Refrigerated electric vehicle routing considering time-dependent temperature energy consumption models
研究将时变温度引入冷藏电动车辆路径规划，发现考虑温度变化可使制冷能耗估算更准确，差异最高达16%，且制冷占总能耗约44%，优化后显著改变路线选择。
Abstract This paper introduces a novel model integrating the Traveling Salesman Problem (TSP) with time-dependent cooling energy consumption, considering external temperature variations and including an optimal route start time decision variable to enhance management capacity. Perishable items like fresh food and life science products require efficient cold chain logistics, and using zero-emission electric vehicles offers an eco-friendly alternative but with operational challenges, particularly in energy efficiency and battery autonomy. The main aim of this research is to show that introducing temperature as a time-dependent variable in route design offers a more accurate approximation of energy consumption in a refrigerated fleet that is primarily influenced by the internal–external temperature differential. Results show that considering time-dependent temperature variations provides more accurate refrigeration consumption estimates, with differences up to 16% compared to average temperature models. Refrigeration accounts for about 44% of total energy consumption, with product load and infiltration contributing 80% and 17%, respectively. Optimizing these consumptions significantly alters route planning, highlighting the importance of time-dependent temperature in effective logistics management.