Automotive energy management systems (EMS) are advancing towards comprehensive lifecycle-intelligence, spanning from product development phase to customer usage phase. This paper proposes a bi-level transfer approach with model-agnostic meta-learning (MAML) to realize cross-platform transferable and online-adaptive EMS. During the development level, MAML is adopted to calibrate the heuristic control maps of an instantaneous optimization-based EMS, with a per-unit state and action space design facilitating knowledge transfer. Leveraging user data, online adaptation is carried out with reliance on digital twin-based cloud computing, updating onboard controller parameters. The effectiveness of the proposed lifecycle intelligent EMS is validated through real vehicle experiments. Firstly, the entire MAML-assisted V-cycle development process is finished to validate the optimality and knowledge transfer of the EMS, resulting in zero-shot transfer for EMS calibration on new vehicle products. Additionally, real vehicle experimental tests show that a correction of 8.0%~9.5% fuel economy is improved against the convention reinforcement learning-based EMS during usage via online-adaptation, effectively bridging the optimality gap between the control policy learned in development and the global optimal control in actual driving scenarios.

The synergy of reinforcement learning (RL)-based energy management and vehicle-to-everything communication has been proved effective in boosting the fuel economy of connected plug-in hybrid electric vehicles (PHEVs). However, the intricate coupling of mechanical, electrical, thermal states and driving cycle results in a high-dimensional complex energy control problem for PHEVs, which is challenging to optimally solve within the same time scale. To this end, this study designs a multi-horizon reinforcement learning (MHRL)-based energy management of PHEVs, aware of the traffic preview from intelligent transportation systems to optimize the energy flow and thermal states as well as the transient dynamics of the powertrain. The proposed strategy features a novel state space representation, and solves the coordinated training among multiple sub-networks belonging to different control tasks in various time scales. Simulation and hardware-in-the-loop experiments are carried out based on a standard driving cycle and a real-world driving cycle with real-time traffic data demonstrate that the MHRL strategy improves fuel economy by 3.0%~7.9% compared to conventional RL-based energy management under various coolant temperature conditions and dynamic driving scenarios.

Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning