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

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%.

• The paper introduces an optimization-based active cell balancing strategy that mitigates cell voltage disparities to extend battery life.
• It employs a fractional-order model combined with a hybrid PSO-GA algorithm for precise state estimation and current distribution control.
• Experimental validation under UDDS profiles demonstrated a 3.2% increase in operational time, confirming the method's effectiveness.

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

The paper "Active Cell Balancing for Extended Operational Time of Lithium-Ion Battery Systems in Energy Storage Applications" presents an optimization-driven methodology for active cell balancing to address the challenge of cell inconsistency, which constrains the operational capacity of lithium-ion battery systems. The research taps into fractional order modeling and advanced optimization algorithms to enhance system-level performance primarily in energy storage contexts.

Lithium-Ion Battery System and Cell Balancing

Lithium-ion batteries (LIBs) are a primary power source for ESSs, given their favorable energy density and longevity. The challenge lies in the inherent cell inconsistency which can originate from manufacturing discrepancies. This inconsistency results in imbalances in State of Charge (SOC) and terminal voltages, often constraining an entire system's capacity to that of its weakest cell. Consequently, cell balancing becomes critical to extend operational time efficiently. Active balancing emerges as a superior approach over passive balancing due to its ability to manage cell discrepancies during both charging and discharging cycles.

Proposed Methodology

The paper introduces a novel active balancing method, focusing on extending the operational time of LIB systems while satisfying power demands. Unlike traditional methods focused on balancing SOC or voltage, this approach embodies an optimization-centric active balancing method. This method employs a fractional-order model augmented by a Particle Swarm Optimization-Genetic Algorithm (PSO-GA) for parameter identification. Two cell-level balancing topologies are analyzed: independent cell balancing and differential cell balancing.

Model Development

The fractional-order model of the battery system is central in this approach, allowing it to forecast terminal voltage dynamics effectively. The model utilizes the Gr{\"u}nwald-Letnikov definition for fractional derivatives, and the dynamics are expressed as state-space equations representative of individual cells and extended to the entire battery system connected in series. The mathematical formulation enables a more precise state estimation and a refined model predictive control (MPC) strategy for current distribution across battery system cells.

Optimization-Based Control Strategy

For each balancing topology, a distinct control optimization strategy is laid out, solved using MPC. The independent cell balancing topology enables decentralized current control through isolated DC-DC converters, optimizing each cell's current relative to its SOC. The differential balancing topology, on the other hand, leverages DC-DC bypass converters parallel to cells, allowing differential energy exchange. Both strategies are designed to optimize minimum cell voltage and prolong operational duration.

Experimental Validation and Results

The proposed solution was verified experimentally using a two-series NCR18650B battery setup under Urban Dynamometer Driving Schedule (UDDS) profiles. The experiments demonstrated a 3.2% extension in operational time achieved through effective active balancing. The results substantiated the reduction in voltage disparity across cells and confirmed the enhanced performance and efficacy of the proposed PSO-GA-based optimization algorithm.

Numerical Results and Analysis

The experiments focused on analyzing the SOC estimation and current optimization under two topologies. The EKF-based SOC estimation achieved rapid convergence to actual SOC values despite initial estimation errors. Current distribution adjustments demonstrated through independent and differential topologies indicated that the independent balancing structure noticeably aligned cell voltages away from cut-off points, thus prolonging battery life.

Conclusion

The paper effectively demonstrates an advanced active balancing methodology leveraging fractional-order modeling and hybrid optimization algorithms for extending LIB system operational time. This research not only highlights an innovative control strategy but also provides an empirical foundation for future advancements in active cell balancing for enhanced energy storage applications. The numerical and experimental validations confirm the potential of sophisticated optimization techniques in achieving superior balancing performances, hinting at broader applications for large-scale battery systems in the renewable energy sector and beyond.