No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Deploying public charging stations for electric vehicles on urban road networks
Abstract
This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers' spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.
... Energy Reports 12 (2024) 5367-5382 impact or sustainability considerations associated with the deployment of these stations. A subsequent study by (He et al., 2015), utilising the Sioux Falls network, determined that it incorporates various initial states of charge and drivers' risk-taking attitudes. Additionally, (Oladigbolu et al., 2022) discuss that Nigeria's optimal configuration for renewable energy-based EV charging stations is a photovoltaic/wind turbine/battery system in Sokoto, which minimises carbon emissions and promotes EV development. ...
... Additionally, (Oladigbolu et al., 2022) discuss that Nigeria's optimal configuration for renewable energy-based EV charging stations is a photovoltaic/wind turbine/battery system in Sokoto, which minimises carbon emissions and promotes EV development. Both (He et al., 2015 andOladigbolu et al., 2022) neglect to account for the effects of various charging technologies, overlook spontaneous alterations in travel and recharging decisions, and fail to investigate the integration of EV charging stations with the current power grid station, considering the associated social and environmental implications. A study conducted by (Uslu and Kaya, 2021) aimed to reduce billing costs in a municipal electricity grid through resource sharing optimisation. ...
... Fig. 17 illustrates the current stations and the prospective paths available for EV users to access charging stations efficiently. Comprehending the road network is essential for evaluating the efficacy and efficiency of the charging station network (He et al., 2015). To evaluate the distribution and accessibility of charging stations around Boulder City. ...
The strategic deployment of EV (EV) charging stations is crucial for promoting sustainable transportation and facilitating the widespread adoption of EVs. However, the lack of readily accessible charging station continues to be a significant barrier to the mainstream adoption of EVs. This study presents a comprehensive approach to optimizing the location of charging stations using a custom K-means clustering algorithm. The algorithm incorporates various factors including charging demand, energy consumption, population density, and existing stations, to identify optimal locations for the charging stations. The proposed methodology aims to minimize the distance between charging stations and areas with high EV demand while considering energy efficiency and avoiding redundancy near low-weighted point locations. The algorithm iteratively assigns data points to clusters and updates the centroids, converging to an optimal solution that balances coverage, accessibility, and sustainability. Folium map in Python and Python code was used for case study analysis. The innovation of the finding shows the effectiveness of the custom K-means clustering algorithm in optimizing charging station placement, by introducing a penalty term to avoid repositioning charging stations close to a low-weighted (charging demand) point charging station, also ensuring convenient access for EV owners, and promoting efficient energy utilization. The study emphasizes on strategic planning and data-driven approaches in developing a comprehensive and sustainable EV charging station network to address the challenge of limited charging station and support the growth of electric mobility.
... Besides the waiting or queuing time of EV charging, several studies have taken the EV traveling time and recharging time into consideration. For instance, He et al. [81] positioned charging stations within the driving ranges of EVs (based on simulated battery's state of charge), while minimizing the overall travel and refueling time. Studies, such as [82,71], provided a more detailed perspective of the time costs, covering traveling, queuing, and recharging time as three sub-objectives to be optimized. ...
... Beyond level 2 and 3 chargers, He et al. [81] included level 1 chargers in their usercentric EVCI planning, capturing EV driving behaviors. Specifically, EV drivers were simulated to stop at different locations. ...
... By plugging in their EVs at home, EV drivers can replenish the battery used during their daily, local journeys. Studies that integrate user journeys, such as [81] Page 16 of 42 AUTHOR SUBMITTED MANUSCRIPT -PRGE-100184.R1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t and [73], often assumed that drivers have access to level 1 residential charging. Although level 1 charging is sufficient for urban residential areas, McKinney et al. [138] explored the potential of utilizing level 2 charging in rural homes. ...
Electric vehicles (EVs) have emerged as a pivotal solution to reduce greenhouse gas emissions paving a pathway to net zero. As the adoption of EVs continues to grow, countries are proactively formulating systematic plans for nationwide electric vehicle charging infrastructure (EVCI) to keep pace with the accelerating shift towards EVs. This comprehensive review aims to thoroughly examine current global practices in EVCI planning and explore state-of-the-art methodologies for designing EVCI planning strategies. Despite remarkable efforts by influential players in the global EV market, such as China, the United States, and the European Union, the progress in EVCI rollout has been notably slower than anticipated in the rest of the world. This delay can be attributable to three major impediments: inadequate EVCI charging services, low utilization rates of public EVCI facilities, and the non-trivial integration of EVCI into the electric grid. These challenges are intricately linked to key stakeholders in the EVCI planning problem within the context of coupled traffic and grid networks. These stakeholders include EV drivers, transport system operators, and electric grid operators. In addition, various applicable charging technologies further complicate this planning task. This review dissects the interests of these stakeholders, clarifying their respective roles and expectations in the context of EVCI planning. This review also provides insights into level 1, 2, and 3 chargers with explorations of their applications in different geographical locations for diverse EV charging patterns. Finally, a thorough review of node-based and flow-based approaches to EV planning is presented. The modeling of placing charging stations is broadly categorized into set coverage, maximum coverage, flow-capturing, and flow-refueling location models. In conclusion, this review identifies several research gaps, including the dynamic modeling of EV charging demand and the coordination of vehicle electrification with grid decarbonization. This paper calls for further contributions to bridge these gaps and drive the advancement of EVCI planning.
