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

Yiming Xu, Xiaohua Ge, Ruohan Guo, Weixiang Shen

TL;DR

This work tackles cell-imbalance in series lithium-ion packs that limit operational time in energy storage systems. It introduces a fractional-order battery model with PSO-GA parameter identification and two balancing topologies (independent and differential), integrated with model predictive control and EKF-based state estimation to actively balance cells while meeting power demands. The approach casts the current-distribution problem as a quadratic program solvable in real time and validates it on NCR18650B cells under UDDS-driven profiles, achieving an operational-time extension of approximately 3.2%. The results demonstrate effective reduction in voltage disparity and SOC mismatch, with accurate SOC estimation and reliable power tracking, highlighting practical gains for extended ESS operation.

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%.

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

TL;DR

This work tackles cell-imbalance in series lithium-ion packs that limit operational time in energy storage systems. It introduces a fractional-order battery model with PSO-GA parameter identification and two balancing topologies (independent and differential), integrated with model predictive control and EKF-based state estimation to actively balance cells while meeting power demands. The approach casts the current-distribution problem as a quadratic program solvable in real time and validates it on NCR18650B cells under UDDS-driven profiles, achieving an operational-time extension of approximately 3.2%. The results demonstrate effective reduction in voltage disparity and SOC mismatch, with accurate SOC estimation and reliable power tracking, highlighting practical gains for extended ESS operation.

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%.
Paper Structure (9 sections, 28 equations, 11 figures, 3 tables, 2 algorithms)

This paper contains 9 sections, 28 equations, 11 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Fractional-Order Lithium-ion Battery Model
  3. Formulation of Optimization-Based Battery Balancing Problems
  4. Independent Cell Balancing Topology
  5. Differential Cell Balancing Topology
  6. Online Current Distribution Algorithms
  7. Experimental Validation
  8. Results and Discussion
  9. Conclusions

Figures (11)

  • Figure 1: FOM with two $RC_{CPE}$ networks
  • Figure 2: (a) Independent cell balancing topology; (b) Differential cell balancing topology
  • Figure 3: Experimental setup
  • Figure 4: Flow chart of the PSO-GA method
  • Figure 5: Model validation for PSO-GA based battery parameter identification
  • ...and 6 more figures