[0071] ;

[0072] In the formula, Indicates the cell transfer value of the power battery. represents the reaction activation energy of the power battery, Indicates the rated capacity of the power battery. The fundamental constant about temperature is Indicates the melting point of the tab material, represents a constant, Indicates the electric field non-uniformity coefficient of the power battery.

[0073] The basic constant regarding temperature is the Boltzmann constant.

[0074] In this embodiment of the present invention, S3 includes the following sub-steps:

[0075] S31. Constructing a transient dynamic matrix and a global dynamic matrix of the power battery according to the dynamic temperature values ​​of the power battery surface at each moment;

[0076] S32. Calculate the global loss value of the power battery according to the transient dynamic matrix and the global dynamic matrix of the power battery;

[0077] S33. Determine the future temperature value of the power battery surface according to the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery.

[0078] In the present invention, by constructing the transient dynamic matrix and the global dynamic matrix of the power battery, the dynamic temperature value changes of the power battery surface at each moment can be comprehensively analyzed. The matrix construction method not only considers the maximum and minimum values ​​of the temperature value, but also considers the average value of the temperature change, thereby more accurately reflecting the thermal behavior characteristics of the power battery. By calculating the global loss value of the power battery, the overall performance of the power battery in terms of thermal behavior is quantified, and the information of the transient dynamic matrix and the global dynamic matrix is ​​comprehensively considered to reflect the energy situation of the power battery during the temperature change process.

[0079] By combining the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery, the future temperature value of the power battery surface can be predicted. Combining the heat transfer influence between the battery cell and the tab and the overall performance of the power battery in thermal behavior, the accuracy of the prediction can be improved.

[0080] In the embodiment of the present invention, in S31, the transient dynamic matrix of the power battery The expression is:

[0081] ;

[0082] In the formula, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates the minimum value symbol, Indicates the maximum value symbol;

[0083] In S31, the global dynamic matrix of the power battery The expression is:

[0084] ;

[0085] In the formula, Indicates the mean symbol, Represents all the time numbers.

[0086] In the embodiment of the present invention, in S32, the global loss value of the power battery The calculation formula is:

[0087] ;

[0088] In the formula, represents the transient dynamic matrix of the power battery, represents the global dynamic matrix of the power battery, Represents all the time numbers, Indicates the logarithmic sign.

[0089] In the embodiment of the present invention, in S33, the future temperature value of the power battery surface The calculation formula is:

[0090] ;

[0091] In the formula, Represents the global loss value of the power battery, Represents the mean value of the heat transfer influence function between all battery cells and the tabs.

[0092] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for detecting the operating performance of a power battery of a new energy vehicle, characterized in that: The following steps are involved: S1. Collect the dynamic temperature value of each battery cell in the power battery, classify all the battery cells, and obtain the battery cell transfer value of the power battery; S2. According to the cell transfer value of the power battery, a heat transfer influence function is constructed between each cell and the tab; S3. Collect the dynamic temperature value of the power battery surface, and determine the future temperature value of the power battery surface by using the heat transfer influence function between each battery cell and the tab; S4. When the future temperature value exceeds a preset temperature of the power battery, it is determined that the power battery performance is abnormal.

2. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 1, characterized in that: The S1 comprises the following sub-steps: S11, collecting the dynamic temperature value of each cell in the power battery, and calculating the spread entropy of all dynamic temperature values; S12, splitting the dynamic temperature values ​​of all battery cells into two sample sets with the same sample size, and using the two sample sets to build a support vector machine model; S13, using a support vector machine model to classify all battery cells to obtain a first battery cell set, a second battery cell set, and a third battery cell set; S14. Based on the first battery cell set, the second battery cell set, and the third battery cell set, calculate the battery cell transfer value of the power battery using the distributed entropy.

3. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 2, characterized in that: In S12, the kernel function of the support vector machine model The expression is: ； In the formula, represents the index, represents the mean of all dynamic temperature values ​​in the first sample set, represents the mean of all dynamic temperature values ​​in the second sample set, Represents the bandwidth of the Gaussian radial basis kernel function of the support vector machine model.

4. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 2, characterized in that: In S14, the cell transfer value of the power battery The calculation formula is: ； In the formula, Represents the spread entropy of the dynamic temperature values ​​of all cells, Indicates the first battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the first cell set, Indicates the second battery cell concentration The first The dynamic temperature value at a moment, Indicates the number of cells in the second cell set, Indicates the second battery cell concentration The battery cell is The dynamic temperature value at a moment, Indicates the number of cells in the third cell set, Indicates the mean symbol, Indicates the minimum value symbol, Indicates the maximum value symbol.

5. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 1, characterized in that: In S2, the heat transfer between the battery cell and the tab affects the function The expression is: ； In the formula, represents the index, Indicates the cell transfer value of the power battery. represents the reaction activation energy of the power battery, Indicates the rated capacity of the power battery. The fundamental constant about temperature is Indicates the melting point of the tab material, represents a constant, Indicates the electric field non-uniformity coefficient of the power battery.

6. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31. Constructing a transient dynamic matrix and a global dynamic matrix of the power battery according to the dynamic temperature values ​​of the power battery surface at each moment; S32. Calculate the global loss value of the power battery according to the transient dynamic matrix and the global dynamic matrix of the power battery; S33. Determine the future temperature value of the power battery surface according to the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery.

7. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 6, characterized in that: In S31, the transient dynamic matrix of the power battery The expression is: ； In the formula, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates that the surface of the power battery is The dynamic temperature value at a moment, Indicates the minimum value symbol, Indicates the maximum value symbol; In S31, the global dynamic matrix of the power battery The expression is: ； In the formula, Indicates the mean symbol, Represents all the time numbers.

8. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 6, characterized in that: In S32, the global loss value of the power battery The calculation formula is: ； In the formula, represents the transient dynamic matrix of the power battery, represents the global dynamic matrix of the power battery, Represents all the time numbers, Indicates the logarithmic sign.

9. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 6, characterized in that: In S33, the future temperature value of the power battery surface The calculation formula is: ； In the formula, Represents the global loss value of the power battery, Represents the mean value of the heat transfer influence function between all battery cells and the tabs.

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