Open Access

ARTICLE

Lithium-Ion Battery Pack Based on Fuzzy Logic Control Research on Multi-Layer Equilibrium Circuits

Tiezhou Wu, Yukan Zhang^*
Faculty of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China
* Corresponding Author: Yukan Zhang. Email: zhangyukan1124@gmail.com
(This article belongs to the Special Issue: Advanced Modelling, Operation, Management and Diagnosis of Lithium Batteries)

Energy Engineering https://doi.org/10.32604/ee.2024.049883

Received 21 January 2024; Accepted 18 March 2024; Published online 24 April 2024

Abstract

In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs, a new multilayer equilibrium topology is designed in this paper. The structure adopts a hierarchical structure design, which includes intra-group equilibrium, primary inter-group equilibrium and secondary intergroup equilibrium. This structure greatly increases the number of equilibrium paths for lithium-ion batteries, thus shortening the time required for equilibrium, and improving the overall efficiency. In terms of control strategy, fuzzy logic control (FLC) is chosen to control the size of the equilibrium current during the equilibrium process. We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software. Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy, specifically, the Li-ion battery equilibrium speed is improved by 12.71% in static equilibrium, 14.48% in charge equilibrium, and 11.19% in discharge equilibrium. In addition, compared with the maximum value algorithm, the use of the FLC algorithm reduces the equalization time by about 3.27% and improves the energy transfer efficiency by about 66.49% under the stationary condition, which verifies the feasibility of the equalization scheme.

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Keywords

Lithium-ion battery for new energy vehicles; lithium-ion battery equilibrium; fuzzy logic control

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