scholarly journals Decreasing Weight Particle Swarm Optimization Combined with Unscented Particle Filter for the Non-Linear Model for Lithium Battery State of Charge Estimation

2020 ◽  
pp. 10104-10116
Author(s):  
Lei Chen ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Linear Model ◽  
Lithium Battery ◽  
Particle Swarm ◽  
State Of Charge ◽  
Swarm Optimization ◽  
Unscented Particle Filter ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

Fault diagnosis based on new particle swarm optimization particle filter

2013 ◽  
Vol 32 (2) ◽  
pp. 432-435
Author(s):  
Zhi-min CHEN ◽  
Yu-ming BO ◽  
Pan-long WU ◽  
Meng-chu TIAN ◽  
Shao-xin LI ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Filter ◽  
Particle Swarm ◽  
Swarm Optimization

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

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

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.

Luenberger Observer for Lithium Battery State-of-Charge Estimation

2020 ◽  
pp. 655-667
Author(s):  
Stefano Barsali ◽  
Massimo Ceraolo ◽  
Jiajing Li ◽  
Giovanni Lutzemberger ◽  
Claudio Scarpelli
Keyword(s):  
Lithium Battery ◽  
State Of Charge ◽  
Luenberger Observer ◽  
Battery State Of Charge ◽  
State Of Charge Estimation
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