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为缓解电动车辆出行过程中包括里程不足、充电时间长、充电站稀少以及电池循环寿命有限等固有问题,提高电动车辆的行驶性能以及驾驶员的接受程度,需要为其推荐合理的出行与充电方案。然而,目前出行方案制定方法没有考虑到交通环境复杂多变的特性,并且仅能提供在单一目标下的出行方案,难以为驾驶员提供综合考虑多种因素的出行策略。该文提出了一种在动态随机路网环境下的考虑多目标多约束的电动车辆出行规划策略。该出行规划策略考虑到交通环境的时变随机特性,利用多目标蚁群优化方法计算求解最优Pareto解集,为驾驶员推荐包括出行路径、各路径上的行驶速度、充电位置与模式、空调使用等出行要素。研究结果表明:基于动态随机路网的出行方案相比于基于静态确定性路网的出行方案更为优秀;相比于单一目标出行方案,基于多目标优化的出行策略综合性能更好。仿真结果证明了该方法能够协调各优化目标与约束条件,合理推荐电动车辆的出行方案解集,提升电动车辆的使用性能。
In order to alleviate the inherent problems such as lack of mileage, long charging time, scarcity of charging stations and limited cycle life of electric vehicles in the course of travel of electric vehicles and to improve the driving performance of electric vehicles and the acceptance of drivers, it is necessary to recommend a reasonable travel and charging plan . However, the current travel planning method does not take into account the complex and changing traffic environment, and can only provide travel planning under a single target. It is difficult to provide pilots with travel strategies that take various factors into consideration. This paper presents a strategy for travel planning of electric vehicles considering multi-objective and multi-constraint under dynamic stochastic road network. Taking into account the time-varying random characteristics of traffic environment, the travel planning strategy uses the multi-objective ant colony optimization method to calculate the optimal Pareto solution set. The proposed route includes the travel route, the travel speed of each route, the charging location and mode, Use other travel elements. The research results show that the travel scheme based on dynamic stochastic road network is superior to the travel scheme based on static deterministic road network. Compared with the single-target travel scheme, the travel strategy based on multi-objective optimization has a better overall performance. The simulation results show that this method can coordinate all the optimization objectives and constraints, and can reasonably recommend the solution set of travel plans for electric vehicles to improve the performance of electric vehicles.