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Research on fault diagnosis and prediction of traction batteries
for electric vehicles
KANG Wei1
, YI Xing2*
, YANG Dongqing1
, CAI Linhao1
, GUO Yanglong1
, HUANG Xilei1
2026, 51(14):
28-34.
DOI: 10.16638/j.cnki.1671-7988.2026.014.005
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.
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