Fangdan Zhenga,c, Jiuchun Jianga, Bingxiang Suna, Yinjiao Xingc, Jonghoon Kimcb and Michael Pecht c
a National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing, 100044, China
b Energy Storage Conversion Laboratory (ESCL), Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
c CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
Battery state of charge (SOC) estimation is a crucial function of battery management systems (BMSs),
since accurate estimated SOC is critical to ensure the safety and reliability of electric vehicles. A widely
used technique for SOC estimation is based on online inference of battery open circuit voltage (OCV).
Low-current OCV and incremental OCV tests are two common methods to observe the OCV-SOC relationship,
which is an important element of the SOC estimation technique. In this paper, two OCV tests are run
at three different temperatures and based on which, two SOC estimators are compared and evaluated in
terms of tracking accuracy, convergence time, and robustness for online estimating battery SOC. The temperature
dependency of the OCV-SOC relationship is investigated and its influence on SOC estimation
results is discussed. In addition, four dynamic tests are presented, one for estimator parameter identifi-
cation and the other three for estimator performance evaluation. The comparison results show that estimator
2 (based on the incremental OCV test) has higher tracking accuracy and is more robust against
varied loading conditions and different initial values of SOC than estimator 1 (based on the lowcurrent
OCV test) with regard to ambient temperature. Therefore, the incremental OCV test is recommended
for predetermining the OCV-SOCs for battery SOC online estimation in BMSs.
This article is available online here and to CALCE Consortium Members for personal review.