... This approach seeks the optimal location among a given number of public charging infrastructures, and explicitly considers drivers' behavior in selecting the path and CSs. The authors extended their work by proposing a tour-based network equilibrium framework incorporating recharging decisions and driver route choices in deploying EV CSs (He et al., 2015). A similar line of studies was then carried out by Xu et al. (2017aXu et al. ( , 2017b and Yang et al. (2016). ...
... The bi-level approach accounts for the driver's selfish behavior in the decision-making process, instead of assuming that the user follows the planner's optimal settings for the chosen route and charging plan. EV drivers typically choose routes to minimize either distance or travel time without running out of battery (Lee et al., 2014;He et al., 2015He et al., , 2018Riemann et al., 2015;Chen et al., 2016;Jing et al., 2017;Liu and Wang, 2017;Miralinaghi et al., 2017;Li et al., 2018;Zhang et al., 2018b;Wang et al., 2019aWang et al., , 2019b. Travel time, by definition, includes charging time, driving time (He et al., 2015(He et al., , 2018Jing et al., 2017;Liu and Wang, 2017), and waiting time at the CS (Wang et al., 2019a). ...
... EV drivers typically choose routes to minimize either distance or travel time without running out of battery (Lee et al., 2014;He et al., 2015He et al., , 2018Riemann et al., 2015;Chen et al., 2016;Jing et al., 2017;Liu and Wang, 2017;Miralinaghi et al., 2017;Li et al., 2018;Zhang et al., 2018b;Wang et al., 2019aWang et al., , 2019b. Travel time, by definition, includes charging time, driving time (He et al., 2015(He et al., , 2018Jing et al., 2017;Liu and Wang, 2017), and waiting time at the CS (Wang et al., 2019a). Furthermore, in practice, the traffic volume crossing a section of road affects the travel time in that section. ...
This chapter reviewed the literature on optimization models for charging electric vehicles and provided a brief overview of charging station location problems (CSLPs). It also discussed some barriers to widespread EV adoption. Optimization objectives and constraints were examined in these contexts. Charging demands were distinguished using flow-based and node-based representations. The flow-based models, network equilibrium models, and game theory approaches are mostly used to model EV charging infrastructure planning. Different options for implementing demand coverage modeling in flow-based mathematical formulations were analyzed. This chapter further addressed metaheuristics algorithms used to tackle the given issues. The key academic contributions of the study are the demonstration of several characteristics for further research – for example, considering behavioral issues and path-dependent policies. The key practical implications of this chapter are identifying different structures and performance metrics affecting policymaking. Critical research gaps were identified – notably dynamic decision processes and reliability metrics, power grid integration and coordination, and uncertain energy consumption during peak and idle hours.
... Conversely, a significant body of research is dedicated to locating en-route charging stations for electric vehicles within congested traffic networks, aiming to minimize a diverse range of costs. Some researchers examined the influence of charging station capacities on their optimal locations ( Chen et al., 2016 ), while others incorporated drivers' deviation behaviors into their models ( He et al., 2013 ;He et al., 2015 ;Liu and Wang, 2017 ;Guo et al., 2018 ;Zhang et al., 2018b ;Wang et al., 2019b ). There are also studies that considered both the charging station capacity and drivers' deviation behavior ( Chen et al., 2016 ;Wang et al., 2019a ;Chen et al., 2020 ;Ghamami et al., 2020 ). ...
... There are also studies that considered both the charging station capacity and drivers' deviation behavior ( Chen et al., 2016 ;Wang et al., 2019a ;Chen et al., 2020 ;Ghamami et al., 2020 ). Regarding cost components, most studies fall into two main categories: (1) Minimization of travel time/costs or charging expenses ( He et al., 2013 ;He et al., 2015 ;Chen et al., 2016 ;Liu and Wang, 2017 ;Guo et al., 2018 ;Zhang et al., 2018b ;Wang et al., 2019a ;Wang et al., 2019b ;Chen et al., 2020 ;Ghamami et al., 2020 ), and (2) minimizing the investment costs for building charging infrastructures ( He et al., 2013 ;Guo et al., 2018 ;Wang et al., 2019b ;Chen et al., 2020 ;Ghamami et al., 2020 ). Some researchers also aim to minimize trip failure costs ( He et al., 2015 ;Liu and Wang, 2017 ) or greenhouse emissions ( Zhang et al., 2018b ). ...
