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Public charging station localization and route planning of electric vehicles considering the operational strategy: A bi-level optimizing approach
Abstract
In recent years, electric vehicles (EVs) have increased considerably in the logistics sector with the implementation of greenhouse gas (GHG) emission regulations. However, the driving range of EVs is limited by the battery capacity compared to combustion engine vehicles. This study proposes a public charging infrastructure localization and route planning strategy for logistics companies based on a bilevel program. A two-phase heuristic approach combining a two-layer genetic algorithm (TLGA) and simulated annealing (SA) is presented to solve the problem. The hybrid method uses TLGA to derive the optimal routing and charging plan and the SA descent algorithm is used to select the charging station (CS) locations. The proposed method is tested and compared to meta-heuristics using benchmark instances with charging stations. A case study is carried out using data from Chengdu, a major city in southwest China, to simulate the charging demand of public charging infrastructures. The proposed method provides more feasible allocations for public CSs and route planning, which could reduce the total delivery cost by 15%. This study demonstrates the potential of a bilevel optimizing approach to provide an optimized solution in citywide CS location selection and logistics routing problems.
In the context of carbon neutralization, the electric vehicle and energy storage market is growing rapidly. As a result, battery recycling is an important work with the consideration of the advent of battery retirement and resource constraints, environmental factors, resource regional constraints, and price factors. Based on the theoretical research of intelligent algorithm and mathematical models, an integer programming model of urban power battery reverse supply chain scheduling was established with the goal of the highest customer satisfaction and the least total cost of logistics and distribution, to study the influence of the resources and operation status of a built city recycling center and dismantling center on the power battery reverse supply chain. The model includes vehicle load, customer demand point satisfaction range, and service capacity constraints. This study collected regional image data, conducted image analysis, and further designed an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization algorithm suitable to solve the global optimization problem by introducing the improvement strategy of convergence rate, particle search, and the traditional elite individual retention. The results verified the practicability of the model, the global optimization ability of the algorithm to solve the problem, and the operation speed through comparing the results obtained from the basic algorithm. A reasonable comprehensive solution for the location and path optimization of the urban recycling center was also obtained. Multi-objective optimization was carried out in vehicle scheduling, facility construction, and customer satisfaction construction. The basic algorithm and integrated optimization software were compared. We found that the model and the scheme provided by the algorithm can significantly reduce the operation cost of the enterprise. This research provided new insights for enterprises to effectively utilize resources and optimize the reverse supply chain scheduling of an urban power battery.
While electric vehicles (EVs) are expected to support decarbonizing transport, EVs can challenge the electricity system. Investigating the EV charging load and its flexibility, e.g., by shifting load, is therefore crucial to ensure a secure and sustainable energy system. We develop an agent-based model to investigate how different plug-in behaviors can affect (future) EV charging load profiles and their spatial–temporal flexibility. We contribute to extant literature by (1) revealing the effect of diverse plug-in behaviors on EV load profiles, particularly the flexibility potential resulting from different plug-in behaviors; (2) presenting the (future) charging load in different spatial structures, i.e. urban, rural, or suburban, and home, work, or public charging locations; and (3) demonstrating the effect of detailed driving profiles in high spatial and temporal resolution. We implement three future scenarios regarding EV and charging infrastructure diffusion and technology developments. We find that the impact of potential changes in plug-in behavior on EV charging load would be highest for urban areas and increases as charging infrastructure becomes more spatially diversified. Decision-makers in policy and industry can use these insights to evaluate the impact of EV charging on distribution grids and design incentives to leverage the flexibility potential of EVs.
The unprecedented growth of global cities together with increased population mobility and a heightened concern regarding climate change and energy independence have increased interest in electric vehicles (EVs) as one means to address these challenges. The development of a public charging infrastructure network is a key element for promoting EVs, and with them reducing greenhouse gas emissions attributable to the operation of conventional cars and improving the local environment through reductions in air pollution. This paper discusses the effectiveness, efficiency, and feasibility of city strategic plans for establishing a public charging infrastructure network to encourage the uptake and use of EVs. A holistic analysis based on the Value Creation Ecosystem (VCE) and the City Model Canvas (CMC) is used to visualise how such plans may offer public value with a long-term and sustainable approach. The charging infrastructure network implementation strategy of two major European cities, Nantes (France) and Hamburg (Germany), are analysed and the results indicate the need to involve a wide range of public and private stakeholders in the metropolitan areas. Additionally, relevant, and fundamental patterns and recommendations are provided, which may help other public managers effectively implement this service and scale-up its use and business model.
Switching to electricity in the ground transport sector is considered a promising way to achieve the energy transition and CO2 emission reductions required to meet China's carbon neutral target by 2060. In this study, a transport energy model containing an elaborate transport demand model and a technology bottom-up model for detailed behavioral and technological representations was developed to investigate how electric vehicles (EVs) will penetrate the markets in the long-term and what impacts on energy consumption and emissions would emerge following EV adoption in China at the provincial level. A set of scenarios was created based on different policy interventions for the promotion of electric mobility. The results showed that subsidies for EV adoption would significantly boost the market share and foster a rapid transition away from fossil fuels, while the business-as-usual scenario would only generate a moderate influence on EV penetration. The regional differences in the emission reduction potential due to EV subsidies across the 31 provinces indicated that policy instruments for EV promotion would have significant positive effects in the developed provinces in both the capital metropolitan area and southeastern China. An economic cost analysis revealed a relatively low economic feasibility in northeastern and northwestern regions where the emission reduction potential is also lower than the national average, implying that the developing provinces in northeastern and northwestern China require greater financial assistance and the establishment of supportive policies for EV promotion.
