Fault diagnosis of new energy vehicles based on improved machine learning

The new energy vehicle system is in the initial stage of application, so the probability of fault is greater. Therefore, its reliability urgently needs to be improved. In order to improve the fault diagnosis effect of new energy vehicles, this paper proposes a fault diagnosis system of new energy vehicle electric drive system based on improved machine learning and also proposes several typical fault detection and diagnosis methods. Through the study of the operating characteristics and structural characteristics of the electric drive system and the analysis of the fault mechanism, this paper classifies the main faults of the electric drive system according to the self-test requirements of the electric drive system and the online diagnosis requirements and presents the self-test fault tree and online diagnosis fault tree of the electric drive system. In addition, this paper designs experiments to verify the performance of the system proposed by this paper, simulates the current common system fault conditions, and uses the system constructed in this paper to perform system fault diagnosis. The research results show that the performance of the fault diagnosis system for drive energy vehicles constructed in this paper is reliable.