TY - JOUR
T1 - A Comprehensive Evaluation of the Influence of Major Hysteresis on State of Charge Prediction of LiNiMnCoO2 Battery
AU - Feng, Hailong
AU - Wang, Zhifu
AU - Zhang, Fujun
N1 - Publisher Copyright:
© Copyright © 2021 Feng, Wang and Zhang.
PY - 2021/5/5
Y1 - 2021/5/5
N2 - Accurate open-circuit voltage (OCV) is crucial for state of charge (SoC) estimation of lithium-ion batteries and, hence has become a key factor to ensure the safety and reliability of electric vehicles (EVs). In engineering, the incremental OCV (IO) testing has been widely used for OCV calibrating. Based on this, the OCV is commonly simplified by averaging the discharging and the charging OCVs, which essentially ignores the influence of the major hysteresis (MH). By a series of experiments on the LiNiMnCoO2 battery, this work first systematically investigated the influence of the MH on SoC estimation via diverse current profiles tested at various ambient temperatures. Besides, the recursive least square (RLS) and the particle filter (PF) algorithms were introduced to estimate the battery parameters and the SoC, respectively. The results report that, compared with the traditional simplified method, the discharging direction and the charging direction of the MH can enhance the estimation accuracy of the discharging process and the charging process of the battery at all the operating conditions above, respectively. By the MH-based estimation method, the maximum mean absolute estimation error can be reduced by about 70%.
AB - Accurate open-circuit voltage (OCV) is crucial for state of charge (SoC) estimation of lithium-ion batteries and, hence has become a key factor to ensure the safety and reliability of electric vehicles (EVs). In engineering, the incremental OCV (IO) testing has been widely used for OCV calibrating. Based on this, the OCV is commonly simplified by averaging the discharging and the charging OCVs, which essentially ignores the influence of the major hysteresis (MH). By a series of experiments on the LiNiMnCoO2 battery, this work first systematically investigated the influence of the MH on SoC estimation via diverse current profiles tested at various ambient temperatures. Besides, the recursive least square (RLS) and the particle filter (PF) algorithms were introduced to estimate the battery parameters and the SoC, respectively. The results report that, compared with the traditional simplified method, the discharging direction and the charging direction of the MH can enhance the estimation accuracy of the discharging process and the charging process of the battery at all the operating conditions above, respectively. By the MH-based estimation method, the maximum mean absolute estimation error can be reduced by about 70%.
KW - energy management
KW - lithium-ion batteries
KW - open-circuit voltage
KW - particle filter
KW - state of charge
UR - http://www.scopus.com/inward/record.url?scp=85105982765&partnerID=8YFLogxK
U2 - 10.3389/fenrg.2021.666092
DO - 10.3389/fenrg.2021.666092
M3 - Article
AN - SCOPUS:85105982765
SN - 2296-598X
VL - 9
JO - Frontiers in Energy Research
JF - Frontiers in Energy Research
M1 - 666092
ER -