Electric vehicles are environmental transportation modes that are widely applied in green logistics systems. To guarantee the energy efficiency, the impacts of customer service modes and recharging strategies need to be integrated into the optimization of electric logistics resource. This paper proposes an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies (EVRPMBTW-RS), minimizing the total travel cost with sophisticated constraints on the time-dependent pickup and delivery requests, limited recharging station capacity, and battery remaining capacity of electric vehicles. Mixed service sequences of linehaul and backhaul customers is allocated for the routing planning, with the synchronous optimization of recharging strategies including the selection of recharging stations and determination of recharging time. A time-discretized multi-commodity network flow model is constructed based on an extended space-time-state modeling framework, which is formulated as a quadratic 0-1 programming model by using the augmented Lagrangian relaxation technique. After the dualization and linearized transformation, we decompose the model into a sequence of least-cost path subproblems based on the alternating direction multiplier method (ADMM). The subproblems are alternately minimized and solved using the time-dependent forward dynamic programming algorithm. The solution quality can be guaranteed through calculating the optimality gap between the best lower bound and upper bound for each iteration. The proposed solution approach is examined on examples of a simple 7-node network and real-world Yizhuang road network. This paper provides a theoretical foundation for the route optimization method of electric logistics vehicles, and contributes to improve the operational efficiency of electric logistics systems.
The Electric Vehicle Routing Problem with Time Windows and Stochastic Waiting Times at Recharging Stations is an extension of the Electric Vehicle Routing Problem with Time Windows where the electric vehicles (EVs) may wait in a queue before the recharging service starts due to limited number of chargers available at stations. Since the customers and the depot are associated with time windows, long waiting times at the stations in addition to the recharging times may cause disruptions in logistics operations. To solve this problem, we present a two-stage simulation-based heuristic using Adaptive Large Neighborhood Search (ALNS). In the first stage, the routes are determined using expected waiting time values at the stations. While the vehicles are following their tours, upon arrival at the stations, their queueing times are revealed. If the actual waiting time at a station exceeds its expected value, the time windows of the subsequent customers on the route may be violated. In this case, the second stage corrects the infeasible solution by penalizing the time-window violations and late returns to the depot. The proposed ALNS applies several destroy and repair operators adapted for this specific problem. In addition, we propose a new adaptive mechanism to tune the constant waiting times used in finding the first-stage solution. To investigate the performance of the proposed approach and the influence of the stochastic waiting times on routing decisions and costs, we perform an experimental study using both small and large instances from the literature. The results show that the proposed simulation-based solution approach provides good solutions both in terms of quality and of computational time. It is shown that the uncertainty in waiting times may have significant impact on route plans.
The use of Electric Vehicles for logistics necessitates routing based on battery capacity constraints and includes trips to the charging station to maintain sufficient level of battery charge. Battery consumption depends on several external parameters and therefore, fixed route solutions may not always remain feasible while execution. This paper presents the idea of decomposing the Electric Vehicle Routing Problem into offline routing over the customers and a online recourse strategy for dynamic charging decisions to suit the practical scenario. To accommodate the stochastic battery consumption, a charging strategy comparable to the inventory control policy is proposed. Two policies based on a.) separate decision parameter for each node and b.) a common parameter over the given network, are tested. The vehicle movement is simulated over fixed routes but with stochastic battery consumption over each arc following charging decisions as specified by the given polices. The three parameters i.e. minimum battery level, number of times vehicle is charged and feasibility of route over each simulation are compared for testing the two policies. It is observed that the network-wide fixed parameter give comparable results to the node dependent parameters, using lesser information.
Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers' travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.
Nowadays, due to the new laws and policies related to the greenhouse gas emissions, and the rise of social and ecological awareness of transport sustainability, logistic companies started to incorporate green technologies in their distribution activities. Here, electric vehicles, as a cleaner mode of transport than conventional vehicles come to the fore and many companies are already integrating electric vehicles in their delivery fleets. Compared to the conventional vehicles, electric vehicles have a shorter driving range due to the limited battery capacity, and they need to recharge at charging stations more frequently. To efficiently manage the fleet of electric vehicles, new algorithms that take into account visits to charging stations have to be developed. In this paper, we observed the Electric Vehicle Routing Problem with Time Windows (E-VRPTW) and multiple or single recharge policies during the route. The homogeneous fleet of battery electric vehicles with limited load and battery capacity, customer time windows and full linear recharge at charging stations are considered. The objective is to minimize total traveled distance while operating a minimal number of vehicles. To find the solution of the problem, on larger instances we applied the metaheuristic based on the ruin-recreate principle and on the small instances we solved the mixed integer program with commercial software.
