Researchers Yilang Hao and Zhibin Chen from the University of California, Santa Barbara have been exploring ways to optimize the operations of electric trucks, particularly focusing on platooning and charging strategies. Their work aims to address the challenges posed by the limited range of electric trucks and the need for strategic charging, especially on mid- to long-haul routes.
Electric trucks are becoming more common as the trucking industry seeks to reduce its carbon footprint. However, their limited range and the need for charging present significant operational challenges. Truck platooning, where trucks travel closely together to save energy, can help mitigate range anxiety. This energy savings can influence routing and charging decisions. Most existing studies on truck platooning focus on single highway corridors and do not capture the complexities of network-wide operations.
In their research, Hao and Chen studied electric truck platooning on a general road network. They considered various factors such as route selection, charging station choices with different prices and charging speeds, and the formation of platoons on shared routes. They also explored the idea of trucks taking detours to balance platoon savings with additional labor hours. Additionally, they allowed for in-platoon position swaps, enabling leading responsibility to rotate among trucks, thereby balancing battery usage and avoiding early depletion of any single truck’s battery.
To optimize routing paths, charging-station choices, labor time, and platoon formation and position swaps, the researchers formulated a mixed-integer linear program (MILP). Recognizing that exact methods become intractable on realistic instances, they developed an Adaptive Large Neighborhood Search (ALNS) algorithm. This algorithm was enhanced with a savings-based bounding scheme, infeasible-pair elimination, and candidate-station filtering.
Computational experiments on test instances with up to 150 trucks showed that incorporating platooning can reduce total operational costs by up to 2.77 percent. The proposed algorithm significantly cut computation time by up to 99.96 percent compared with CPLEX, solving 150-truck instances in about 120 seconds. These results indicate strong potential for real-world applications, offering practical solutions for the energy and transportation sectors.
This research was published in the journal Transportation Research Part B: Methodological, providing valuable insights for optimizing electric truck operations and enhancing the efficiency of the trucking industry.
This article is based on research available at arXiv.