An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery-Reference-Cited by-同舟云学术

An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery

Published:2023-03-30 Issue:2 Volume:21 Page:376-386
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Zhou Yifei¹,Wang Shunli¹,Xie Yanxin¹,Zhu Tao¹,Fernandez Carlos²

Affiliation:

1. School of Information Engineering, Engineering and Technology Center, Southwest University of Science and Technology, Mianyang, China

2. School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, UK

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2023.2196328

Reference39 articles.

1. Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

2. Support Vector Machines Used to Estimate the Battery State of Charge

3. Battery Internal State Estimation: A Comparative Study of Non-Linear State Estimation Algorithms

4. Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization

5. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

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