To develop a non-polluting and sustainable city, urban administrators encourage logistics companies to use electric vehicles instead of conventional (i.e., fuel-based) vehicles for transportation services. However, electric energy-based limitations pose a new challenge in designing reasonable visiting routes that are essential for the daily operations of companies. Therefore, this paper investigates a real-world electric vehicle routing problem (VRP) raised by a logistics company. The problem combines the features of the capacitated VRP, the VRP with time windows, the heterogeneous fleet VRP, the multi-trip VRP, and the electric VRP with charging stations. To solve such a complicated problem, a heuristic approach based on the adaptive large neighborhood search (ALNS) and integer programming is proposed in this paper. Specifically, a charging station adjustment heuristic and a departure time adjustment heuristic are devised to decrease the total operational cost. Furthermore, the best solution obtained by the ALNS is improved by integer programming. Twenty instances generated from real-world data were used to validate the effectiveness of the proposed algorithm. The results demonstrate that using our algorithm can save 7.52% of operational cost.
In the paper, the effect of the charging behaviours of electric vehicles (EVs) on the grid load is discussed. The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. A nonlinear stochastic programming model with the maximized comprehensive index is developed to analyze the charging schemes, and a heuristic searching algorithm is used for the optimal parameters configuration. With the comparison of the evaluation criteria, the multiobjective strategy is more appropriate than the single-objective strategy for the charging, i.e., electricity price. Furthermore, considering the characteristics of the normal batteries and charging piles, user behaviour and EV scale, a Monte Carlo simulation process is designed to simulate the large-scale EVs traveling behaviours in long-term periods. The obtained simulation results can provide prediction for the analysis of the energy demand growth tendency of the future EVs regulation. As a precedent of open-source simulation system, this paper provides a stand-alone strategy and architecture to regulate the EV charging behaviours without the unified monitoring or management of the grid.
The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration. We formulate this problem as a 0–1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR. These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions.
This research introduces the recharging vehicle routing problem (RVRP), a new variant of the well-known vehicle routing problem (VRP) where vehicles with limited range are allowed to recharge at customer locations mid-tour. The problem has potential practical applications in real-world routing problems where electric vehicles with fast recharging capabilities may be used for less-than-truckload (LTL) deliveries in urban areas. The general problem is introduced as a capacitated problem (CRVRP) and a capacitated problem with customer time window constraints (CRVRP-TW). A problem statement is formulated and experimental results along with derived solution bounds are presented. Intuitive results are observed when the vehicle range is constrained and when recharging time is lengthy. It is also shown that the average tour length highly correlates with derived solution bounds. Estimations of the average tour length can be used in planning application to estimate energy costs and consumption as a function of vehicle and customer characteristics.
The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.
This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.
The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.
The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.
In view of the phenomenon that more and more companies use electric vehicles (EVs) to distribute cargo in urban areas, this paper presented an optimized EV distribution method with the time window by considering the intelligent charging strategy in the smart grid. The EVs can either charge or discharge when connecting to the smart grid through the vehicle to grid (V2G) system. The V2G mode provides a more flexible way for EVs to operate. It can help EVs to increase efficiency and lower the cost at the same time. Based on the recharge and vehicle routing problem, this paper proposed a nonlinear integer programming model, considered the charging and discharging decisions of EVs, and proposed an improved genetic algorithm. Finally, 25 cases were designed to verify the feasibility of the algorithm. The simulation result shows that the iterative efficiency of the improved genetic algorithm (GA) is higher than other algorithms and the quality of the optimal solution is improved by 47%. © 2021, Editorial Department, Journal of South China University of Technology. All right reserved.
Recently, electric vehicles (EVs) have gained attention in the field of logistics owing to the strong support received from the government and continuous increase in social environmental awareness. Compared to traditional logistics vehicles, EVs incur additional charging costs, such as charging time and battery wear costs. In this study, the routing problem of EVs is formulated as an integer programming model based on a nonlinear charging model and practical battery wear model. Subsequently, a three-phase algorithm called CWIGALNS was proposed to solve this problem. Based on the proposed model, a series of instances was generated, showing the benefits of combining charging time, battery wear, and distribution. Finally, sensitivity analyses were systematically conducted on the wear cost and charging time under a realistic background. The results show that the optimal planning of an EV network considering time and wear costs is in line with the practical needs of EV logistics enterprises, which can help reduce the operating costs.
It is undoubtedly that the environmental and economic benefits will increase by integrating electric vehicles, which are the vehicles of the future, with the car-sharing system. The problem of determining the locations of electric vehicle charging stations (EVCS) and service areas of the car-sharing system appear with the integration. Environmental and energy concerns are the biggest driving factor behind electric car-sharing systems. In this study, Geographic Information Systems (GIS) based fuzzy multi-criteria decision making (MCDM) method is proposed for the solution of site selection problems of electric car-sharing stations (ECSS). To do so, a three-step solution methodology is developed: (i) determining 20 sub-criteria and weighting the criteria with fuzzy Analytic Hierarchy Process (f-AHP), (ii) obtaining a suitability map for potential ECSS via GIS, (iii) sorting the performance levels of ECSSs assigned according to the suitability map by Elimination and Choice Translating Reality (ELECTRE). The proposed methodology is applied for Istanbul, a metropolitan city in Turkey as a case study. The results show that the most suitable areas for ECSSs in the European and Anatolian side, Istanbul is an intercontinental city, are the south-east part and south-western part, respectively.
