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

摘要：

新能源重型卡车换电模式因补能效率高，正逐步成为公路货运低碳转型的重要路径。本文系统梳理了新能源换电调度优化的研究现状，聚焦于调度优化的建模与求解方法。研究表明，换电调度建模需考虑经济效益、换电效率或功率稳定性目标，应对物理资源、时空匹配、操作规范及需求波动等多重限制，并通过滚动时域优化、充电功率动态调节及分时电价响应机制细化动态性处理、排队时长缩减及电网削峰填谷等约束表达；求解方法层面，数学规划方法在精确解获取上具有优势但受限于计算复杂度，启发式算法具备大规模问题求解效率但无最优性保证，强化学习方法凭借序列决策能力在动态场景中展现潜力但需强化安全性约束处理，实际应用中需根据问题特点设计混合式算法求解。

关键词: 新能源重型卡车, 换电调度, 优化, 建模, 算法