A Decoupled Two-Stage Optimization Framework for the Multi-Objective Coordination of Charging Efficiency and Battery Health

Xin Yi, Lingxia Shi, Xiaoyang Chen and Xu Lei ()
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Xin Yi: School of Electronic and Control Engineering, Chang’an University, Middle Section of Nan Erhuan Road, Beilin District, Xi’an 710064, China
Lingxia Shi: School of Electronic and Control Engineering, Chang’an University, Middle Section of Nan Erhuan Road, Beilin District, Xi’an 710064, China
Xiaoyang Chen: School of Electronic Information, Central South University, 932 Lushan Road, Yuelu District, Changsha 410075, China
Xu Lei: School of Electronic and Control Engineering, Chang’an University, Middle Section of Nan Erhuan Road, Beilin District, Xi’an 710064, China

Energies, 2025, vol. 18, issue 19, 1-33

Abstract: A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health.

Keywords: multi–objective optimization; lithium-ion battery; multistage constant current charging; adaptive multi–phase charging; wavelet neural network; multi–objective particle swarm optimization; active learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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