As electrification becomes one of the most promising ways for low carbon transition in the transport sector, electric vehicle charging infrastructure (EVCI) has been widely perceived as a vital public service and strategic asset for supporting the transition in China. Considering the massive investments required by EVCI deployment and the urgent need to mitigate climate change, it is significant to maximize the environmental benefits of EVCI with limited resources. However, current studies mainly focus on the economic efficiency of EVCI, with limited attention to the environmental perspective. Hence, this study employs the slacks-based data envelopment analysis (SBM-DEA) model and Malmquist Productivity Index to evaluate the climate change mitigation efficiency of EVCI in 30 provinces of China from 2016 to 2019. The results show substantial inter-provincial variations in current efficiency levels and dynamic changes. To explain the differences and provide theoretical reference for future improvement, this study conducts determinant analysis and finds that resource utilization and energy structure have significant impacts on EVCI mitigation efficiency. Based on the results, this study further puts forward targeted efficiency improvement strategies for provinces at different development stages from circular economy and energy transition perspectives.
A primary challenge in goods distribution using Electric Commercial Vehicles (ECVs) pertains to tackling their limited driving range. This paper proposes a multi-faceted approach towards increasing the driving range of ECVs by coordinating the options of: (i) intra-route recharging at an intermediate Recharging Station (RS), with (ii) synchronised en-route battery swapping services performed by Battery Swapping Vans (BSVs) at a pre-planned rendezvous time and space. We introduce and solve a variant corresponding to an Electric Vehicle Routing Problem with Time Windows, RSs and Synchronised Mobile Battery Swapping (EVRPTW-RS-SMBS). In the proposed model, route planning is carried out synchronously for two interdependent fleets, i.e., ECVs and BSVs, which work in tandem to complete the delivery tasks. To address methodological complications arising from the simultaneous consideration of intra-route recharging at RSs and the synchronised battery swapping on-the-fly, the paper develops a pre-optimisation procedure based on a Non-Dominated Path Identification (NDPI) algorithm that is used in deriving a significantly strengthened path-based formulation of the problem, and an efficient dynamic programming based heuristic algorithm. To gain practical insights on the economic and environmental added value and viability of the proposed logistics model, we compare different scenarios for goods distribution using ECVs in urban and regional levels in London and Southeast England, respectively. A set of numerical experiments are further performed to demonstrate the efficiency of the proposed algorithms. Our results indicate significant cost and emissions savings and an opportunity for going beyond last mile local deliveries using ECVs with the proposed logistics model.
Due to global warming and fast depletion of fossil fuels, the option of battery-operated electric vehicles (EVs) has emerged as one of the most popular alternatives for sustainable transport. In the present study, India is considered as a case country to explore the challenges in sustainable supply chain of electric vehicle batteries. India, being the second most populated country after China and having limited reserves of fossil fuels, has great potential to excel in electric vehicle supply chains. In addition, growth of electric vehicle markets in India is in the emerging phase. With increasing demand for EVs, the industry is facing many challenges for sustainable supply chain of electric vehicle batteries. The lithium-ion battery is a major component of electric vehicles. Many challenges for sustainability of electric vehicle battery supply chains have been extracted through literature review and discussions with industry experts. These challenges may be categorized as operational, technological, economic, environmental and social. Delphi technique is utilized to finalize major challenges for analysis. For further prioritization of these challenges, Best-Worst Method (BWM) is used. Finally, findings of the BWM are validated through an empirical study by collecting responses from 87 respondents. It is observed that ineffective recycling and reuse of batteries, disposal of batteries, and insufficient charging infrastructure are the three most important challenges in EV battery supply chain in India. The findings may be equally relevant in many developing countries having similar technological and infrastructure constraints. It will help policymakers in developing strategies for sustainable transport systems in developing countries.
The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. This paper compares battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.
Technology innovations are expected to overcome several barriers to the uptake of Electric Vehicles (EVs). This paper explored the role of battery and charging technologies in the diffusion of EVs. Specifically, four groups of “what-if” scenario in Beijing were set up to assess the potential impacts of battery cost (i.e., EV price), battery capacity (i.e., driving range), battery swap stations and fast charging posts on the expansion of EV market. An agent-based spatial integrated model (SelfSim-EV) was used to simulate how vehicle consumers might respond to these technological innovations over time. The results suggested that 1) Plug-in Hybrid Electric Vehicle (PHEV) became competitive when its sale price decreased over time at a yearly rate of 8%, due to the decrease in battery cost; 2) Increasing the driving range of Battery Electric Vehicle (BEV) had little influence on the total number of vehicle purchasers, but did increase electricity consumption; 3) Deploying fast charging infrastructures, i.e., battery swap stations and fast charging posts, had little influence on the uptake of EVs at the macro level, suggesting that fast charging facilities might not be necessary at the early stage of EV development.
In recent years, electric ride-hailing has increased considerably in the taxi industry with the development of battery electric vehicles (BEVs) and implementation of greenhouse gas (GHG) emission regulations. Private BEVs are charged mostly in private garages, while electric ride-hailing services require public charging stations (CSs). This work uses an improved genetic algorithm (GA) to locate public CSs by considering the investment of CS operators and the travel costs of BEV owners. A case study is presented with large-scale order data collected from the ride-hailing fleet of the city of Haikou and charging data from the electric ride-hailing fleet of the city of Shanghai. The elastic demand for electric ride-hailing is also considered by incorporating feedback between congestion at the CS and the geographical area. The proposed methodology uses the multipopulation genetic algorithm (MPGA) to provide more feasible allocations for public CSs and could reduce the total cost by 7.6%.
