Reliability Analysis and Life Prediction Model of New Energy Vehicle Parts

With the rapid development of the new energy vehicle industry, the reliability and life of its parts have become the key factors affecting the performance and safety of the whole vehicle. This paper aims to provide a scientific basis for the maintenance and life management of new energy vehicles through the research on the reliability analysis and life prediction model of new energy vehicle parts. Firstly, the basic theory of reliability engineering is reviewed, and the research progress and existing problems of the existing life prediction models are comprehensively analyzed. On this basis, a data-driven life prediction model is proposed in this paper, which comprehensively uses the methods of machine learning, statistical analysis and reliability engineering to improve the accuracy and practicability of the prediction. In the research, the operation and maintenance data of new energy vehicle parts are collected, and the failure modes of the parts are deeply analyzed. Using fault tree analysis (FTA) and reliability parameter estimation methods, the key factors affecting the life of the parts are identified. Furthermore, the key features affecting the life are extracted through feature engineering, and the life prediction model is constructed by using machine learning methods. In the process of model construction, special attention is paid to the generalization ability and robustness of the model to ensure its applicability under different working conditions and environments. In order to verify the effectiveness of the model, this paper selects the battery and motor of new energy vehicles as cases to carry out the practical application and verification of the model. The results show that the proposed model is superior to the traditional methods in prediction accuracy and can provide strong support for the maintenance decision of new energy vehicle parts. Finally, this paper discusses the challenges that the model may face in practical application and puts forward suggestions for future research directions.