| 摘要: The current research of state of charge (SoC) online estimation of lithium-ion battery (LiB) in electric vehicles (EVs) mainly focuses on adopting or improving of battery models and estimation filters. However, little attention has been paid to the accuracy of various open circuit voltage (OCV) models for correcting the SoC with aid of the ampere-hour counting method. This paper presents a comprehensive comparison study on eighteen OCV models which cover the majority of models used in literature. The low-current OCV tests are conducted on the typical commercial LiFePO4/graphite (LFP) and LiNiMnCoO2/graphite (NMC) cells to obtain the experimental OCV-SoC curves at different ambient temperature and aging stages. With selected OCV and SoC points from experimental OCV-SoC curves, the parameters of each OCV model are determined by curve fitting toolbox of MATLAB 2013. Then the fitting OCV-SoC curves based on diversified OCV models are also obtained. The indicator of root-mean-square error (RMSE) between the experimental data and fitted data is selected to evaluate the adaptabilities of these OCV models for their main features, advantages, and limitations. The sensitivities of OCV models to ambient temperatures, aging stages, numbers of data points, and SoC regions are studied for both NMC and LFP cells. Furthermore, the influences of these models on SoC estimation are discussed. Through a comprehensive comparison and analysis on OCV models, some recommendations in selecting OCV models for both NMC and LFP cells are given. |
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| | 图1 Impacts of OCV models on SoC estimation at 25 °C for both NMC and LFP fresh cells | 图2 Comprehensive comparison of OCV models | 引文信息: Yu Q Q, Xiong R, Wang L Y, et al. A comparative study on open circuit voltage models for lithium-ion batteries[J]. Chinese Journal of Mechanical Engineering, 2018, 31(1): 65. (下载链接) | 其他相关论文: 1. Xiong R, Yu Q, Lin C. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter[J]. Applied energy, 2017, 207: 346-353.(下载链接)
2. Xiong R , Gong X , Mi C C , et al. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources, 2013, 243(6):805-816. (下载链接)
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