汽车工程 ›› 2021, Vol. 43 ›› Issue (1): 1-9.doi: 10.19562/j.chinasae.qcgc.2021.01.001

• •    下一篇

基于数据驱动的电动汽车动力电池SOC预测

胡杰1,2,3(),高志文1,2,3   

  1. 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
  • 收稿日期:2020-03-29 修回日期:2020-06-28 出版日期:2021-01-25 发布日期:2021-02-03
  • 通讯作者: 胡杰 E-mail:auto_hj@163.com
  • 基金资助:
    柳州市重点研发计划项目(2018BC20501)

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

Jie Hu1,2,3(),Zhiwen Gao1,2,3   

  1. 1.Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan 430070
    2.Wuhan University of Technology,Auto Parts Technology Hubei Collaborative Innovation Center,Wuhan 430070
    3.Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering,Wuhan 430070
  • Received:2020-03-29 Revised:2020-06-28 Online:2021-01-25 Published:2021-02-03
  • Contact: Jie Hu E-mail:auto_hj@163.com

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。

关键词: 电动汽车, SOC预测, 数据驱动, 机器学习

Abstract:

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 factors 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.

Key words: electric vehicle, SOC prediction, data?driven, machine learning