The integration of electric vehicles (EVs) with the energy grid has become an important area of research due to the increasing EV penetration in today’s transportation systems. Under appropriate management of EV charging and discharging, the grid can currently satisfy the energy requirements of a considerable number of EVs. Furthermore, EVs can help enhance the reliability and stability of the energy grid through ancillary services such as energy storage. This paper proposes the EV routing problem with time windows under time-variant electricity prices (EVRPTW-TP) which optimizes the routing of an EV fleet that are delivering products to customers, jointly with the scheduling of the charging and discharging of the EVs from/to the grid. The proposed model is a multiperiod vehicle routing problem where EVs can stop at charging stations to either recharge their batteries or inject stored energy to the grid. Given the energy costs that vary based on time-of-use, the charging and discharging schedules of the EVs are optimized to benefit from the capability of storing energy by shifting energy demands from peak hours to off-peak hours when the energy price is lower. The vehicles can recover the energy costs and potentially realize profits by injecting energy back to the grid at high price periods. EVRPTW-TP is formulated as an optimization problem. A Lagrangian relaxation approach and a hybrid variable neighborhood search/tabu search heuristic are proposed to obtain high quality lower bounds and feasible solutions, respectively. Numerical experiments on instances from the literature are provided. The proposed heuristic is also evaluated on a case study of an EV fleet providing grocery delivery at the region of Kitchener-Waterloo in Ontario, Canada. Insights on the impacts of energy pricing, service time slots, range reduction in winter as well as fleet size are presented.
We consider a general electric vehicle (EV) charging system with stochastic demand, demand request locations, and predetermined charging facilities (including charging station locations and charger capacities). The objective is to design a good routing strategy that accommodates well demand-request dynamics so as to satisfy the charging system’s stability constraints and also minimize the EV’s mean response time. We introduce a class of flexible and measurement-based routing policies called “partition-based random routing” (PBRR) and show that the performance measure of interest can be formulated as a constrained optimization problem with a convex objective function when the system is heavily loaded. This formulation enables us to establish strong theoretical results that are in aid of finding the optimal routing solution; however, in practice, finding this solution requires rather involved numerical calculations. To that end, we propose a surrogate, easy to design and implement, optimization algorithm for finding the desired optimal routing solution. Numerical work based on synthetic data shows that the performance of the developed routing strategy and its fast implementation is highly satisfactory for a number of system settings.
It is critical to provide coordinated charging facilities along with the rapid growth of Electric vehicles (EVs). Public-Private-Partnerships (PPP) have been used to build and operate electric vehicle charging infrastructure (EVCI). However, stakeholders such as operators, users and governments have not obtained satisfactory benefits using fixed concession period PPP contracts. To promote revenue and encourage the participation of the private sector, this paper simulates the use of flexible concession periods for EVCI-PPP projects using an integrated System Dynamics (SD) model. The model involves a peak-valley time-of-use tariff (TOU) price strategy, while a price reduction strategy is designed to redistribute revenues between users and operators. Three scenarios are defined to optimize the flexible concession period: i) a unitary charging price reduction strategy, ii) an variable charging price reduction strategy, and iii) a government incentive strategy. The results indicate that a valley load period rebate price strategy can increase charging station revenue to a certain extent. Furthermore, redistributing operating revenue between operators and users could improve the efficiency of EVCI-PPP projects by obtaining a shorter payback period and reducing government subsidies. Our results have important policy implications.
The use of electric vehicles (EVs) is becoming more and more widespread and the interest in these vehicles is increasing each day. EVs promise to emit less air pollution and greenhouse gas (GHG) emissions with lower operational costs when compared to fossil fuel-powered vehicles. However, many factors such as the limited mileage of these vehicles, long recharging times, and the sparseness of available recharging stations adversely affect the preferability of EVs in industrial and commercial logistics. Effective planning of EV routes and recharge schedules is vital for the future of the logistics sector. This paper proposes an electric vehicle routing problem with the time windows (EVRPTW) framework, which is an extension of the well-known vehicle routing problem (VRP). In the proposed model, partial recharging is considered for the EVRPTW with the multiple depots and heterogeneous EV fleet and multiple visits to customers. While routing a set of heterogeneous EVs, their limited ranges, interdependent on the battery capacity, should be taken into consideration and all the customers' deliveries should be completed within the predetermined time windows. To deal with this problem, a series of neighbourhood operators are developed for the local search process in the variable neighbourhood search (VNS) and variable neighbourhood descent (VND) heuristics. The proposed solution algorithms are tested in large-scale instances. Results indicate that the proposed heuristics perform well as to this problem in terms of optimizing recharging times, idle waiting times, overtime of operators, compliance with time windows, number of vehicles, depots, and charging stations used.
The electric vehicle (EV) industry and related service industries have developed rapidly because of the growing environmental awareness and government support. However, high purchase costs and an imperfect service network hinder EVs'promotion. Based on the time-sharing leasing mode of EVs, this study examines the deployment of EV service stations for intercity travel, considering user experience and allowing multipaths for each origin–destination pair. A novel mathematical programming model is formulated considering the user satisfaction and detour-and-service decision relationship. Further, a linearization method is used to transform a nonlinear satisfaction function into a linear one and improve computational efficiency. To solve the problem, two heuristic algorithms (K-MGAE and K-ALNS) are introduced with modified procedures and operators based on the characteristics of the model. The experimental results are compared with those of CPLEX and K-MGAE over two sets of instances, and it was found that K-ALNS generates a more efficient solution. Then, this study considers a real highway network in the Hubei province of China. Sensitivity analyses are conducted to reveal the location strategy and route selection for EV users under a personal intercity usage scenario. Finally, managerial insights are presented to help governments and enterprises analyze the perception of different types of EV users and optimize the service strategy.
