TY - JOUR
T1 - A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system
AU - Wang, Zuolu
AU - Feng, Guojin
AU - Liu, Xiongwei
AU - Gu, Fengshou
AU - Ball, Andrew
N1 - Funding Information:
The research work has received funding from InnovateUK project under Grant agreement No 105427. The research has been undertaken as a part of the project entitled “EnSmartEV - Entrust Smart EV Charging System for Public Spaces”.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Battery energy storage system (BESS) has been widely used for the expansion of the electricity grid. Accurate state of charge (SOC) estimation of BESS is essential for ensuring system safety and reliability. However, it is still a challenging work to improve the model accuracy and supress the cumulative error during the SOC estimation. This paper develops a parameter identification method based on the dynamic voltage responses in the practical constant current (CC) discharging process to identify the battery model parameters with the particle swarm optimization (PSO) method. Furthermore, to minimize system errors caused by the model, algorithm, and measurement system, a hybrid SOC estimation method is proposed. In the proposed hybrid method, an improved extended Kalman filter (EKF) method with constructed compensation error is employed to suppress system errors. PSO is again adopted to determine the dynamic compensation error based on the reliable increment of the ampere-hour counting (AHC) method over the whole SOC range. To validate the effectiveness of the proposed method, three CC discharging tests are carried out at 25 and 5 ℃. The results show the proposed model parameter identification method and the hybrid SOC estimation method can jointly provide more accurate SOC estimation.
AB - Battery energy storage system (BESS) has been widely used for the expansion of the electricity grid. Accurate state of charge (SOC) estimation of BESS is essential for ensuring system safety and reliability. However, it is still a challenging work to improve the model accuracy and supress the cumulative error during the SOC estimation. This paper develops a parameter identification method based on the dynamic voltage responses in the practical constant current (CC) discharging process to identify the battery model parameters with the particle swarm optimization (PSO) method. Furthermore, to minimize system errors caused by the model, algorithm, and measurement system, a hybrid SOC estimation method is proposed. In the proposed hybrid method, an improved extended Kalman filter (EKF) method with constructed compensation error is employed to suppress system errors. PSO is again adopted to determine the dynamic compensation error based on the reliable increment of the ampere-hour counting (AHC) method over the whole SOC range. To validate the effectiveness of the proposed method, three CC discharging tests are carried out at 25 and 5 ℃. The results show the proposed model parameter identification method and the hybrid SOC estimation method can jointly provide more accurate SOC estimation.
KW - Battery energy storage system
KW - Lithium-ion battery
KW - State of charge estimation
KW - Extended Kalman filter
KW - Particle swarm optimization
KW - Ampere-hour counting method
UR - http://www.scopus.com/inward/record.url?scp=85124098583&partnerID=8YFLogxK
U2 - 10.1016/j.est.2022.104124
DO - 10.1016/j.est.2022.104124
M3 - Article
VL - 49
JO - Journal of Energy Storage
JF - Journal of Energy Storage
SN - 2352-1538
M1 - 104124
ER -