Multi‐Constraint SOP Estimation for Battery‐Supercapacitor Hybrid Energy Storage Systems Based on Temperature‐Dependent GA‐ELM SOC Prediction

ABSTRACT Hybrid battery capacitor (HBC) devices combine high energy density with high power density and therefore have broad application prospects in new energy vehicles and energy storage systems. However, their electrochemical dynamics are complex and strongly affected by temperature, making it difficult for conventional state‐of‐charge (SOC) and state‐of‐power (SOP) estimation methods to maintain accuracy over wide temperature ranges and under dynamic operating conditions. In this paper, an 8 Ah HBC cell is investigated. An electro–thermal coupled model is established, and its parameters are identified using an adaptive forgetting‐factor recursive least‐squares (AFFRLS) algorithm, yielding highly accurate voltage and temperature estimates. On this basis, an SOC estimation method that incorporates surface temperature and employs a genetic algorithm optimized extreme learning machine (GA‐ELM) is proposed, which achieves superior estimation accuracy and robustness over a wide temperature range and under multiple operating conditions. Furthermore, a multi‐constraint SOP estimation method is developed by simultaneously considering SOC constraints, terminal voltage constraints, and allowable operating‐condition limits of the cell. Experimental results show that the proposed method maintains SOP estimation errors within 5% across different temperatures and time scales, and such high‐accuracy SOP estimation ensures the safe and efficient operation of the HBC.