Hierarchical Energy Management for Fuel Cell Electric Vehicles with Adaptive-Modality Deep Deterministic Policy Gradient

Abstract

Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality Deep Deterministic Policy Gradient and Model Predictive Control hierarchical energy management strategy (AMDDPG–MPC HEMS). In the proposed architecture, the upper-level AMDDPG controller identifies driving-condition patterns and generates adaptive weights for hydrogen consumption, battery power, and state-of-charge regulation, while the lower-level MPC controller performs constrained power allocation between the fuel cell and battery. To improve adaptability, the AMDDPG algorithm incorporates an adaptive modality perception mechanism that extracts driving-condition features and a multi-scale reward mechanism that coordinates short-term energy-saving objectives with long-term component-protection requirements. A dedicated weight-scheduling and switching mechanism is also introduced to ensure smooth transitions between operating conditions. The proposed strategy is evaluated under the World Light Vehicle Test Cycle and Urban Dynamometer Driving Schedule and compared with rule-based, equivalent consumption minimization, and fixed-weight MPC strategies. The results show that the AMDDPG–MPC HEMS achieves the lowest equivalent hydrogen consumption, with reductions of 18.853% and 11.732% relative to the rule-based strategy under the two driving cycles, respectively. It also improves fuel-cell operating efficiency and maintains feasible battery SOC regulation. These results demonstrate the effectiveness and engineering potential of the proposed hierarchical energy management strategy.