电池2026，Vol.56Issue(1)：69-76,8.DOI:10.19535/j.1001-1579.2026.01.010
基于改进Smote策略的动力电池故障检测
Fault detection in power batteries based on an improved Smote strategy
摘要
Abstract
Fault detection in automotive power batteries represents a critical technology for preventing vehicle safety incidents.However,current research faces two major challenges:imbalanced distribution of fault data and sample scarcity,which leads to insufficient prediction accuracy and limited model generalization capability.To tackle these issues,a Stacking-integrated diagnostic framework that merges data augmentation with feature reconstruction techniques is introduced.Firstly,the fault level classification system is reconstructed based on Fault Tree Analysis(FTA),an improved Border-Smote algorithm is employed to achieve sample augmentation.Secondly,time,working condition and driving-related features are extracted through feature engineering to construct a multi-dimensional feature space.Finally,based on Bayesian hyperparameter optimization,aStacking ensemble model comprising LightGBM,XGBoost and random forest(RF)as base-level classifiers with logistic regression(LR)as the meta-classifier is constructed.The experimental validation shows that when combined with the enhanced Smote data augmentation and feature reconstruction techniques,the proposed ensemble approach attains a remarkable detection accuracy of over 97％.关键词
动力电池/电池故障检测/数据增强/Stacking模型Key words
power battery/battery fault detection/data augmentation/Stacking model分类
信息技术与安全科学引用本文复制引用
蓝南愉,陈学文,胡立鹏,唐进君..基于改进Smote策略的动力电池故障检测[J].电池,2026,56(1):69-76,8.基金项目
国家自然科学基金面上项目(52172310),湖南省重点研发计划(2023GK2014),辽宁省属本科高校基本科研业务费专项资金资助项目(LJZZ232410154016) （52172310）
