RLS-MAMBA: Enhanced SOC Estimation and Parameter Identification for Lithium-Ion Batteries

This work presents the RLS-MAMBA method for lithium-ion battery State of Charge (SOC) estimation. The method integrates Recursive Least Squares (RLS) for dynamic parameter identification with the Mamba neural network for real-time error correction. RLS efficiently updates battery model parameters in real-time, ensuring accurate and adaptive parameter estimation under dynamic operating conditions. The Mamba neural network captures the nonlinear dynamics and long-range dependencies inherent in battery behavior, providing an accurate SOC estimation framework. Experiments with various initial SOC values demonstrate that this method achieves SOC estimation accuracy exceeding 97.56%, with a maximum accuracy of up to 99.98%.