... Regarding cost components, most studies fall into two main categories: (1) Minimization of travel time/costs or charging expenses ( He et al., 2013 ;He et al., 2015 ;Chen et al., 2016 ;Liu and Wang, 2017 ;Guo et al., 2018 ;Zhang et al., 2018b ;Wang et al., 2019a ;Wang et al., 2019b ;Chen et al., 2020 ;Ghamami et al., 2020 ), and (2) minimizing the investment costs for building charging infrastructures ( He et al., 2013 ;Guo et al., 2018 ;Wang et al., 2019b ;Chen et al., 2020 ;Ghamami et al., 2020 ). Some researchers also aim to minimize trip failure costs ( He et al., 2015 ;Liu and Wang, 2017 ) or greenhouse emissions ( Zhang et al., 2018b ). ...
A comparative analysis of modeling and solution methods for the en-route charging station location problems within uncongested and congested highway networks Metanetworks Bi-level programming models Branch-and-bound algorithm a b s t r a c t This paper investigates a widely discussed class of charging station location problems for the en-route charging need of electric vehicles traveling in intercity highway networks. Due to the necessity for multiple charges along an intercity long-haul trip, this type of charging station location problems implies such an individual behavior that electric vehicle drivers make self-optimal route-and-charge decisions while ensuring the driving range of their vehicles to sustain trips without running out of charge. The main contribution of this paper is on analytically and computationally comparing the modeling and solution methods for the charging station location problems within uncongested and congested networks. Two distinct modeling frameworks are presented and analyzed: A metanetwork-based two-stage model for uncongested networks and a network-based bi-level model for congested networks. Both models are tackled by the classic branch-and-bound algorithm, which, however, resorts to different problem decomposition schemes, subregion bounding strategies, and network flow evaluation methods. Specifically, for uncongested networks, a two-phase procedure first employs a bi-criterion label-correcting algorithm for constructing a metanetwork and then implements the branch-and-bound algorithm on the metanetwork embedding a single-criterion label-setting algorithm for deriving network flows; on the other hand, for congested networks, the branch-and-bound algorithm is directly applied on the original network encapsulating a convex combinations method for deriving network flows. Finally, the two network scenarios and their modeling and solution methods are quantitatively evaluated with two real-world highway networks, in terms of implementation complexity, solution efficiency, and routing behavior.
... This deployment strategy enables drivers to make spontaneous adjustments and interact with their travel and recharging decisions. Additionally, it helps maintain multi-class tour-based network equilibrium conditions, ensuring a balanced and sustainable transportation system for EV users [13]. Real-time traffic conditions and the availability of charging stations can significantly impact route planning for EVs. ...
... [18] determined the optimal number and locations of fast-charging stations for range-limited alternative fuel vehicles, considering both cost minimization and maximum coverage objectives. [13] investigated the optimal placement of public charging stations for electric vehicles within a road network, taking into account the dynamic nature of drivers' travel and recharging decisions and their spontaneous adjustments and interactions. ...
... The bidding curves enable the efficient allocation of electricity resources by facilitating the matching of supply and demand at equilibrium prices. Equation (13) formulates the bidding curve for the dayahead market. This curve describes the relationship between the bid price and the corresponding quantity of electricity that market participants are willing to supply or demand in the day-ahead market. ...
With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between OD (Origin-Destination) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential. This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.
... With the established equilibrium model, the optimal deployment plan of charging stations across the network is described by a mathematical program with complementarity constraints. In a similar spirit, He et al. (2015) and Xie and Jiang (2016) established a network equilibrium model to determine the tour paths and recharging plans of EV drivers and their relationship with the location plan of charging stations. He et al. (2015) and Bao and Xie (2021) further formulated a bi-level mathematical program with the network equilibrium model embedded in the lower level, allowing for at-destination charges and en-route charges, respectively, to optimize the location plan. ...
... In a similar spirit, He et al. (2015) and Xie and Jiang (2016) established a network equilibrium model to determine the tour paths and recharging plans of EV drivers and their relationship with the location plan of charging stations. He et al. (2015) and Bao and Xie (2021) further formulated a bi-level mathematical program with the network equilibrium model embedded in the lower level, allowing for at-destination charges and en-route charges, respectively, to optimize the location plan. Considering the location problem of charging lanes, a new type of charging technology to electrify public roads such that EVs can be recharged when they are moving on the roads (Chen et al., 2017(Chen et al., , 2018, Chen et al. (2016) developed a novel user equilibrium model to capture the spontaneous adjustments and interactions between EV drivers' route choice and their battery recharging plan given the presence of charging lanes. ...
