关键词: 新能源重型卡车, 换电调度, 优化, 建模, 算法

Abstract:

Owing to its high refueling efficiency, the battery-swapping mode for new-energy heavy-duty trucks is rapidly becoming a key pathway for the low-carbon transformation of road freight transport. This paper systematically reviewed the current research on scheduling optimization for battery swapping focusing on modeling techniques and solution approaches. Studies showed that optimization models must balance economic benefits, swapping efficiency or power stability while accommodating multiple constraints such as physical resources, spatiotemporal matching, operational protocols and demand fluctuations. Dynamic aspects, including queue-length reduction, peak-load shifting and time-of-use pricing, were handled via rolling-horizon optimization, dynamic charging-power adjustment and demand-response mechanisms. Regarding solution methodologies, exact mathematical programming exceled in optimality yet suffered from computational complexity; heuristic algorithms scaled well for large-scale problems but lacked optimality guarantees; reinforcement learning demonstrates promised in dynamic settings through sequential decision-making, although safety constraints still required reinforcement. In practice, hybrid algorithms tailored to the problem's characteristics were recommended.

Key words: new-energy heavy-duty truck, battery-swapping scheduling, optimization, modeling, algorithms