An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimizati...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 7; no. 11; p. 321
Main Authors Lai, Xin, Yi, Wei, Zheng, Yuejiu, Zhou, Long
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 2018
Subjects
Accuracy
Algorithms
Experiments
Extended Kalman filter
Identification methods
Lithium-ion batteries
Mathematical models
Methods
Optimization
Parameter identification
Particle swarm optimization
Rechargeable batteries
State of charge
Online AccessGet full text

Cover

More Information
Summary:In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics7110321