... In a nutshell, in order to incorporate the spontaneous reaction of EV drivers to various charging-infrastructure deployment plans, the deployment problem is normally formulated as a bi-level program, in which the upper level specifies the locations and numbers of charging facilities to deploy, while the lower level delineates the corresponding network equilibrium flow and charging-demand distribution of EV drivers across the network. In particular, to describe the equilibrium condition, EV drivers are generally presumed to determine their route choices and battery recharging plans, i.e., when and where to charge, in a way to minimize their individual travel cost, simply a weighted sum of travel time and recharging costs (e.g., Jiang and Xie, 2014;He et al., 2015;Chen et al., 2016;Liu and Wang, 2017;Sun et al., 2020). Nevertheless, none of these travel cost functions have ever taken into account the impact of driving fatigue, which may play an important role in both the real-world operation and the modeling outcome. ...
... Numerous studies have attempted to develop energy models for managing and designing BEB fleets, focusing on optimizing battery size, charger capacity, and operating schedules while minimizing investment and operating costs [12]. Notable models include flow-based models for optimizing oil and gas station locations [13][14][15][16] and network equilibrium models for EV charging station location optimization [17,18]. Some studies integrate business and policy recommendation models [19,20]. ...
... Charging Plan: Charging is planned for the daytime using quick DC charging, and there are no charging limitations. BEB Energy Consumption: The energy consumption of battery electric buses (BEBs) is assumed to be 1.23 kWh/km [18], considering the use of a regenerative braking system. This average applies to all bus routes. ...
In Thailand, diesel buses are notorious for their poor energy efficiency and contribution to air pollution. To combat these issues, battery electric buses (BEBs) have emerged as a promising alternative. However, their high initial costs have posed challenges for fleet management, especially for agencies such as the Bangkok Mass Transit Authority (BMTA). This study aims to revolutionize BEB fleet management by developing an energy model tailored to the BMTA’s needs. The methodology consists of two crucial steps: analyzing BMTA bus routes and designing fleet management and charging systems. Through this process, the study seeks to determine the maximum number of BEBs that can be operated on each route with the fewest chargers possible. The results reveal exciting possibilities. Within the city bus landscape, two out of five BMTA bus routes show potential for transitioning to BEBs, provided they meet a maximum energy requirement of 200 kWh every two rounds. This analysis identifies routes ripe for BEB adoption while considering the limitations of battery size. In the next step, the study unveils a game-changing strategy: a maximum of 13 BEBs can operate on two routes with just four chargers requiring 150 kW each. This means fewer chargers and more efficient operations. Plus, the charging profile peaks at 600 kW from 4:00 to 8:00 p.m., showing when and where the fleet needs power the most. However, the real eye-opener? Significant energy savings of THB 10.44 million per year compared to diesel buses, with an initial investment cost savings of over 37%. These findings underscore the potential for BEB fleet management to revolutionize public transportation and save money in the long run. However, there is more work to be done. The study highlights the need for real-time passenger considerations, the development of post-service charging strategies, and a deeper dive into total lifetime costs. These areas of improvement promise even greater strides in the future of sustainable urban transportation.
... Locating charging stations to minimize the system cost (or maximize the system profit) represents another research stream, including different focuses and features. Some studies take into account the capacity of charging stations (Wang and Lin, 2009;Zheng and Peeta, 2017;Rose et al., 2020) while others focus on the detour behavior of electric vehicles (He et al., 2015;Liu and Wang, 2017;Zhang et al., 2018;Wang et al., 2019). Included cost components vary across studies, with some focusing on minimizing the total infrastructure investment (Wang and Lin, 2009;Rose et al., 2020;Zheng and Peeta, 2017), while others aim to minimize the user costs, i.e., the sum of the travel and recharging costs (He et al., 2015;Liu and Wang, 2017;Wang et al., 2019;Zhang et al., 2018). ...
... Some studies take into account the capacity of charging stations (Wang and Lin, 2009;Zheng and Peeta, 2017;Rose et al., 2020) while others focus on the detour behavior of electric vehicles (He et al., 2015;Liu and Wang, 2017;Zhang et al., 2018;Wang et al., 2019). Included cost components vary across studies, with some focusing on minimizing the total infrastructure investment (Wang and Lin, 2009;Rose et al., 2020;Zheng and Peeta, 2017), while others aim to minimize the user costs, i.e., the sum of the travel and recharging costs (He et al., 2015;Liu and Wang, 2017;Wang et al., 2019;Zhang et al., 2018). There is also a number of studies aiming at minimizing the system cost, which includes both the infrastructure investment and user cost Fakhrmoosavi et al., 2021;Kavianipour et al., 2021;He et al., 2022;Zhou et al., 2022;Wang et al., 2023). ...
