Method for evaluating health state and residual life of power battery of electric vehicle

Abstract

The invention provides a method for evaluating the health state and residual life of a power battery of an electric vehicle. The method comprises the steps: constructing a capacity increment curve ofa power battery in a current state through a discrete capacity increment analysis method for a processed data segment; determining the change of the charging capacity by analyzing the change of the capacity increment sum corresponding to the voltage value of the overlapped part of the abscissa of the discrete capacity increment curve, so that the evaluation of the SOH of the power battery of the electric vehicle is completed; and stripping a long-range change trend part and a short-range fluctuation part of the SOH curve of the battery by utilizing an empirical mode decomposition method, and synthesizing the two parts of prediction results to obtain an SOH prediction result of the power battery in the future stage, namely residual life RUL prediction. The method is suitable for health state evaluation of electric vehicles of various types and various charging strategies, is high in compatibility and clear in physical significance, avoids blindly using various intelligent algorithms forblack box modeling, and is considerable in processing rate and memory occupation condition.

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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