电工技术学报2018，Vol.33Issue(1)：17-25,9.DOI:10.19595/j.cnki.1000-6753.tces.161325
基于电动汽车工况识别预测的锂离子电池SOE估计
Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition
摘要
Abstract
State-of-energy (SOE) is an important index of the internal state of electric vehicle traction batteries that determines the range of electric vehicles directly and which is influenced by the driving condition significantly. In order to estimate SOE based on the driving condition, the SOE estimation algorithm, driving condition identification algorithm, driving condition prediction algorithm were studied in this paper. A battery state of residual energy (SOR) estimation algorithm based on battery model was established. A driving condition identification algorithm based on the informational entropy theory was built. A driving condition prediction algorithm was proposed with Markov chain theory. The battery predicted working condition schedule was achieved by modeling the electric vehicle system. In the end, the SOE estimation algorithm based on the identification and prediction of driving condition was achieved. Validation results show that the proposed SOE estimation algorithm was efficient.关键词
锂离子电池/SOE估计/工况识别/工况预测/电动汽车模型Key words
Lithium-ion battery/SOE estimation/identification algorithm/prediction algorithm/electric vehicle model分类
信息技术与安全科学引用本文复制引用
刘伟龙,王丽芳,王立业..基于电动汽车工况识别预测的锂离子电池SOE估计[J].电工技术学报,2018,33(1):17-25,9.基金项目
国家重点研发计划资助项目(2016YFB0101801). （2016YFB0101801）