... locations in Lisbon, aiming to maximize covered demands[16].He et al. (2015) proposed a double-layer mathematical model considering vehicle driving distances and charging needs, underlining the importance of daily mobility patterns of EV users[17].Shahraki et al. (2015) presented an optimization model maximizing vehicle mileage based on driving patterns, emphasizing the role of real-world data in location decisi ...
... locations in Lisbon, aiming to maximize covered demands[16].He et al. (2015) proposed a double-layer mathematical model considering vehicle driving distances and charging needs, underlining the importance of daily mobility patterns of EV users[17].Shahraki et al. (2015) presented an optimization model maximizing vehicle mileage based on driving patterns, emphasizing the role of real-world data in location decisions[18].Wu et al. (2017) designed a stochastic flow-capturing location model reflecting the randomness in EV users' traveling behavior [19]. Models by Tu et al. (2016) and Luo et al. (2018) included temporal and spatial constraints, making these models more realistic by considering variable parking availability, congestion levels, and EV owners' home and work locations [20] [21]. ...
Research Objectives
This study aims to provide a comprehensive and efficient optimization network genetic algorithm to locate and distribute electric vehicle charging stations across Germany.
The main goals can be summarized as follows:
- Modeling and building the network
- Analysis of the current EVCS network analysis, including its evolution, distribution of charging stations, and user accessibility
- Strengths and weakness of the existing infrastructure and highlight areas for improvement
- Implement the GA to optimize the location of new EVCS
- Evaluate the impact of various factors, such as technical specifications and spatial considerations
- Validate the optimization network by comparing the proposed EVCS network with the existing infrastructure
Through these objectives, this research aims to provide valuable insights and practical solutions to address the challenges faced by the EVCS network in Germany. This work will contribute to the advancement of EV infrastructure and promote the transition towards a more sustainable and efficient transportation system.
... Early attempts to solve the charging/fueling station location problem include greedy heuristics proposed by Berman et al. (1992) and Hodgson et al. (1996), which produced good quality results within short computation times. Metaheuristic methods, such as genetic algorithms, have been developed by many researchers to solve the charging station location problem, including Dong et al. (2014), He et al. (2015), Kang et al. (2015), Xie et al. (2018), and Kadri et al. (2020). When the problem objective involves non-linear components, good approximation algorithms are needed to overcome computational difficulties. ...
... Another challenge in locating charging stations is to consider the driving behavior of BEV drivers. He et al. (2015) established a bilevel modeling structure to deploy DCFCs while considering BEV drivers' route choice decision due to traffic congestion. However, as we have mentioned, deploying only DCFC stations may not meet the huge demands of BEV drivers, especially during long-distance travel under a large BEV adoption rate. ...
With the rapid development of charging-while-driving technology, the deployments of wireless charging lanes will inevitably affect the route choice of Battery Electric Vehicle (BEV) drivers and the power supply of power grids. This paper builds a bi-level optimization problem to optimize the locations of charging lanes and charging stations given an integrated transportation-power network. The decision variables of the upper level include the locations of charging lanes and charging stations and the electricity generated in each busbar (an electricity generation node in the power network), while the lower level determines the charging strategy and the route choice of the drivers. Then we develop an effective solving algorithm for the problem through various reformulations. The results from the numerical study and sensitivity analysis show that charging infrastructures' deployment affects the route choices of BEV drivers significantly, and charging lanes can be a more economical and effective charging method than charging stations.
... The advancement of the sustainable transportation infrastructure, particularly EV charging networks, has been a pivotal research domain. Investigations by He et al. [28] and Pagany et al. [29] have concentrated on optimal charging station placement strategies, factoring in elements such as traffic flow, land use, and grid capacity. These studies provide valuable insights into the spatial aspects of the charging infrastructure's development but often treat these as a static optimization problem. ...
