A method for evaluating the state of health and remaining life of the power battery of an electric vehicle

Abstract

The invention provides a method for evaluating the state of health and remaining life of a power battery of an electric vehicle. For processed data segments, a discrete capacity increment analysis method is used to construct a capacity increment curve of the power battery in the current state, and by analyzing the discrete capacity The change of the capacity increment sum corresponding to the voltage value of the overlapping part of the abscissa of the incremental curve determines the change of the charging capacity, and then completes the evaluation of the state of health of the electric vehicle power battery, that is, the SOH. The long-term variation trend part and the short-term fluctuation part of the battery SOH curve are stripped out by the empirical mode decomposition method, and the SOH prediction result of the power battery in the future stage, that is, the RUL prediction of the remaining life, is obtained by synthesizing the prediction results of the two parts. It is suitable for evaluating the health status of electric vehicles of various models and charging strategies. It has strong compatibility and clear physical meaning. It avoids blindly using various intelligent algorithms for black-box modeling, so the processing rate and memory usage are also very impressive.

Description

technical field [0001] The invention relates to the technical field of power battery management systems for electric vehicles, and in particular to a method for evaluating the health status and remaining life of a power battery of an electric vehicle. Background technique [0002] With the gradual popularization of electric vehicles, more and more attention has been paid to the safety and durability of their on-board power batteries. As far as safety is concerned, most of the safety accidents of electric vehicles originate from the explosion of aging power batteries, so how to quickly and accurately assess the health status of electric vehicle power batteries is extremely important. [0003] At present, most of the evaluation methods for the health status of power batteries are divided into two categories: model-based estimation methods and data-driven estimation methods. Among them, the model-based estimation method mainly refers to the use of nonlinear aging model combine...

AI Technical Summary

Problems solved by technology

Among them, the machine learning method based on offline data requires a large amount of offline data to achieve accurate training of the battery model, so the algorithm has a large amount of calculation and the estimated cost is high

The machine learning method based on online data does not require offline data, and completely relies on online data to realize the prediction of the remaining life of the power battery, and each prediction requires retraining data. A major drawback of this method is that it does not consider the attenuation of battery aging. characteristics, so the estimated battery health status and remaining service life are less accurate

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