Electric vehicle battery management method

Abstract

This invention discloses a battery management method for electric vehicles, comprising the following steps: A. presetting the initial capacity and initial constant-current discharge curve of the power battery; recording the real-time discharge curve during the power battery discharge process; B. marking approximate constant-current discharge intervals on the real-time discharge curve and then standardizing the marked curve segments; C. generating a battery state matrix; D. processing and converting the battery state matrix; E. predicting the current actual maximum capacity and state of charge (SOC) of the power battery based on the battery state matrix; and F. determining the current charge and discharge strategy based on the most recently predicted actual maximum capacity and state of charge (SOC) of the power battery. This invention can overcome the shortcomings of existing technologies and improve the accuracy of predicting the actual maximum capacity and state of charge (SOC) of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle battery management, and in particular to an electric vehicle battery management method. Background Art

[0002] In recent years, my country's electric vehicle market has experienced rapid growth. The power batteries in electric vehicles gradually degrade with use. To accurately determine the battery's current charge level, it's necessary to predict the battery's current maximum capacity and SOC. This is particularly true for lithium iron phosphate batteries, where the relationship between battery voltage and battery charge level is highly nonlinear during use, leading to inaccurate predictions of the battery's maximum capacity and SOC. Chinese invention patent CN103513188A discloses a battery charge calculation method. However, this method still fails to address the issue of inaccurate charge predictions for lithium iron phosphate batteries after multiple charge and discharge cycles, requiring repeated full charge and discharge experiments for calibration. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an electric vehicle battery management method that can address the deficiencies of the existing technology and improve the accuracy of prediction of the actual maximum capacity and SOC of the battery.

[0004] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A method for managing an electric vehicle battery comprises the following steps:

[0006] A. Preset the initial capacity and initial constant current discharge curve of the power battery; during the power battery discharge process, record the real-time discharge curve;

[0007] B. Mark the approximate constant current discharge interval on the real-time discharge curve, and then standardize the marked curve segment;

[0008] C. Select several sampling points in the standardized constant current discharge interval, use the time corresponding to the sampling points in the constant current discharge interval to select corresponding sampling points in the initial constant current discharge curve, form the two groups of sampling points into sampling vectors, and then perform the outer product operation on the two sampling vectors to obtain the battery state matrix;

[0009] D. Process and transform the battery state matrix;

[0010] E. Predict the actual maximum capacity and SOC of the power battery based on the battery state matrix;

[0011] F. Determine the current charge and discharge strategy based on the most recently predicted actual maximum capacity and SOC of the power battery.

[0012] Preferably, in step B, standardizing the marked curve segment comprises the following steps:

[0013] Calculate the average discharge current in the marked time period, then calculate the standard deviation between the actual discharge current and the average discharge current. Lengthen the time axis of the interval in the marked curve segment where the actual discharge current is less than the average discharge current, and shorten the time axis of the interval in the marked curve segment where the actual discharge current is greater than the average discharge current. The ratio of lengthening or shortening the time axis is proportional to the standard deviation calculated above.

[0014] Preferably, in step C, at least 30% of the sampling points in the standardized constant current discharge interval are located in the interval whose time axis has been adjusted.

[0015] Preferably, in step D, the battery state matrix is ​​projected onto the time axis to obtain a projection curve, the projection curve is divided at its singular point, and then the divided projection curve segments are linearly transformed to make the module lengths of the projection curve segments equal, and then back-projected to restore it to the battery state matrix, and constraint factors are added to the elements that have changed in the battery state matrix.

[0016] Preferably, the rank of the constraint matrix composed of constraint factors is smaller than the rank of the battery state matrix.

[0017] Preferably, in step E, elements in the battery state matrix are extracted according to the time sequence, and a state curve is fitted. The state curve is compared with the initial constant current discharge curve, and the average deviation ratio is the deviation ratio of the current actual maximum capacity of the power battery to the initial capacity. Then, the current SOC is obtained based on the current actual maximum capacity and discharge amount.

[0018] The beneficial effect brought about by adopting the above technical solution is that the present invention utilizes the battery's own charging and discharging process during use to automatically calibrate the capacity and SOC, thereby reducing the interference caused by the different states of different battery cells on the calibration and improving the calibration accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0020] Reference Figure 1 , a specific embodiment of the present invention includes the following steps:

[0021] A. Preset the initial capacity and initial constant current discharge curve of the power battery; during the power battery discharge process, record the real-time discharge curve;

[0022] B. Mark the approximate constant current discharge interval on the real-time discharge curve, and then standardize the marked curve segment;

[0023] C. Select several sampling points in the standardized constant current discharge interval, use the time corresponding to the sampling points in the constant current discharge interval to select corresponding sampling points in the initial constant current discharge curve, form the two groups of sampling points into sampling vectors, and then perform the outer product operation on the two sampling vectors to obtain the battery state matrix;

[0024] D. Process and transform the battery state matrix;

[0025] E. Predict the actual maximum capacity and SOC of the power battery based on the battery state matrix;

[0026] F. Determine the current charge and discharge strategy based on the most recently predicted actual maximum capacity and SOC of the power battery.

