Enhanced eco-driving and energy management for heterogeneous hybrid electric vehicles through integrated transfer learning and deep reinforcement learning

Eco-driving and energy management strategy (EMS) significantly influence the real-world performance of hybrid electric vehicles (HEVs). Traditional approaches often optimize eco-driving and energy management strategies separately using layered optimization, which frequently results in suboptimal outcomes. Moreover, transferring eco-driving and energy management strategies between heterogeneous HEVs remains challenging. To address these issues, this paper proposes a collaborative optimization strategy for eco-driving and EMS leveraging transfer learning (TL) and deep reinforcement learning (DRL). The proposed framework not only enhances optimization performance but also improves the generalizability of reinforcement learning methods across different HEV types. Firstly, models of heterogeneous hybrid electric vehicles (HEVs) and traffic signalized lights were constructed. Secondly, an eco-driving and energy management agent for fuel cell hybrid electric vehicles (FCHEVs) was constructed through the utilization of the soft actor-critic (SAC) algorithm. Knowledge transfer via TL was then employed to establish the TL-SAC eco-driving and EMS agent for power-split hybrid electric vehicles (PSHEVs). Compared to conventional SAC-based method, the proposed TL-SAC strategy achieved equivalent fuel reductions of 16.10 %, 10.57 %, and 4.76 % under multi-intersection scenarios. Compared with the PMP algorithm, the proposed TL-SAC strategy reduces energy consumption of PSHEV by 11.88 %, 3.73 % and 4.16 % in three generalization testing scenarios respectively, while achieving close travel time and better real-time performance. This study provides a novel and transferable approach to co-optimize eco-driving and EMS, paving the way for broader deployment of diverse HEVs.