Abstract

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.

Details

Title
A Comparative Study on Open Circuit Voltage Models for Lithium-ion Batteries
Author
Quan-Qing, Yu 1 ; Xiong Rui 1   VIAFID ORCID Logo  ; Le-Yi, Wang 2 ; Cheng, Lin 1 

 Beijing Institute of Technology, National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing, China (GRID:grid.43555.32) (ISNI:0000 0000 8841 6246) 
 Wayne State University, Department of Electrical and Computer Engineering, Detroit, USA (GRID:grid.254444.7) (ISNI:0000 0001 1456 7807) 
Publication year
2018
Publication date
Dec 2018
Publisher
Springer Nature B.V.
ISSN
10009345
e-ISSN
21928258
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2511712595
Copyright
© The Author(s) 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.