With the rising share of electric vehicles used in the service industry, the optimization of their specific constraints is gaining importance. Lowering energy consumption, time of charging and the strain on the electric grid are just some of the issues that must be tackled, to ensure a cleaner and more efficient industry. This paper presents a Two-Layer Genetic Algorithm (TLGA) for solving the capacitated Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) and Electric Vehicles (EV) with partial nonlinear recharging times (NL) – E-MDVRPTW-NL. Here, the optimization goal is to minimize driving times, number of stops at electric charging stations and time of recharging while taking the nonlinear recharging times into account. This routing problem closes the gap between electric vehicle routing problem research on the one hand and its applications to several problems with numerous real-world constraints of electric vehicles on the other. Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered. This allows the GA to not only solve the order of the routes but also the visits to electric charging stations and the electric battery recharging times. Various settings of the proposed method are presented, tested and compared to competing meta-heuristics using well-known benchmarks with the addition of charging stations.
The biggest change will occur in taxis which have an important share in transportation along with the dissemination process of electric vehicles (EV). Therefore, charging infrastructure problem must be solved for electric taxis (ETs). It is aimed to solve the site selection problem of electric taxi charging station (ETCS) for Istanbul, a metropolitan city in this study. A four-step solution approach is developed: (i) determination of six main and 25 sub-criteria, (ii) weighting of evaluation criteria using fuzzy Analytic Hierarchy Process (fuzzy AHP), (iii) Performing spatial analysis of criteria via Geographical Information System (GIS) and assigning of ETCSs, (iv) ranking of assigned ETCSs using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) accordance with performance values. The results show that, in Istanbul, it is seen that the southeast part of the European side and the southwestern part of the Anatolian side are more suitable for ETCS. The proposed methodology approach provides a more accurate solution for high degree of uncertainty problems.
The accelerating energy demand, growing concern regarding global warming and climate change has paved the path of electrification of the transport sector. Large scale adoption of Electric Vehicles (EVs) call for availability of sustainable and easily accessible charging infrastructure. The sporadic energy demand, different battery storage capacity and diverse penetrating patterns of electric vehicles have significantly raised the load elasticity on a power grid. Smart-grid environment promises to assist the addition of EVs into national grids by enabling both EV-charging and discharging (G2V and V2G) load. This will modify the load profile and reduce cost.This paper discusses comprehensively three basic infrastructures by which charging of EVs can be done. These infrastructures are studied and compared on the basis of some parameters. It has been found that distributed infrastructure shows best results for the charging of electric vehicles. The other two infrastructures prove costlier and increase power demand. Also, this paper examines three specific smart charging strategies and the impact of each strategy on the power system load profile and realization cost. Simulation results establish the superiority of smart charging over dumb charging.
The rapid development of logistics has increased carbon emissions. Smart Logistics distribution system is a comprehensive logistics system supported by advanced information technology, which aims to improve the operation efficiency of the logistics industry and reduce carbon emissions by optimizing resource allocation. Therefore, it is of great significance to explore the effects of Smart Logistics policy (SLP) on China's carbon emissions. This paper used the binary choice model to investigate the main factors influencing the establishment of Smart Logistics in Chinese cities, and whether carbon emissions are the cause of the establishment of Smart Logistics. Secondly, it analyzed the effect of SLP on carbon emissions using a difference-in-differences (DID) model. The results reveal that freight volume, logistics employment, and total social retail are important factors determining whether a city should establish Smart Logistics or not. Additionally, the decision whether to establish Smart Logistics is an exogenous policy variable to carbon emissions. The implementation of SLP can restrain carbon emissions significantly, with a continuous impact in the second year. Based on the findings of this paper, a series of policy implications with respect to promote the development of Smart Logistics were proposed.
As fuel prices increase and emission regulations become increasingly strict, electric vehicles have been used in various logistics distribution activities. Most studies have focused on the electric vehicle routing problem under a deterministic environment, neglecting the effects of uncertain factors in practical logistics distribution. Thus, a novel fuzzy electric vehicle routing problem with time windows and recharging stations (FEVRPTW) is investigated in this study, and a fuzzy optimization model is established based on credibility theory for this problem. In the presented model, fuzzy numbers are used to denote the uncertainties of service time, battery energy consumption, and travel time. Moreover, the partial recharge is allowed under the uncertain environment. To solve the model, an adaptive large neighborhood search (ALNS) algorithm enhanced with the fuzzy simulation method is proposed. In the proposed ALNS algorithm, four new removal algorithms are designed and integrated for addressing the FEVRPTW. To further improve the algorithmic performance, the variable neighborhood descent algorithm is embedded into the proposed ALNS algorithm and five local search operators are applied. The experiments were conducted to verify the effectiveness of the proposed ALNS algorithm for solving the presented model.
In this paper, a new variant of the electric vehicle routing problem is presented. Since recharging time is commonly considered as idle time, the aim is to take advantage of it by allowing customer visits by an alternative mode of transport while the electric vehicle is at a recharging station. This is particularly pertinent in a city logistics context. A mathematical model is proposed, as well as an Iterated Local Search metaheuristic. The Iterated Local Search is reinforced by adding Variable Neighborhood Descent and set partitioning. The proposed method is tested on public instances for E-VRPTWPR. Finally, it is shown that allowing satellite customers enables us to take advantage of recharging times and to reduce time spent at recharging stations.