This study presents a novel approach to understanding the complex dynamics of the electric vehicle (EV) market through the lens of differential game theory. We developed a comprehensive model that captures the strategic interactions between EV manufacturers and charging network operators, while incorporating the effects of consumer behavior, market uncertainties, and reference price effects. Using differential game theory, we examined the impact of reference price effects and the charging network’s influence on pricing strategies, focusing on three distinct approaches: basic pricing, static pricing considering reference price effects, and dynamic pricing strategies. Our model offers new insights into consumer behavior and price expectations in the rapidly evolving EV market. The key findings reveal that under static or dynamic pricing strategies, the optimal pricing for EV manufacturers is positively correlated with the initial reference price. When the initial reference price is high (low), the optimal pricing strategy resembles skimming pricing (penetration pricing). As the effort level of charging network operators increases and their influence on consumers’ purchase decisions grows stronger, EV manufacturers tend to set higher prices. Notably, while dynamic pricing strategies can optimize EV manufacturers’ profits, the profits of charging network operators may decrease compared with static pricing strategies. This integrated approach significantly contributes to the field by bridging gaps among market dynamics, pricing strategies, and the infrastructure’s development in the context of electric mobility, providing a comprehensive framework for understanding and optimizing the EV ecosystem. Ultimately, this study advances sustainable business models that balance profitability, consumer behavior, and the infrastructure’s growth in the rapidly evolving EV market.
... Frade and others analyzed the siting of electric vehicle charging stations in the Lisbon region of Portugal using a maximum coverage model to determine the optimal number and capacity of stations in the area 19 . There have been propositions to formulate the charging station location problem as a bi-level mathematical program and solve it using a genetic algorithm-based method 20 . To maximize vehicle mileage, Shahraki introduced an optimization model based on driving patterns and used real data to determine the locations and sizes of charging stations 21 ; Wu utilized the Stochastic Flow Capturing Location Model to optimize the locations and numbers of charging stations 22 . ...
As electric vehicles (EV) become increasingly popular, issues such as the limited number of charging stations, low utilization rates, and suboptimal placement have drawn significant attention. Therefore, the rational planning of charging station locations is of paramount importance. Traditional site selection methods often require high-quality data inputs and are prone to overfitting, resulting in poor generalization. This study innovatively proposes converting regional characteristics into natural language text and introduces the PETRoBERTa model based on prompt learning to assess the suitability of different areas for constructing charging stations. The study focuses on Wuhan, using hourly time granularity and kilometer spatial granularity to predict the suitability of different grids for station construction. The proposed model is compared with other baseline models, and the results show that the PETRoBERTa model achieves a prediction accuracy of 93.21%, outperforming others across various evaluation metrics. Therefore, our method can effectively aid in the planning of charging station layouts, making a significant contribution to the further adoption and promotion of electric vehicles.
... Then, taking Ireland as an example, through the genetic algorithm, the model is solved and analyzed. Fang et al. [23] studied the site selection problem for public electric vehicle charging stations, considering the limited range and charging demands of pure electric vehicles. They established a Maximal Covering Location Problem model and solved it using a dual-level mathematical programming approach and a genetic algorithm-based procedure. ...
The rise of new energy technologies has accelerated progress towards sustainable development, and many companies are beginning to invest in renewable resource-related facilities. Electric bicycles have always been an important mode of green transportation; however, they also have problems such as slow charging, difficult charging, and that burning and short circuiting may occur during charging. Electric bicycle battery exchange cabinets effectively solve these problems by exchanging low batteries with full batteries instead of charging. However, current battery exchange cabinets face the problems of insufficient construction and unreasonable site selection. Therefore, this paper proposes a location selection model for electric bicycle battery exchange cabinets based on point demand theory, aiming to maximize rider satisfaction and the service capacity of exchange cabinets. The immune algorithm is introduced to solve the location model; however, the traditional immune algorithm has some problems such as poor stability and slow convergence. In this paper, the mutation process of the traditional immune algorithm is improved by introducing multi-point mutation, guided mutation, and local search. Finally, based on the data of electric bicycle riders in Shanghai, we verify that the location model based on point demand theory performs well on two objective functions of rider satisfaction and battery exchange cabinet service capability. We also expand the application of point demand theory to location models. Then, by conducting experiments with different parameter groups, through sensitivity analysis and convergence analysis, we verified that the improved immune algorithm performs better than the traditional immune algorithm in its accuracy, search accuracy, stability, and convergence.
... In the contemporary era, research on this transition primarily focuses on the public transportation and logistics industry. Depending on the study approaches, it can be categorized into three main categories: vehicle routing problem for electric vehicles (Li et al., 2018;Basso et al., 2019;Yao et al., 2020;Tang et al., 2023), site selection for charging stations (He, Yin, and Zhou, 2015;Tu et al., 2016;Wu and Sioshansi, 2017;Tadayon-Roody, Ramezani, and Falaghi, 2021),and intelligent charging strategies (Hiermann et al., 2016;Amini, Kargarian, and Karabasoglu, 2016;Khalkhali and Hosseinian, 2020;Zweistra, Janssen, and Geerts, 2020). One of the hot research topics is the trajectory optimization of electric vehicles, but these studies generally have the following caveats: firstly, numerous studies make the assumption that the battery discharge model is linear, which does not align with the actual nonlinear discharge behavior. ...
