Research On Path Optimization And Effectiveness Of Multi-Vehicle Electric Vehicles Under Two Charging Strategies

Urban distribution is an important area to protect and improve people’s livelihood,a key link in the development of modern logistics,and a basic support to ensure the normal operation of urban economy and society.Realizing the transformation of logistics and distribution vehicles from fuel vehicles to electric vehicles is of great importance for urban environmental protection and alleviating the demand for petroleum resources.Due to the short range and long charging time of electric vehicles,the use of different charging strategies during distribution has an important impact on the effectiveness of logistics distribution.Based on improving the efficiency of electric vehicles in urban logistics distribution and reducing the distribution cost of logistics enterprises,this paper studies the electric vehicle path problem considering different charging strategies.In order to complete the distribution task and distribute the items to each customer demand point with minimum cost,a reasonable distribution plan needs to be developed,including the planning of distribution routes,the selection of distribution models,and the adoption of charging strategies.In the previous studies of electric vehicle route planning,only a single factor is often considered,and the obtained distribution plan differs greatly from the actual situation.In order to develop a more realistic distribution scheme,the multi-model characteristics of logistics enterprises and the time requirements of customers are considered in the basic electric vehicle path problem,different charging strategies of electric vehicles are flexibly used,and the electric vehicle path planning is considered as a hybrid variant problem,and a multi-model electric vehicle path problem model with time windows for full charging strategy and partial charging strategy is constructed.The established EV path optimization model needs to satisfy multiple constraints at the same time,including the node access constraints in the path,the time window constraints at customer points,the load constraints of the EV and the battery power constraints of the EV.For this hybrid variant optimization problem,a hybrid particle swarm algorithm with adaptive weights is designed to solve the model based on the particle swarm algorithm with an improved strategy of adaptive inertia weights,while three local neighborhood structures are embedded to enhance the search capability of the algorithm.The validity of the algorithm is verified by Solomon dataset,and the RC2 example is constructed to simulate and analyze under two charging strategies.The results show that compared with the full charging strategy,the partial charging strategy can effectively reduce the number of vehicles used,reduce the vehicle driving distance,improve the vehicle full load rate,and reduce the distribution cost,and verify the accuracy of the model.Finally,the urban distribution problem of company C is studied,and the hybrid particle swarm algorithm with adaptive weights is used to solve the distribution scheme under two charging strategies.The results show that the partial charging strategy can reduce the distribution cost by 7.17%,reduce the number of vehicles used by 10%,and improve the average vehicle full load rate by 7.92%.In addition,the impact on the total distribution cost is analyzed in terms of the fixed and variable costs of electric vehicle operation.