电工技术学报  2018, Vol. 33 Issue (1): 17-25    DOI: 10.19595/j.cnki.1000-6753.tces.161325
电力电子 |
基于电动汽车工况识别预测的锂离子电池SOE估计
刘伟龙1,2,王丽芳1,王立业1
1. 中国科学院电力电子与电力传动重点实验室 中国科学院电工研究所 北京 100190;
2. 中国科学院大学 北京 100049
Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition
Liu Weilong1,2,Wang Lifang1,Wang Liye1
1. Key Laboratory of Power Electronics and Electric Drives Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190 China;
2. University of Chinese Academy of Sciences Beijing 100049 China
全文: PDF (425 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 能量状态(SOE)是电动汽车动力电池的重要状态指标,直接影响电动汽车续航里程,受电动汽车工况显著影响。为进行基于电动汽车工况的SOE估计,对SOE估计方法、行驶工况识别算法、行驶工况预测算法展开研究,建立基于模型的电池剩余能量状态(SOR)估计方法,提出基于信息熵理论的行驶工况识别算法,应用马尔科夫链理论构建了行驶工况预测算法,建立电动汽车系统模型,仿真获取电动汽车预测行驶工况对应的电池预测工况,实现基于电动汽车工况识别与预测的SOE估计。仿真结果验证了该方法的有效性。
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
刘伟龙
王丽芳
王立业
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    
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.
Key wordsLithium-ion battery    SOE estimation    identification algorithm    prediction algorithm    electric vehicle model   
收稿日期: 2016-08-24      出版日期: 2018-01-16
基金资助:国家重点研发计划资助项目(2016YFB0101801)
引用本文:   
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. Liu Weilong,Wang Lifang,Wang Liye. Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition. Transactions of China Electrotechnical Society, 2018, 33(1): 17-25.
链接本文:  
https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.161325          https://dgjsxb.ces-transaction.com/CN/Y2018/V33/I1/17