Airports worldwide are actively promoting the transition of ground service vehicles from traditional fuel-powered vehicles to electric vehicles. The key to the successful implementation of this transition lies in the development of efficient electric vehicle dispatching models that comprehensively consider the charge-discharge processes of electric vehicles. However, due to the nonlinear characteristics of charge-discharge processes, finding precise solutions poses a significant challenge. Previous researchers have often used traditional energy consumption models and constant charging rates to simplify calculations, but this has resulted in inaccurate estimates of the remaining battery charge level. Furthermore, the lack of diverse pacing and charging strategies for airport ground service vehicles necessitates more adaptable solutions to enhance operational efficiency. To address these challenges, this paper uses airport electric tractors as a case study, develops an accurate model that takes into account the start-stop process and a piecewise linear charging function, designs an improved genetic algorithm that incorporates a greedy algorithm and an adaptive strategy, and develops charge-discharge coupling strategies for different configuration scenarios at Nanjing Lukou Airport to meet current and future needs. The research results indicate that compared to traditional genetic algorithms, the proposed improved genetic algorithm significantly enhances solution accuracy and convergence speed. Additionally, with the increase in flight scale, airports can appropriately enhance their charging strategies; airports with dispersed aircraft stands should devise higher pacing strategies compared to those with dense aircraft stands.
... Interesting models that are widely used include flow-based models, which show the potential optimizations of oil and gas station locations [9][10][11][12], and network equilibrium models, which show potential optimizations of EV charging station location [13,14]. Some studies have attempted to mix their models with business models and policy recommendation models [15,16]. ...
In Thailand, there are several bus fleets—predominantly diesel buses—which is an internal combustion engine (ICEs) in circulation. These fleets are consumed a lot of energy and cause pollution, especially by emitting GHGs and PM2.5. Battery electric buses (BEBs) have been proposed to address these issues. BEB fleets still have a high initial cost, which is why most fleet governance agencies, such as the Bangkok Mass Transit Authority (BMTA), are facing investment decisions. This study aims to develop an energy model for the BMTA BEB fleet by using their real operating data. The methodology for the development of the investment model with two steps is described; there is a BMTA bus route analysis step and a fleet management and charging design step. The output is illustrated in terms of the maximum number of BEBs that can be operated on a sample route with the minimum number of chargers. Interesting results were obtained with the first step: Two of the five BMTA bus routes can be changed into BEBs in phases with a limit of 200 kWh for the energy requirements for every two rounds. The results from the second step demonstrated that the maximum number of BEBs for the two routes was 13, with four charger plugs of charger, thus requiring 150 kW per plug. The charging profile peaked at 600 kW from 4:00 to 8:00 p.m. These results show the potential of the model for fleet design and investment decisions. The sample results from the models illustrate energy savings and cost evaluations for fleet management and design. Compared with diesel buses, THB 10.44 million per year can be saved in terms of energy costs by using a BEB fleet. An optimization model was used to assess the savings incurred due to the investment cost. More than 37% of the costs were saved. A full economic investigation should be carried out in the future. These results show only the energy used when the fleet has already been transformed into a BEB fleet in phases. The emphasis of battery size investigation and energy used were illustrated. They only depict an economic validation of the model, but do not refer to such projects’ feasibility, and the model has not yet been fully validated.
... In conjunction with worldwide initiatives, their strategy focus on Electric Vehicles (EVs) as an ecologically friendly alternative is obvious (Din et al., 2023;Hao et al., 2016;Kitamori et al., 2012;Tu & Yang, 2019). An essential aspect of this project is the intentional allocation of resources towards the development of charging infrastructure, which is widely acknowledged as crucial for the extensive adoption of Electric Vehicles in the transportation sector (He et al., 2015;Ling et al., 2021;Patt et al., 2019;Sachan et al., 2020). By June 2023, China has achieved remarkable results from its efforts, with a fleet of around 16.2 million EVs and a vast network of nearly 2.15 million public charging stations (Briceno-Garmendia et al., 2023). ...
This research paper explores the critical aspects of User Experience (UX) in Electric Vehicle Charging Applications (EVCA) within the Chinese context. Employing a mixed-methods approach, the study identifies and validates six key UX factors: user-friendliness, payment convenience, information reliability, practical functionality, interactivity, and privacy protection. The research examines how these factors contribute to user satisfaction, emphasizing the contextual variability of user perceptions, particularly regarding privacy protection. The findings provide valuable insights for EVCA developers and operators, emphasizing the importance of user-centered design principles and the enhancement of key UX elements. The paper suggests that developers should focus on creating EVCA systems that prioritize user needs, while operators should consider user demographics and streamline operational procedures. Despite privacy protection not being a significant factor in this study, its potential to enhance user trust is recognized. The study contributes to the theoretical understanding of EVCA and offers practical guidance for improving UX in China’s rapidly evolving field of electric mobility and digital technology. It also underlines the role of policymakers in aligning policies and content with user expectations, fostering a mutually beneficial relationship between developers and users. This research advances scholarly knowledge and provides actionable recommendations for stakeholders in the electric mobility and digital technology sectors.
