Work

Learning-augmented optimization and control of long-haul mobility propulsion systems

Year: 2026

Type: article

Abstract: Decarbonizing the long-haul transportation sector is a critical global challenge. This paper introduces a hierarchical, learning-augmented framework for co-designing an ammonia-hydrogen hybrid electric powertrain as a viable carbon-free propulsion solution. At the core of the proposed framework is the tight coupling between online control and offline design. In the online supervisory control layer, a deep reinforcement learning (DRL) agent is trained to make real-time decisions on power split and on-board hydrogen production. In the offline propulsion system optimization layer, a DRL-augmented adaptive non-dominated sorting genetic algorithm (RL-ANSGA) is employed to solve the multi-objective component sizing problem, with each candidate design evaluated in high-fidelity co-simulation under a fixed, pre-trained DRL energy management policy. Results demonstrate that the optimized ammonia-hydrogen vehicle outperforms conventional diesel and diesel-hybrid counterparts in energy efficiency and well-to-wheel carbon emissions.

Source: iScience

DOI: 10.1016/j.isci.2026.115579

Authors: Mubai Ding, Yisen Li, Chaoyi Chen, Nuo Lei, Hao Zhang

Institutions: Fudan University, University of Pennsylvania, Tsinghua University, Carnegie Mellon University

Language: English

Topic: Electric and Hybrid Vehicle Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,240