A method for detecting the operating performance of power batteries for new energy vehicles

Abstract

The present invention discloses a method for detecting the operating performance of a power battery of a new energy vehicle, which relates to the technical field of battery monitoring and includes the following steps: S1, collecting the dynamic temperature values of each battery cell in the power battery and classifying all the battery cells to obtain the cell transfer value of the power battery; S2, constructing a heat transfer influence function between each battery cell and the tab according to the cell transfer value of the power battery; S3, collecting the dynamic temperature value on the surface of the power battery and determining the future temperature value on the surface of the power battery by using the heat transfer influence function between each battery cell and the tab; 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 present invention can accurately predict the future temperature value on the surface of the power battery, discover potential overheating risks in advance, avoid battery damage or safety accidents, and timely detect signs of battery performance degradation or faults.

Description

Technical Field

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

[0002] In recent years, with the rapid development of the new energy industry, people's understanding of new energy vehicles has changed, and new energy vehicles have been increasingly recognized and favored by the general public; with the large-scale expansion of the electric vehicle industry, safety issues have become problems that need to be solved urgently in the industry.

[0003] During the operation of the power battery of a new energy vehicle, the temperature distribution and change of the internal battery cells are extremely complex. Due to the heat conduction between the battery cells, the heat exchange between the battery cells and the tabs, and the influence of the external environment, the overall temperature distribution of the power battery shows highly dynamic and non-linear characteristics. This complex thermal behavior not only affects the energy conversion efficiency of the battery, but also may cause safety problems such as battery thermal runaway and thermal abuse, thereby seriously affecting the performance and lifespan of the power battery.

[0004] Most traditional methods for detecting the performance of power batteries rely on real-time temperature measurement by sensors and cannot analyze temperature data, showing obvious deficiencies in predicting the future temperature trend of power batteries, timely 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 power batteries for new energy vehicles.

[0006] The technical solution of the present invention is: a method for detecting the operating performance of power batteries for new energy vehicles includes the following steps:

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

[0008] S2. Construct a heat transfer influence function between each battery cell and the tab according to the cell transfer value of the power battery;

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

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

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

[0012] S11. Collect the dynamic temperature values of each battery cell in the power battery, and calculate the dispersion entropy of all the dynamic temperature values;

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

[0014] S13. Classify all the battery cells using the support vector machine model 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 cell transfer value of the power battery using the dispersion entropy.

[0016] The beneficial effect of the above further solution is as follows: In the present invention, each battery cell in the power battery has a dynamically changing temperature value at each moment. By calculating the dispersion entropy of all the dynamic temperature values, the complexity and non-uniformity of the cell temperature distribution can be quantified. The dispersion entropy is an index for measuring the scatter degree of data distribution. Based on the classified battery cell sets, calculating the cell transfer value of the power battery using the dispersion entropy can comprehensively consider the temperature distribution characteristics of the battery cells, avoid the defect of simply averaging all the battery cells as a whole, and improve the accuracy of calculating the cell transfer value.

[0017] In S12, if the number of dynamic temperature values is odd, randomly discard one dynamic temperature value.

[0018] Further, in S12, the kernel function of the support vector machine model has the following expression:

[0019] ;

[0020] In the formula, represents the exponent, represents the mean value of all the dynamic temperature values in the first sample set, represents the mean value of all the 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 as follows: In the present invention, due to the use of the mean values and , the computational complexity is reduced, making the model more efficient in the training stage. The bandwidth parameter is adjustable, enabling the model to be optimized according to the data characteristics and improving the classification accuracy.

[0022] Further, in S14, the calculation formula for the cell transfer value of the power battery is as follows:

[0023] ;

[0024] Wherein, represents the dispersion entropy of the dynamic temperature values of all battery cells, represents the th battery cell in the first battery cell set at the th moment, represents the number of battery cells in the first battery cell set, represents the th battery cell in the second battery cell set at the th moment, represents the number of battery cells in the second battery cell set, represents the th battery cell in the second battery cell set at the th moment, represents the number of battery cells in the third battery cell set, represents the symbol for taking the mean value, represents the symbol for taking the minimum value, represents the symbol for taking the maximum value.

[0025] Furthermore, in S2, the heat transfer influence function between the battery cell and the tab has the following expression:

[0026] ;

[0027] Wherein, represents the cell transfer value of the power battery, represents the reaction activation energy of the power battery, represents the rated capacity of the power battery, is the basic constant with respect to temperature, represents the melting point of the tab material, represents a constant, represents the electric field non-uniformity coefficient of the power battery.

[0028] The basic constant with respect to temperature is the Boltzmann constant.

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

[0030] S31. Construct the transient dynamic matrix and the 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 on the surface of the power battery 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 effects of the above further solution are as follows: In the present invention, by constructing the transient dynamic matrix and the global dynamic matrix of the power battery, it is possible to comprehensively analyze the variation of the dynamic temperature value on the surface of the power battery at each moment. 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, thus 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, comprehensively considering the information of the transient dynamic matrix and the global dynamic matrix, and 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, it is possible to predict the future temperature value on the surface of the power battery, combining the heat transfer between the battery cell and the tab and the overall performance of the power battery in terms of thermal behavior, and improving the accuracy of the prediction.

