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A Robust Optimization Framework for Lithium-Ion Battery Parameter Identification Using Red-Tailed Hawk Algorithm
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Keywords
Lithium-ion battery
Red-Tailed Hawk Algorithm
Parameter identification
Metaheuristic optimization
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Abstract
Accurate parameter identification in lithium-ion (Li-ion) batteries is critical for performance optimization, safety assurance, and longevity enhancement in electric vehicle (EV) and energy storage applications. Conventional methods for battery parameter estimation often struggle with nonlinearities, high-dimensional parameter spaces, and environmental uncertainties. This research proposes a robust optimization framework utilizing the Red-Tailed Hawk Algorithm (RTHA) for precise parameter identification of Li-ion batteries. The framework leverages the algorithm's metaheuristic capabilities to navigate complex, multimodal search spaces and achieve global optimization efficiently. Extensive simulations demonstrate the effectiveness of the RTHA-based method in identifying battery equivalent circuit model parameters, outperforming classical particle swarm optimization and coot-inspired approaches (A. Faramarzi et al., 2020; S. Ferahtia et al., 2023). Results indicate improved convergence speed, enhanced accuracy in state-of-charge (SoC) and state-of-health (SoH) estimation, and resilience to measurement noise. The study contributes a computationally efficient, generalizable framework for battery modeling, offering practical utility for both EV battery management systems and stationary energy storage evaluation.
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