Adaptive State of Charge and State of Health Estimation through Optimized Deep Reinforcement Learning with Dynamic Agent Selection and Test-Time Training Tailored to Dynamic Operating Conditions
Abstract Accurately estimating the state of charge (SOC) and state of health (SOH) of lithium−ion batteries under dynamic conditions is critical for electric vehicles (EVs) and energy storage systems; however, nonlinearity and uncertainties hinder traditional methods. This paper proposes an adaptive estimation framewor...