Generative model-enabled lifelong reinforcement learning for robust energy management of fuel cell vehicles

Deep reinforcement learning has shown strong potential for the development of energy management strategies (EMSs) for fuel cell vehicles (FCVs). However, an EMS trained on fixed driving-cycle data may lose fuel economy competitiveness under evolving driving conditions. This suggests that EMS optimization should be revisited from a life-cycle perspective, particularly when the deployed EMS encounters driving cycles that cause severe performance degradation. A direct re-training strategy, however, may damage previously learned control knowledge and compromise performance under other conditions. To address this issue, this study proposes a generative model-enabled lifelong reinforcement learning (GM-LRL) framework for FCV energy management. A baseline soft actor-critic-based EMS is first developed on the standard cycle. The deployed EMS is then periodically diagnosed using a dynamic-programming-referenced relative optimality-gap criterion to identify the cycle on which the deployed EMS exhibits severe performance degradation. Based on this cycle, a physics-informed diffusion model is introduced to generate additional driving cycles, and an elastic-weight-consolidation-based lifelong re-training strategy is developed to improve adaptability while preserving previously acquired control knowledge. Experiments on a fuel cell heavy-duty vehicle show that the proposed method reduces the optimality-gap of origin EMS on the identified cycle by 6.19 percentage points. Moreover, it achieves an average fuel economy of 95.20% over 100 collected driving cycles, exceeding origin EMS and direct re-training by 3.14 and 3.21 percentage points, respectively. These results demonstrate that the proposed life-cycle optimization framework can both repair weak-performance regions and sustain robust EMS performance during long-term operation.