基于数据驱动的电动汽车动力电池SOC预测  

A Data-driven SOC Prediction Scheme for Traction Battery in Electric Vehicles

作　　者：胡杰[1,2,3] 高志文 Hu Jie;Gao Zhiwen(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)

机构地区：[1]武汉理工大学,现代汽车零部件技术湖北省重点实验室,武汉430070 [2]武汉理工大学,汽车零部件技术湖北省协同创新中心,武汉430070 [3]新能源与智能网联车湖北工程技术研究中心,武汉430070

出　　处：《汽车工程》2021年第1期1-9,18,共10页Automotive Engineering

基　　金：柳州市重点研发计划项目(2018BC20501)资助。

摘　　要：为准确预测电动汽车动力电池的能耗,缓解驾驶者的里程焦虑,本文中提出一种基于数据驱动的电动汽车动力电池SOC预测模型。首先分析电动汽车能耗构成并提取能耗影响因素,接着基于某款电动出租车CAN总线采集的汽车运行数据,采用机器学习算法,提出基于温度分层的能耗模型,通过宏观数据与微观数据的融合减小误差,最后使用该模型对车载BMS提供的SOC数据进行对比验证。结果表明,该模型预测效果较好,为帮助优化电动汽车能量控制策略、缓解里程焦虑提供科学的决策支持。In order to accurately predict the energy consumption of traction battery in electric vehicle(EV)and alleviate the mileage anxiety of drivers,a data⁃driven SOC prediction model for the traction battery in EV is proposed in this paper.Firstly,the composition of energy consumption in EVs is analyzed and the influencing fac⁃tors of energy consumption are extracted.Then based on the vehicle operation data collected by the CAN bus of an EV with machine learning algorithm adopted,an energy consumption model based on temperature stratification is proposed and the macro data and micro data is fused to reduce errors.Finally,the model is used to verify the SOC data provided by on-board BMS.The results show that the model has a good prediction result,providing a scientific decision support for optimizing the energy control strategy of EVs and alleviating driver’s mileage anxiety.

关 键 词：电动汽车 SOC预测 数据驱动 机器学习 

分 类 号：U469.72[机械工程—车辆工程]