LAPSE:2023.31710
Published Article
LAPSE:2023.31710
A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control
Guan-Jhu Chen, Yi-Hua Liu, Yu-Shan Cheng, Hung-Yu Pai
April 19, 2023
Lithium-ion (Li-ion) batteries play a substantial role in portable consumer electronics, electric vehicles and large power energy storage systems. For Li-ion batteries, developing an optimal charging algorithm that simultaneously takes rises in charging time and charging temperature into account is essential. In this paper, a model predictive control-based charging algorithm is proposed. This study uses the Thevenin equivalent circuit battery and transforms it into the state-space equation to develop the model predictive controller. The usage of such models in the battery optimal control context has an edge due to its low computational cost, enabling the realization of the proposed technique using a low-cost Digital Signal Processor (DSP). Compared with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature by 3.25% and 0.76%, respectively.
Keywords
equivalent circuit model, lithium-ion battery, Model Predictive Control
Suggested Citation
Chen GJ, Liu YH, Cheng YS, Pai HY. A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control. (2023). LAPSE:2023.31710
Author Affiliations
Chen GJ: Department of Electrical Engineering, National Taiwan University of Science and Technology, Da’an District, Taipei 10607, Taiwan
Liu YH: Department of Electrical Engineering, National Taiwan University of Science and Technology, Da’an District, Taipei 10607, Taiwan [ORCID]
Cheng YS: Department of Electrical Engineering, National Taiwan Ocean University, Zhongzheng District, Keelung 202301, Taiwan
Pai HY: Department of Electrical Engineering, National Taiwan University of Science and Technology, Da’an District, Taipei 10607, Taiwan
Journal Name
Energies
Volume
14
Issue
8
First Page
2238
Year
2021
Publication Date
2021-04-16
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14082238, Publication Type: Journal Article
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doi:10.3390/en14082238
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