期刊
ENERGY
卷 153, 期 -, 页码 732-742出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2018.04.026
关键词
Lithium-ion battery; Parameters estimation online; Adaptive sliding mode observer; State of charge estimation; State of health estimation
资金
- Fundamental Research Funds for the Central Universities
Simplicity and accuracy are both important factors in real-time battery states estimation applications. However, a battery model initialized with static parameters which are identified in ideal laboratory conditions will not be able to get an accurate estimation in various actual applications. Besides, it is time-consuming and complex in implement. To solve the above problem, a new battery states estimation method is proposed. Firstly, an adaptive battery model is proposed according to a new online parameter estimation algorithm. Based on it, the parameter adaptive sliding mode observer for state of charge is proposed. Thus, the state of charge systematic error led from various work environments could be effectively reduced. The parameter adaptive sliding mode observer for state of health is proposed by tracing the derivative of open circuit voltage estimated online. As the reference open circuit voltage is estimated based on measurable inputs and outputs, rather than conventional observer with an assumed constant capacity. The estimated battery capacity could converge to the actual value while the error of battery open circuit voltage converges to zero. The proposed method is verified through the urban dynamometer driving schedule driving cycle. The results indicate that:1) parameters estimated online are accurate, 2) the absolute error of state of charge is less than 2%, 3) the estimated lithium-ion battery capacity could converge to the actual value with small capacity error. (C) 2018 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据