Review on state of charge estimation techniques of lithium-ion batteries: A control-oriented approach
Review article, 2023

Energy storage has become one of the most critical issues of modern technology. In this regard, lithium-ion batteries have proven effective as an energy storage option. To optimize its performance and extend its lifetime, it is essential to monitor the battery's state of charge. Due to the distinct nonlinear behavior of batteries over their lifetime, the state of charge estimation is challenging. The challenge has been establishing a relationship between accuracy, robustness, and low implementation complexity. Over the last decade, numerous attempts have been made to effectively analyze and compare the state of charge estimation methods for commercial lithium-ion batteries. However, they seldom reflect on the state of charge estimation techniques based on a control-oriented viewpoint for a Li-ion battery system. To fill this gap, this paper reviews the most up-to-date battery state of charge estimation methods applied to lithium-ion battery systems. They are broadly classified as open-loop-based, closed-loop-based, and hybrid approaches. Finally, the paper concludes by providing an analysis of the positive and negative aspects of the reviewed techniques and some suggestions for future research.

State of charge

Feedback control

Estimation methods

Lithium-ion batteries

Battery management system

Author

Nourallah Ghaeminezhad

Nanjing University of Aeronautics and Astronautics

Quan Ouyang

Nanjing University of Aeronautics and Astronautics

Chalmers, Electrical Engineering, Systems and control

Jingwen Wei

Nanjing University

Yali Xue

Nanjing University of Aeronautics and Astronautics

Zhisheng Wang

Nanjing University of Aeronautics and Astronautics

Journal of Energy Storage

2352-152X (eISSN)

Vol. 72 108707

User behaviour informed learning and intelligent control for charging of vehicle battery packs

European Commission (EC) (101067291), 2022-08-01 -- 2024-07-31.

Subject Categories

Probability Theory and Statistics

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.est.2023.108707

More information

Latest update

9/7/2023 1