[0027] The present invention uses actual discharge curve data points and constant current discharge curve data points to establish a battery state matrix, and uses the battery state matrix to predict the current actual maximum capacity and SOC of the power battery. By processing and converting the battery state matrix, the interference caused by the different states of different battery cells on the calibration can be effectively reduced.

[0028] In step B, standardizing the marked curve segment includes the following steps: calculating the average discharge current within the marked time period, then calculating the standard deviation between the actual discharge current and the average discharge current, lengthening the time axis of the interval in the marked curve segment where the actual discharge current is less than the average discharge current, and shortening the time axis of the interval in the marked curve segment where the actual discharge current is greater than the average discharge current, with the ratio of time axis lengthening or shortening being proportional to the calculated standard deviation. In step C, at least 30% of the sampling points in the standardized constant current discharge interval are within the adjusted time axis interval.

[0029] Due to the inherent material characteristics of lithium iron phosphate batteries, the middle portion of their SOC-OCV curve is very flat, making it difficult to accurately calibrate the SOC value using the SOC-OCV curve. Furthermore, because the voltage changes of lithium iron phosphate batteries at different discharge rates are also nonlinear, direct prediction based on the actual discharge curve will also result in significant errors. By standardizing the marked curve segments, the linearity of the curve segments can be improved, thereby preventing interference in the prediction caused by differences in battery state at different discharge rates.

[0030] In step D, the battery state matrix is ​​projected onto the time axis to obtain a projection curve. The projection curve is then segmented at its singular point. A linear transformation is then performed on the segmented projection curve segments to make the moduli of the projection curve segments equal. Back-projection is then performed to restore the battery state matrix. Constraint factors are added to the changed elements in the battery state matrix. The rank of the constraint matrix composed of the constraint factors is smaller than the rank of the battery state matrix.

[0031] The original pool state matrix is For the voltage U n , time is t n Battery status when

[0032] The battery state matrix after back-projection restoration is

[0033] The constraint factor is Where x<n, y<n.

[0034] The processing and transformation of the battery state matrix can effectively eliminate the interference elements in the original battery state matrix and concentrate the effective elements, thereby effectively improving the accuracy of the prediction.

[0035] In step E, the elements in the battery state matrix are extracted according to the time sequence, and the state curve is fitted. The state curve is compared with the initial constant current discharge curve. The average deviation ratio is the deviation ratio between the current actual maximum capacity and the initial capacity of the power battery. Then, the current SOC is obtained based on the current actual maximum capacity and discharge amount.

[0036] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A battery management method for electric vehicles, characterized in that The following steps are involved: A. Preset the initial capacity and initial constant current discharge curve of the power battery; during the power battery discharge process, record the real-time discharge curve; B. Marking an approximate constant current discharge interval on the real-time discharge curve, and then standardizing the marked curve segment; standardizing the marked curve segment includes the following steps: Calculate the average discharge current in the marked time period, then calculate the standard deviation between the actual discharge current and the average discharge current, lengthen the time axis of the interval in the marked curve segment where the actual discharge current is less than the average discharge current, and shorten the time axis of the interval in the marked curve segment where the actual discharge current is greater than the average discharge current, with the ratio of lengthening or shortening the time axis being proportional to the above-calculated standard deviation; C. Select several sampling points in the standardized constant current discharge interval, use the time corresponding to the sampling points in the constant current discharge interval to select corresponding sampling points in the initial constant current discharge curve, form the two groups of sampling points into sampling vectors, and then perform the outer product operation on the two sampling vectors to obtain the battery state matrix; D. Process and transform the battery state matrix; project the battery state matrix onto the time axis to obtain a projection curve, split the projection curve at its singular point, and then perform a linear transformation on the segmented projection curve segments to make the module lengths of the projection curve segments equal. Then, perform back-projection to restore the battery state matrix, and add constraint factors to the changed elements in the battery state matrix; E. Predict the actual maximum capacity and SOC of the power battery based on the battery state matrix; F. Determine the current charge and discharge strategy based on the most recently predicted actual maximum capacity and SOC of the power battery.

2. The electric vehicle battery management method according to claim 1, characterized in that: In step C, at least 30% of the sampling points in the standardized constant current discharge interval are located within the interval where the time axis is adjusted.

3. The electric vehicle battery management method according to claim 2, characterized in that: The rank of the constraint matrix composed of constraint factors is smaller than the rank of the battery state matrix.

4. The electric vehicle battery management method according to claim 3, characterized in that: In step E, the elements in the battery state matrix are extracted according to the time sequence, and the state curve is fitted. The state curve is compared with the initial constant current discharge curve. The average deviation ratio is the deviation ratio between the current actual maximum capacity and the initial capacity of the power battery. Then, the current SOC is obtained based on the current actual maximum capacity and discharge amount.

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