This paper investigates a location problem of public charging stations for electric vehicles with the objective of CO2 emissions minimization through massive GPS-enabled trajectory data. The problem considers two distinct features, including CO2 emissions generated in round trips to charging stations and remaining electricity restrictions on charging decisions. A data-driven and particle swarm optimization-based intelligent optimization approach is developed to handle this problem. We then present how to implement this approach by using taxi trip data in Chengdu, China as case data and explore how much data could reflect effectively the travel patterns of an area. The results of case study show that one-week taxi trip data are sufficient to handle the investigated problem. The results also validate the necessity of considering two realistic features, including CO2 emissions in round trips to charging stations and remaining electricity restrictions on charging decisions, in charging station location problems. It can lead to (1) the reduction of daily CO2 emissions captured by about 0.14–0.37 ha of forests in one year, and (2) 0.85%–2.64% more charging demands being satisfied per day.
We investigate a specific version of the Green Vehicle Routing Problem, in which we assume the availability of a mixed vehicle fleet composed of electrical and conventional (internal combustion engine) vehicles. These are typically light- and medium-duty vehicles. We allow partial battery recharging at any of the available stations. In addition, we use a comprehensive energy consumption model which can take into account speed, acceleration, deceleration, load cargo and gradients. We propose a matheuristic embedded within a large neighborhood search scheme. In a numerical study we evaluate the performance of the proposed approach.
We introduce the electric vehicle routing problem with shared charging stations (E‐VRP‐SCS). The E‐VRP‐SCS extends the electric vehicle routing problem with nonlinear charging function (E‐VRP‐NL) by considering several companies that jointly invest in charging stations (CSs). The objective is to minimize the sum of the fixed opening cost of CSs and the drivers cost. The problem consists of deciding the location and technology of the CSs and building the routes for each company. It is solved by means of a multistart heuristic that performs an adaptive large neighborhood search coupled with the solution of mixed integer linear programs. It also contains a number of advanced efficient procedures tailored to handle specific components of the E‐VRP‐SCS. We perform extensive computational experiments on benchmark instances. We assess the competitiveness of the heuristic on the E‐VRP‐NL and derive 38 new best known solutions. New benchmark results on the E‐VRP‐SCS are presented, solved, and analyzed.
Electromobility (e-Mobility) represents the concept of using electric powertrain technologies, in-vehicle information, commu-nication technologies and connected infrastructures to enable the electric propulsion of vehicles andfleets. It has been recognized as amajorfield of innovation throughout the coming decades and the dominant technology for future urban mobility.
Due to growing environmental concerns about greenhouse gas (GHG) emissions, people are more incline to use electric vehicles in various distribution services. Because of the limited battery capacity, electric vehicles are required to visit recharging stations en-route. Therefore, the routing schemes of conventional vehicles may not be suitable as the routing schemes of electric vehicles. In this paper, the Electric Vehicle Routing Problem (EVRP) is introduced, and the corresponding mathematical model is formulated. The EVRP seeks to minimize the energy consumption of electric vehicles. The comprehensive calculation of energy consumption used by electric vehicles is provided in the EVRP model. An ant colony (AC) algorithm based meta-heuristics is proposed as the solution method of the EVRP. The effectiveness of the proposed algorithms is evaluated through extensive numerical experiments on the newly generated instances. We also illustrate the benefits of using an energy consumption-minimizing objective function rather than a distance-minimizing objective function for routing electric vehicles.
Key to the mass adoption of electric vehicles (EV) is establishing a sufficient recharging infrastructure network, based on customer behavior and psychology. This study examines the battery charging station location problem, considering users’ range anxiety and distance deviations, two major barriers to the mass adoption of EV. The problem is formulated as a bi-level integer programming model based on a range anxiety function. Then, the problem is solved using an adaptive large-neighborhood search, combined with a k-shortest path algorithm and an iterative greedy heuristic. Finally, the effects of parameters are analyzed in the context of a real-world road network.
Governments take an active role in promoting electric vehicles (EVs), but the lack of recharging infrastructures restricts companies to adopt EVs. To promote EVs' penetration in companies, a suitable public recharging infrastructure grid should be systematically designed by governments. This paper proposes a public recharging infrastructure location strategy for governments based on the bi-level programming. In the upper-level problem, the government optimizes his location strategy, i.e., selects infrastructures from candidate locations, to minimize the construction budget and meet desired EV adoption rate. In the lower-level problem, the company decides the percentage of the electric vehicles in her mixed fleet and the corresponding vehicle routing plan to minimize her operational cost utilizing the infrastructures constructed by the government. A two-phase heuristic combining variable neighborhood descent and scatter search is presented to solve the problem. The hybrid method hires scatter search to derive the optimal routing plan of mixed fleet and variable neighborhood descent to select infrastructure locations. The proposed method is examined against Cplex using benchmark instances. The results from extensive numerical studies reveal that the government should thoughtfully determine the desired adoption rate. The short-term optimal locations might be inefficient design for the long run if the rate varies. In order to minimize the budget, the government may not choose the infrastructure locations that are the most beneficial for the company. It's hard to achieve the desired adoption rate while considering the covering areas of the infrastructures merely. The subsidy policy and recharging infrastructure location strategy should be systematically designed to achieve a higher promoting effect with a lower budget.
