Abstract：Reliable routing and charging plans are needed for fully electric vehicles (FEVs) to ease the intrinsic drawbacks of FEVs, such as mileage limitations, overly long charging times, limited charging stations, and limited battery lifetimes to enhance the driving performance and drivers' acceptance. However, most research has overlooked the effects of changing traffic system features and has provided travelling strategies for only a single travelling objective rather than strategies that consider multi-factors simultaneously. This paper describes a multi-objective, multi-constraint travelling plan optimization strategy for dynamic time-dependent traffic networks for FEVs. This optimization strategy includes the time-dependent stochastic changes of the traffic environment with the multi-objective ant colony optimization method used to calculate the optimal Pareto travelling plan set. The result provides drivers with information including travelling route, travelling speed on each road, charging locations and modes, and air-conditioner usage. The results show that a travelling plan within a dynamic time-dependent traffic environment is better than that within a stable deterministic traffic environment. The travelling result with multi-objective optimization is better than a single objective strategy. This multi-objective, multi-constraint optimization method provides reasonable travelling plans that enhance the driving performance of FEVs.
张书玮, 罗禹贡, 李克强. 动态交通环境下的纯电动车辆多目标出行规划[J]. 清华大学学报（自然科学版）, 2016, 56(2): 130-136.
ZHANG Shuwei, LUO Yugong, LI Keqiang. Multi-objective optimization for traveling plan of fully electric vehicles in dynamic traffic environments. Journal of Tsinghua University(Science and Technology), 2016, 56(2): 130-136.
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