期刊：
IEEE Transactions on Transportation Electrification,2021年7(2):399-409 ISSN：2332-7782
通讯作者：
Liu, Longcheng;Wei, Zhongbao
作者机构：
[Bian, Xiaolei; Liu, Longcheng] KTH Royal Inst Technol, Dept Chem Engn, S-11428 Stockholm, Sweden.;[Wei, Zhongbao] Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China.;[He, Jiangtao; Yan, Fengjun] McMaster Univ, Dept Mech Engn, Hamilton, ON L8S 4L8, Canada.;[Liu, Longcheng] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
通讯机构：
[Liu, Longcheng] K;[Wei, Zhongbao] B;KTH Royal Inst Technol, Dept Chem Engn, S-11428 Stockholm, Sweden.;Beijing Inst Technol, Sch Mech Engn, Natl Engn Lab Elect Vehicles, Beijing 100811, Peoples R China.
关键词：
State of charge;Estimation;Batteries;Computational modeling;Mathematical model;Integrated circuit modeling;Transportation;Filter tuning;Kalman filter;lithium-ion battery;particle swarm optimization (PSO);state of charge (SOC)
摘要：
The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.
