Integrated optimization of energy management and thermal control strategies for fuel-cell heavy truck

Abstract

The energy management strategy (EMS) and thermal management strategy (TMS) are interdependent and collectively influence the overall performance of hydrogen fuel cell hybrid power systems. This paper presents an integrated design and optimization approach for EMS/TMS in fuel cell-powered heavy-duty trucks (HDTs). A high-fidelity fuel cell model incorporating thermal dynamics and cooling system behavior is developed. Subsequently, fuzzy logic controllers with multi-dimensional inputs are designed to enable coordinated control of EMS and TMS. To systematically enhance the control performance of both EMS and TMS, an integrated optimization scheme with 82 parameters has been designed. An advanced hybrid population optimization algorithm, HPPSO, has been developed by combining Pelican Optimization Algorithm (POA) and Particle Swarm Optimization (PSO). HPPSO avoids local optima and improves performance during large-scale parameter optimization. Simulation results indicate that the proposed controller optimizes performance in both energy efficiency and temperature regulation, with improvements of 15.73 % and 27.63 %, respectively, compared to the existing rule-based controller. A comparison across different optimization schemes reveals that the proposed integrated optimization maximizes fuel consumption reduction and thermal control performance, with improvements of 2.70 % and 19.48 %, respectively, compared to sequential optimization. Furthermore, hardware-in-the-loop (HIL) experiments validate the adaptability of the proposed controller to diverse driving conditions.