[0035] Further, in S31, the transient dynamic matrix of the power battery has the following expression:

[0036] ;

[0037] In the formula, represents the dynamic temperature value on the surface of the power battery at the th moment, represents the dynamic temperature value on the surface of the power battery at the th moment, represents the symbol for taking the minimum value, represents the symbol for taking the maximum value;

[0038] In S31, the global dynamic matrix of the power battery has the following expression:

[0039] ;

[0040] In the formula, represents the symbol for taking the average value, represents the total number of moments.

[0041] Further, in S32, the calculation formula for the global loss value of the power battery 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 number of time instants, represents the logarithm symbol.

[0044] Further, in S33, the future temperature value on the surface of the power battery is calculated by the formula:

[0045] ;

[0046] In the formula, represents the global loss value of the power battery, represents the average value of the heat transfer influence function values between all the 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 of a new energy vehicle. According to the cell transfer value of the power battery, a personalized influence function is established for the heat transfer between each cell and the tab. Considering the complexity and diversity of the heat transfer between the cell and the tab, the accurate heat transfer influence function provides a basis for optimizing the monitoring of the power battery. Then, by using the heat transfer influence function between each cell and the tab and combining with the dynamic temperature value on the surface of the power battery, the present invention can accurately predict the future temperature value on the surface of the power battery, discover potential overheating risks in advance, avoid battery damage or safety accidents, and timely detect signs of battery performance degradation or faults. Description of the Drawings

[0048] Figure 1 is a flowchart of the method for detecting the operating performance of a power battery of a new energy vehicle. Detailed Embodiments

[0049] The embodiments of the present invention will be further described below with reference to the drawings.

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

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

[0052] S2. Construct a heat transfer influence function for each cell and the tab according to the cell transfer value of the power battery;

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

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

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

[0056] S11. Collect the dynamic temperature values of each cell in the power battery and calculate the dispersion entropy of all dynamic temperature values;

[0057] S12. Split the dynamic temperature values of all cells into two sample sets with the same sample size and construct a support vector machine model using the two sample sets;

[0058] S13. Classify all cells using the support vector machine model to obtain the first cell set, the second cell set, and the third cell set;

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

[0060] In the present invention, each cell in the power battery has a dynamically changing temperature value at each moment. By calculating the dispersion entropy of all dynamic temperature values, the complexity and non-uniformity of the cell temperature distribution can be quantified. The dispersion entropy is an index that measures the degree of scatter of data distribution. Based on the classified cell sets, calculating the cell transfer value of the power battery using the dispersion entropy can comprehensively consider the temperature distribution characteristics of the cells, avoiding the defect of simply averaging all cells as a whole and improving the accuracy of calculating the cell transfer value.

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

[0062] In S12, if the number of dynamic temperature values is odd, randomly discard one dynamic temperature value.

[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 exponent, Represents the mean value of all dynamic temperature values in the first sample set, Represents the mean value 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 value And , the computational complexity is reduced, making the model more efficient during the training phase. The bandwidth parameter is adjustable, enabling the model to be optimized according to data characteristics and improving the classification accuracy.

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

[0068] ;

[0069] In the formula, represents the dispersion entropy of the dynamic temperature values of all cells, represents the dynamic temperature value of the -th cell in the first cell set at the -th moment, represents the number of cells in the first cell set, represents the dynamic temperature value of the -th cell in the second cell set at the -th moment, represents the number of cells in the second cell set, represents the dynamic temperature value of the -th cell in the second cell set at the -th moment, represents the number of cells in the third cell set, represents the symbol for taking the mean, represents the symbol for taking the minimum value, represents the symbol for taking the maximum value.

[0070] In the embodiment of the present invention, in S2, the heat transfer influence function between the cell and the tab is expressed as:

[0071] ;

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

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

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

[0075] S31. Construct 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, it is possible to comprehensively analyze the change of the dynamic temperature value of the power battery surface at each moment. 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.

[0079] By combining the heat transfer influence function between each battery cell and the tab and the global loss value of the power battery, it is possible to predict the future temperature value of the power battery surface, and by combining the heat transfer influence between the battery cell and the tab and the overall performance of the power battery in terms of thermal behavior, the prediction accuracy is improved.

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

[0081] ;

[0082] In the formula, represents the dynamic temperature value of the power battery surface at the th moment, represents the dynamic temperature value of the power battery surface at the th moment, represents the symbol for taking the minimum value, represents the symbol for taking the maximum value;

[0083] In S31, the global dynamic matrix of the power battery has the following expression:

[0084] ;

[0085] In the formula, represents the symbol for taking the average value, represents the number of all moments.

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

[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 number of moments, represents the logarithm symbol.

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

[0090] ;

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

[0092] Those of ordinary skill in the art will realize that the embodiments described herein are to assist the reader in understanding the principles of the present invention and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations without departing from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection 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 the preset temperature of the power battery, determining that the power battery performance is abnormal; 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, calculating the cell transfer value of the power battery using the spread entropy based on the first cell set, the second cell set, and the third cell set; 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; 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.

2. 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 the Boltzmann constant, Indicates the melting point of the tab material. represents a constant, Indicates the electric field non-uniformity coefficient of the power battery.

3. 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.

4. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 3, 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.

5. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 3, 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.

6. The method for detecting the operating performance of a power battery for a new energy vehicle according to claim 3, 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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