Multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state

This study proposes a multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state. By integrating rule-based control with multi-objective optimization methods, the strategy aims to improve system efficiency, extend the lifespan of proton exchange membrane fuel cells (PEMFC), and reduce operating costs. Based on a comprehensive model of PEMFC output characteristics and lifetime degradation, this study introduces an optimized point line (OPL) strategy. This strategy dynamically adjusts operating constraints according to the state of health (SOH) of the PEMFC, ensuring optimal vehicle performance throughout its lifecycle. To optimize the OPL strategy parameters, a particle swarm optimization algorithm with compression factor was employed, enhancing the strategy's optimization efficiency, adaptability, and robustness to better handle various real-world operating conditions. The strategy was evaluated under US06 and WLTC driving cycles and compared with traditional power following (PF) and point line (PL) strategies. Results show that compared to the PL strategy, the OPL strategy achieved a 36.4% and 34.2% reduction in operating costs under US06 and WLTC cycles, respectively. Moreover, PEMFC lifetime degradation decreased by 44.9% and 39.4% in these cycles. In high-power regions, the average operating efficiency of PEMFC improved by 2%. The strategy demonstrated good adaptability to different driving conditions, providing an effective solution for optimizing the performance and durability of fuel cell hybrid electric vehicles.