Active Cell Balancing for Extended Operational Time of Lithium-Ion Battery Systems in Energy Storage Applications

TL;DR

This paper introduces an optimization-based active cell balancing method using fractional order models and evolutionary algorithms to extend lithium-ion battery system operational time, validated through experiments.

Contribution

It presents a novel optimization-driven active balancing approach with fractional order modeling and genetic algorithms for improved battery lifespan in energy storage.

Findings

Increases operational time by 3.2%.

Validates effectiveness through experiments on two balancing topologies.

Enhances battery management with fractional order models.

Abstract

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%.