Enhancing energy management of fuel cell buses: An intelligent strategy for passenger load variation

Energy management is crucial for improving fuel cell bus efficiency, yet learning-based strategies may become unstable when passenger-load-induced mass variations change traction power demand. This paper proposes a mass-adaptive intelligent energy management strategy without explicit online mass estimation. A joint state-augmentation and reward-shaping mechanism parameterizes the deviation between current traction-power demand and the empty-mass baseline, guiding the agent to adjust the fuel-cell/battery power split to compensate for this mass-induced power gap, so that a single policy remains adaptive across different vehicle masses. An embedded attention module identifies key state variables and visualizes decision priorities under different driving conditions. Simulation results show that the proposed method achieves energy savings of 6.85% and 6.47% compared with the baseline strategy while maintaining stable SoC. Furthermore, the approach shows strong algorithmic generalization within both DDPG and SAC frameworks, achieving adaptive response to mass variations and generalized transfer of energy management strategies.