Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm

The eco-driving strategy based on deep reinforcement learning holds significant potential for achieving energy efficiency, longevity, and safety of fuel cell hybrid electric vehicle (FCHEV). This paper proposes a health-aware eco-driving strategy for FCHEV based on the soft actor-critic (SAC) algorithm. Building upon health awareness of multiple electric components including power battery, fuel cell, and driving motor, this eco-driving strategy integrates energy management system (EMS) and adaptive cruise control (ACC) to comprehensively optimize vehicle performance. By incorporating health-awareness into the eco-driving approach, this study aims to maximize the lifespan of electric components, enhance energy utilization efficiency, and ensure driving comfort and safety. SAC algorithm not only enhances optimization performance in complex nonlinear multi-objective optimization problems but also accommodates real-time control requirements under diverse driving conditions. The simulation results demonstrate that the proposed strategy achieves 0.41% reduction in H2 consumption and same level health maintenance of electric components compared with the dynamic programming benchmark of EMS, while maintaining the comfort within 6% of the gap but safer following performance compared with the intelligent driver model benchmark of ACC. Moreover, the comparative experiments demonstrate that the effectiveness and adaptability of proposed eco driving strategy.