Optimal Charging Planning with Energy Consumption Simulation for Battery Electric Long-Haul Trucks Aristotle University of Thessaloniki

Format:

Book

Conference/Event

Author/Creator:

Perdikopoulos, Michail, author.

Doulgeris, Stylianos, author.

Livitsanos, Georgios, author.

Kazakis, Thomas, author.

Mellios, Giorgos, author.

Ntziachristos, Leonidas, author.

Conference Name:

CO2 Reduction for Transportation Systems Conference (2026-06-09 : Turin, Italy)

Language:

English

Subjects (All):

Electric vehicles.

Fleets.

Energy consumption.

Vehicle charging.

Trucks.

Batteries.

Climate change mitigation.

Research and development.

Simulation and modeling.

Local Subjects:

Electric vehicles.

Fleets.

Energy consumption.

Vehicle charging.

Trucks.

Batteries.

Climate change mitigation.

Research and development.

Simulation and modeling.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Vehicle fleet decarbonization is a key objective for the coming years, with electrification representing the primary pathway to achieving the targets set by the European Union. The share of battery electric trucks in new registrations has been gradually increasing especially in light and medium size trucks. The replacement rate of diesel long-haul trucks with zero emission trucks is still low due to challenges posed by added complexity and limitations of battery charging. Depot overnight charging is not sufficient to cover the energy needs of a truck covering large distances and careful planning of the route using public charging infrastructure is crucial for an optimized route minimizing extra costs and range anxiety. The current work aims to develop a methodology to propose the optimal charging locations for a given route of a battery electric truck based on nearby stations along the route. Our study uses an open-source optimization algorithm for the fixed route vehicle charging problem coupled with a powertrain simulation model that is used to calculate the energy consumption and the electric range of the vehicles. A variety of constraints, such as initial State of Charge, lowest allowed State of Charge threshold, maximum trip duration, distance deviation, have been implemented in different scenarios from real world locations with a goal to investigate the impact of planning constraints and charging infrastructure in the optimal planning of electric truck routing. The results of our analysis indicate that the integration of an accurate energy consumption calculation model to a route and charging optimisation algorithm can be proven beneficial for minimizing the time penalty due to charging

Notes:

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