关键词: 新能源汽车；动力电池；故障诊断；预测预警

Abstract: With the continuous increase in the number of new energy vehicles, the safety issues of their traction battery systems have gradually emerged. To address the issues of complex and diverse fault types in power battery systems and the insufficient detection accuracy of traditional diagnostic methods, this paper combines decision tree (DT) and adaptive boosting (AdaBoost) algorithms to propose a battery fault diagnosis and prediction method based on the DT-AdaBoost model. By constructing a multi-dimensional feature dataset of typical battery fault characteristics, including voltage, current, temperature, and cycle number, and after data cleaning, feature selection, and standardization preprocessing, the adaptive weight adjustment mechanism of the AdaBoost algorithm is used to integrate the weak learner DT. The training process of the DT-AdaBoost model is designed, key parameters are optimized, and stability is ensured through cross-validation. This research provides an effective solution for the intelligent diagnosis and prediction of traction battery faults in new energy vehicles, which can reduce maintenance costs and improve battery system safety, holding significant engineering application value.

Key words: new energy vehicles; traction battery; fault diagnosis; prediction and early warning