Multi-objective predictive cruise control for electric heavy-duty trucks considering fleet battery swapping under cyber-physical system

Electric truck fleets require battery swapping to continue transportation tasks. However, due to the inability to predict battery swap station electricity prices and queuing times in advance, drivers find it difficult to plan and control their speed effectively. To address this, this paper first establishes a multi-objective driving planning model for the truck fleet, considering factors such as electricity prices, queuing times at swap stations, and energy consumption, to optimize the battery swapping time window for the trucks. Subsequently, a Vehicle-to-Cloud (V2C) hierarchical architecture based on Cyber-Physical System (CPS) is proposed, in which predictive cruise control for the trucks is deployed. This system enables dynamic speed adjustments through real-time information exchange between the cloud and vehicles, improving the economic efficiency of the fleet during battery swaps. Finally, a simulation based on real battery swap scenarios is conducted to compare the fleet's journey to a swap station. The results show that, compared to the commonly used constant-speed cruise control system, the proposed cloud-supported predictive cruise control (PCC) system reduces battery swapping costs 8.81%-9.01 % and driving energy consumption 10.48%-15.56 %.