2024/05/02 by Yiming Xu, Xiaohua Ge, Xu, Yiming +5
Engineering · #Advanced Battery Technologies Research #Advancements in Battery Materials #FOS: Electrical engineering #Microgrid Control and Optimization #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.00973
openalex publication_date 2024/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cell inconsistency within a lithium-ion battery system poses a significant challenge in maximizing the system operational time. This study presents an optimization-driven active balancing method to minimize the effects of cell inconsistency on the system operational time while simultaneously satisfying the system output power demand and prolonging the system operational time in energy storage applications. The proposed method utilizes a fractional order model to forecast the terminal voltage dynamics of each cell within a battery system, enhanced with a particle-swarm-optimisation-genetic algorithm for precise parameter identification. It is implemented under two distinct cell-level balancing topologies: independent cell balancing and differential cell balancing. Subsequently, the current distribution for each topology is determined by resolving two optimization control problems constrained by the battery's operational specifications and power demands. The effectiveness of the proposed method is validated by extensive experiments based on the two balancing topologies. The results demonstrate that the proposed method increases the operational time by 3.2%.