摘要
为了对电池安全风险进行准确预测,本文提出基于一种车-天气-驾驶员的多指标电池安全风险预测方法。首先提取车内外多维度信息即运用数据挖掘提取了天气状况、汽车行驶工况和驾驶风格等多指标特征,以模拟实际的电池应用场景;然后通过随机森林和SHAP组合模型的方式对特征进行筛选,从而提高了模型的泛化性和鲁棒性;最后将电池安全风险预测问题解耦为机器学习预测和时间序列预测问题,分别选择XGBoost和随机森林模型进行预测,并在此基础上建立新的Stacking集成模型对电池安全风险进行预测。最终模型的预测效果和数据实验的结果表明,该方案对电动汽车电池安全风险能做出较为准确的预测,可以为安全化、智能化的电池管理系统提供辅助决策信息。
In order to accurately predict the battery safety risk,a multi-index battery safety risk prediction method based on vehicle-weather-driver is proposed in this paper.Firstly,multi-dimensional information inside and outside the vehicle is extracted,i.e.multi-index characteristics such as weather condition,driving conditions and driving style are extracted by data mining to simulate the actual battery application scenario.Then,features are filtered by random forest and SHAP combination model,which improves the generalization and robustness of the model.Finally,the battery safety risk prediction problem is decoupled into machine learning prediction and time series prediction problems,and XGBoost and random forest models are selected to predict respectively.On this basis,a new Stacking integrated model is established to predict the battery safety risk.According to the predictive effect of the final model and the results of data experiment,the scheme can make a more accurate prediction of the battery safety risk of EV and provide decision-making information for safe and intelligent battery management system.
作者
胡杰
余海
杨博闻
程雅钰
Hu Jie;Yu Hai;Yang Bowen;Cheng Yayu(Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070;Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070;Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070)
出处
《汽车工程》
EI
CSCD
北大核心
2023年第5期814-824,共11页
Automotive Engineering
基金
湖北省科技重大专项(2021AAA001)资助。
作者简介
通信作者:胡杰,教授,博士生导师,E-mail:auto_hj@163.com。
