电池(电)
蒙特卡罗方法
均方误差
电阻抗
标准差
介电谱
荷电状态
锂离子电池
计算机科学
控制理论(社会学)
数学
统计
工程类
化学
电化学
功率(物理)
电气工程
电极
物理
热力学
人工智能
物理化学
控制(管理)
作者
H.P.G.J. Beelen,Luc H.J. Raijmakers,M.C.F. Donkers,Peter H. L. Notten,Henk Jan Bergveld
出处
期刊:Applied Energy
[Elsevier BV]
日期:2016-05-06
卷期号:175: 128-140
被引量:119
标识
DOI:10.1016/j.apenergy.2016.04.103
摘要
In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the battery temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the battery temperature and several EIS-based temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the battery temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the temperature with an average absolute bias of 0.4°C and an average standard deviation of 0.7°C using a single impedance measurement for the battery under consideration.
科研通智能强力驱动
Strongly Powered by AbleSci AI