In this paper, the authors formulate an emission-minimizing vehicle routing problem with heterogeneous vehicles and give rise to the effects of path selection. They take into account different paths for traveling between two locations differing with respect to their emissions. Computational experiments with artificial and real-world data illustrate the effects of path selection by considering networks with different road types like urban roads and highways. The experiments suggest an emission saving potential of about 2–4%. The authors conclude that in reality a larger emission reduction potential exists when multiple paths are considered in transportation planning.
Using the Transportation Mode-Technology-Energy-CO2 (TMOTEC) model which is based on discrete choice mothed and general transport cost simulation, this study made a scenario analysis of energy consumption and reductions in CO2 emissions in China’s transport sector. We used scenarios to investigate the relative influences of improving vehicle energy efficiency, promoting EV use, and increasing taxes for fossil fuels and CO2. We found that in the reference scenario, total transport energy consumption would increase to 636 million tons of oil equivalent (Mtoe) in 2050; that would result in 1602 million tons of CO2 emissions. In the comprehensive development scenario, transport energy consumption would peak at 497 Mtoe around 2045; the resulting CO2 emissions peak would be 1129 million tons of CO2 between 2040 and 2045. Both energy consumption and CO2 emissions in the transport sector would decline steadily after reaching their peak. We believe that the Chinese government should make greater efforts with vehicle fuel economy standards, in improving technological progress and market expansion of EVs, and in increasing taxes on traditional transport energy and CO2. This would contribute to reducing energy consumption and achieving a CO2 emissions peak in China’s transport sector as soon as possible.
A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In our prior CTSP, each salesman is allocated a particular color and each city, carrying 1, 2, or all salesmen's colors depending on the problem types, allows any salesmen with the same color to visit exactly once. This paper presents a more common CTSP, in which city colors are diverse, i.e., each city has one to all salesmen's colors while other elements of the problem keeps unchanged. It is a generalization of the existing CTSPs, i.e., the radial and serial ones, and can be used to model the scheduling problems with different accessibility of jobs toward executors. A city color matrix is introduced to describe the accessibility difference of cities to all salesmen. Since CTSP is NP-hard, this paper presents a variable neighborhood search (VNS) approach, instead of computationally intractable exact solutions. First, the repetitive solution space due to the dual-chromosome encoding for the prior genetic algorithms can be entirely avoided by using direct-route encoding. Then, a two-stage greedy initialization algorithm is utilized by VNS to generate the initial solution. A city removal mechanism and a reinsertion operation are introduced to change the neighborhood space of the current solution and 2-opt method is adopted for the local search. Extensive simulation is conducted and the results show that the proposed VNS is an efficient heuristics to solve CTSP.
Electric vehicle routing problems (E-VRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing E-VRP models assume that the battery-charge level is a linear function of the charging time, but in reality the function is nonlinear. In this paper we extend current E-VRP models to consider nonlinear charging functions. We propose a hybrid metaheuristic that combines simple components from the literature and components specifically designed for this problem. To assess the importance of nonlinear charging functions, we present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting nonlinear charging may lead to infeasible or overly expensive solutions. Furthermore, to test our hybrid metaheuristic we propose a new 120-instance testbed. The results show that our method performs well on these instances.
Effective route planning for battery electric commercial vehicle (ECV) fleets has to take into account their limited autonomy and the possibility of visiting recharging stations during the course of a route. In this paper, we consider four variants of the electric vehicle-routing problem with time windows: (i) at most a single recharge per route is allowed, and batteries are fully recharged on visit of a recharging station; (ii) multiple recharges per route, full recharges only; (iii) at most a single recharge per route, and partial battery recharges are possible; and (iv) multiple, partial recharges. For each variant, we present exact branch-price-and-cut algorithms that rely on customized monodirectional and bidirectional labeling algorithms for generating feasible vehicle routes. In computational studies, we find that all four variants are solvable for instances with up to 100 customers and 21 recharging stations. This success can be attributed to the tailored resource extension functions (REFs) that enable efficient labeling with constant time feasibility checking and strong dominance rules, even if these REFs are intricate and rather elaborate to derive. The studies also highlight the superiority of the bidirectional labeling algorithms compared to the monodirectional ones. Finally, we find that allowing multiple as well as partial recharges both help to reduce routing costs and the number of employed vehicles in comparison to the variants with single and with full recharges.
In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.
This paper presents a unified tabu search heuristic for the vehicle routing problem with time windows and for two important generalizations: the periodic and the multi-depot vehicle routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the vehicle routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.
This paper addresses the fuzzy version of Capacitated Location Routing Problem (CLRP). The proposed CLRP is defined considering opening costs for depots, limited capacities of vehicles and a set of customers with known demands. There are some potential nodes to locate depots, fuzzy travel times between nodes, and also a time window to meet the demand of each customer. Although there exists several papers, manuscripts and technical reports in the literature concerning Location Routing Problem (LRP) extensions and solution procedures, most of them assume the variables to be crisp. In this paper, we present a multi-depot capacitated LRP (MDCLRP) in which travel time between two nodes is a fuzzy variable. A simulation-embedded Simulated Annealing (SA) procedure is proposed in order to solve the problem. The proposed framework is tested using a standard test problem of MDCLRP and results are analyzed and justified. This numerical example shows that the proposed method is robust and could be used in real world problems.