The adoption of heavy-duty electric vehicles (HDEVs) presents significant challenges in trip planning due to high energy consumption, limited battery capacity, and the reduced availability of high-power charging stations. Unlike passenger EVs, HDEVs require careful coordination of routing and charging decisions to ensure both operational efficiency and feasibility. This work proposes a graph-based optimization framework for eco-charging strategies that jointly optimizes energy consumption, travel time, and charging costs while considering road, traffic, and infrastructure constraints.
The methodology relies on constructing first a routing graph, where nodes represent waypoints, and edges represent road links with attributes such as speed limits, traffic conditions, and energy consumption. This graph is then extended into a hypergraph, where nodes incorporate information about both vehicle location and state-of-charge (SOC), while edges represent either driving or charging actions. The hypergraph formulation enables a unified treatment of routing and charging decisions, accurately modeling detours, charging station availability, and SOC evolution.
To generate realistic speed and consumption profiles, a physics-informed vehicle model is employed, integrating acceleration constraints, road gradients, and regenerative braking effects. The final trip plan is determined by solving a shortest-path problem on the hypergraph, where edge costs reflect a weighted combination of energy efficiency, travel time, and economic factors.
Simulation results demonstrate the effectiveness of the proposed approach, achieving significant improvements in energy efficiency and cost savings compared to naive routing strategies. This framework provides a scalable solution for HDEV fleet operators, ensuring optimized trip planning in real-world electromobility scenarios.