scholarly journals Integrated Optimization of Routing and Energy Management for Electric Vehicles in Delivery Scheduling

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1762
Author(s):  
Lixing Wang ◽  
Zhenning Wu ◽  
Changyong Cao
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Energy Management ◽  
Electric Vehicles ◽  
Time Windows ◽  
Case Based Reasoning ◽  
Dynamic Adjustment ◽  
Routing Problem ◽  
Battery Capacity ◽  
Delivery Scheduling

At present, electric vehicles (EVs) are attracting increasing attention and have great potential for replacing fossil-fueled vehicles, especially for logistics applications. However, energy management for EVs is essential for them to be advantageous owing to their limitations with regard to battery capacity and recharging times. Therefore, inefficiencies can be expected for EV-based logistical operations without an energy management plan, which is not necessarily considered in traditional routing exercises. In this study, for the logistics application of EVs to manage energy and schedule the vehicle route, a system is proposed. The system comprises two parts: (1) a case-based reasoning subsystem to forecast the energy consumption and travel time for each route section, and (2) a genetic algorithm to optimize vehicle routing with an energy consumption situation as a new constraint. A dynamic adjustment algorithm is also adopted to achieve a rapid response to accidents in which the vehicles might be involved. Finally, a simulation is performed to test the system by adjusting the data from the vehicle routing problem with time windows. Solomon benchmarks are used for the validations. The analysis results show that the proposed vehicle management system is more economical than the traditional method.

scholarly journals The Mobile Charging Vehicle Routing Problem with Time Windows and Recharging Services

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shaohua Cui ◽  
Hui Zhao ◽  
Hui Chen ◽  
Cuiping Zhang
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicles ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Routing Problem ◽  
Battery Electric Vehicles ◽  
Battery Capacity

For the environmental friendliness of the technology on battery electric vehicles, there is growing attention on it. However, the market share of battery electric vehicles remains low due to the range anxiety. As a remedy, the mobile charging services could offer charging service at any time or locations requested. For profitability of the services, the operator should route the charging vehicles in a more efficient manner. For this consideration, we formulate the mobile charging vehicle routing problem as a mixed integer linear program based on the classical vehicle routing problem with time windows. To demonstrate the model, test instances are designed and computational results are presented. In order to examine the change of the number of mobile charging vehicles and travel distance, sensitivity analyses, such as battery capacity and recharging rate, are performed. The results show that larger battery capacity, quicker charging rate, or higher service efficiency could decrease the number of mobile charging vehicles and total traveled distances, respectively.

Metaheuristics for solving the vehicle routing problem with the time windows and energy consumption in cold chain logistics

2020 ◽  
Vol 95 ◽  
pp. 106561 ◽  
Author(s):  
Mei-xian Song ◽  
Jun-qing Li ◽  
Yun-qi Han ◽  
Yu-yan Han ◽  
Li-li Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Cold Chain ◽  
Routing Problem

scholarly journals The Electric Vehicle Routing Problem with Time Windows under Travel Time Uncertainty

2021 ◽  
Vol 2095 (1) ◽  
pp. 012032
Author(s):  
Dan Wang ◽  
Hong Zhou
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

Abstract Due to environmental friendliness, electric vehicles have become more and more popular nowadays in the transportation system. For many express companies, it is more and more important to meet the predetermined time window of customers. The uncertainty in travel times often causes uncertain energy consumption and uncertain recharging time, thus electric vehicles may miss the time windows of customers. Therefore, this paper addresses the electric vehicle routing problem with time windows under travel time uncertainty, which aims to determine the optimal delivery strategy under travel time uncertainty. To solve this problem, a robust optimization model is built based on the route-dependent uncertainty sets. However, considering the complexity of the problem, the robust model can only solve few instances including the small number of customers. Thus, a hybrid metaheuristic consisting of the adaptive large neighborhood search algorithm and the local search algorithm is proposed. The results show that the algorithm can obtain the optimal solution for the small-sized instances and the large-sized instances.

scholarly journals Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 285
Author(s):  
Tomislav Erdelić ◽  
Tonči Carić
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Time Windows ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Charging Station ◽  
Routing Optimization ◽  
Mode Of Transport

With the rise of the electric vehicle market share, many logistic companies have started to use electric vehicles for goods delivery. Compared to the vehicles with an internal combustion engine, electric vehicles are considered as a cleaner mode of transport that can reduce greenhouse gas emissions. As electric vehicles have a shorter driving range and have to visit charging stations to replenish their energy, the efficient routing plan is harder to achieve. In this paper, the Electric Vehicle Routing Problem with Time Windows (EVRPTW), which deals with the routing of electric vehicles for the purpose of goods delivery, is observed. Two recharge policies are considered: full recharge and partial recharge. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) metaheuristic based on the ruin-recreate strategy is coupled with a new initial solution heuristic, local search, route removal, and exact procedure for optimal charging station placement. The procedure for the O(1) evaluation in EVRPTW with partial and full recharge strategies is presented. The ALNS was able to find 38 new best solutions on benchmark EVRPTW instances. The results also indicate the benefits and drawbacks of using a partial recharge strategy compared to the full recharge strategy.

scholarly journals Bi-Objective Optimization for Vehicle Routing Problems with a Mixed Fleet of Conventional and Electric Vehicles and Soft Time Windows

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peixin Zhao ◽  
Fanfan Liu ◽  
Yuanyuan Guo ◽  
Xiaoyang Duan ◽  
Yunshu Zhang
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicles ◽  
Programming Model ◽  
Time Windows ◽  
Nsga Ii ◽  
Weighting Method ◽  
Routing Problem ◽  
Penalty Cost ◽  
Soft Time Windows ◽  
The Impact

With the growing interest in environmental protection and congestion, electric vehicles are increasingly becoming the important transportation means. However, electric vehicles currently face several adoption barriers including high purchasing price and limited travelling range, so the fleets where electric vehicles and conventional vehicles coexist are closer to the current fleet management status. Considering the impact of charging facilities and carbon emission, this paper proposes a vehicle routing problem with a mixed fleet of conventional and electric vehicles and soft time windows. A bi-objective programming model is established to minimize total operational cost and time penalty cost. Finally, the nondominated sorting genetic algorithm II (NSGA-II) is employed to deal with this problem. Furthermore, single-objective optimization is carried out for the two objectives, respectively, and the linear weighting method is also used to solve the problem. Through the contrast of these results and the NSGA-II results, the effectiveness of the algorithm in this paper is further verified. The results indicate that two objectives are contradictory to some extent and decision-makers need a trade-off between two objectives.

scholarly journals Multi-Objective Energy Management and Charging Strategy for Electric Bus Fleets in Cities Using Various ECO Strategies

2021 ◽  
Vol 13 (14) ◽  
pp. 7865
Author(s):  
Mohammed Mahedi Hasan ◽  
Nikos Avramis ◽  
Mikaela Ranta ◽  
Andoni Saez-de-Ibarra ◽  
Mohamed El Baghdadi ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Thermal Management ◽  
Energy Management ◽  
Management Strategy ◽  
Energy Requirement ◽  
Optimization Technique ◽  
Total Cost ◽  
Multi Objective ◽  
Battery Capacity ◽  
Eco Features

The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a “brute-force” Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%.

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