A study on the application of machine learning algorithms incorporating biomechanical principles in optimising the health status assessment of electric vehicle power batteries
Abstract
This study addresses the problem of power battery health state assessment for electric vehicles, integrating biomechanical principles and machine learning algorithms to investigate the health state assessment accuracy of different types of power batteries under different working conditions. The study adopts a variety of data-driven methods to deeply analyse the performance degradation law of power batteries. The results show that the machine learning algorithm incorporating biomechanical principles can effectively improve the accuracy of power battery health state assessment, especially under complex working conditions, and exhibits better robustness. The current status of power battery health state assessment technology is reported, and it provides a useful reference for future power battery health management in electric vehicles.
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