... This omission, however, results in inaccurate estimations of traffic flow patterns and energy demand distribution. Some researches [2], [23] have examined th e impacts of EV driving range limitations by introducing energy consumption ratio into the UE model. It is assumed that the energy consumption is based on distance. ...
The increasing presence of hybrid hydrogen-electric vehicles (HEVs) in urban areas leads to interdependence among power networks (PNs), transportation networks (TNs), and hydrogen networks (HNs). Therefore, this paper proposes a new energy pricing scheme for multi-energy networks (MENs) considering the effect of HEV mobility. To capture the responsive behaviors of HEV drivers, a new user equilibrium (UE) model is proposed, which incorporates flow-dependent energy consumption of HEVs and their choices regarding routing, charging, and refueling. For mathematical tractability, the UE model is recast into a variational inequality (VI) formulation. Based on the VI formulation, a coordinated energy pricing scheme is proposed for the MENs to capture the interactions between pricing strategies and HEV user behaviors. The scheme is generalized as an optimization problem with VI constraints, and a fixed-point based iterative algorithm is developed to efficiently obtain the optimal solution. The case study results demonstrate the effectiveness of the pricing scheme, which successfully considers the behavior of HEV drivers and realizes the optimal operation of the MENs with HEV mobility.
... The study provides insights into the allocation of the corresponding resources, but the model only gives the distribution of the number of charging stations between regions and does not give specific deployment locations. Building on this foundation, He [19] considered driver specificity, taking into account both spontaneous regulation and travel decisions, and proposed a two-layer mathematical planning model to determine the locations of electric vehicle charging stations. The approach considered drivers' travel characteristics and analyzed the travel process to reconstruct a travel chain. ...
Electric buses have been vigorously promoted and implemented in major countries worldwide and have generated a huge demand for charging stations. Optimizing the daily charging experience of electric buses, adapting the daily operation scheduling, improving the utilization rate of charging stations, reducing the load on the power grid, and improving the operation efficiency of electric bus line networks require the reasonable layout of the charging stations. In this study, public transportation charging station layout and siting is the research object. We summarize the progress of analysis methods from the charging station and vehicle sides; introduce related research on the planning and layout of charging stations based on optimization models, including cost analysis and siting and layout for electric bus systems; summarize the data-driven station planning and siting research; and provide an overview of the current charging demand estimation, accuracy, and charging efficiency. Finally, we address the problems of the charging demand estimation accuracy, the mismatch between the charging station layouts for electric buses, and the charging demand on a long time scale. We suggest that research be conducted on data fusion for the temporal and spatial refinement of charging demand prediction in the context of the electrification of public transportation systems and the big data of telematics.
Accelerating EV adoption is crucial in reducing carbon emissions. To support EV penetration and minimize waste, we analyzed real-world charging status big data in a high-density city heavily reliant on public charging infrastructure. Using a two-part spatial lag of X model, we investigated the impact of charging station attributes and the surrounding built environment on the utilization rates, defined as the ratio of occupied (fast/slow) charging piles to the total available (fast/slow) piles at each station. Our findings reveal that fast charging stations located outdoors, sheltered, and near major roads attract more users, while these factors do not significantly affect the slow utilization rate. Utilization rates are higher for fast charging stations surrounded by a diverse land use mix, while the opposite is observed for slow charging stations. These findings provide valuable insights for the effective operation of charging infrastructure, furthering the development of the new-energy market and sustainable transportation.
The Automotive sector is a pivotal contributor to India’s economic growth and contributes 49% to India’s manufacturing GDP. In FY24, the country produced approximately 49 lakh passenger vehicles, 9.9 lakh three-wheelers, 214.7 lakh two-wheelers, and 10.7 lakh commercial vehicles. The world’s primary modes of transportation are facing two major problems: rising oil costs and increasing carbon emissions. As a result, electric vehicles (EVs) are gaining popularity as they are independent of oil and do not produce greenhouse gases. However, despite their benefits, several operational issues still need to be addressed for EV adoption to become widespread. This research delves into the evolution of EVs over time and highlights their benefits, including reducing carbon emissions and air pollution. India's transition from traditional internal combustion engines (ICE) to electric vehicles (EVs) is driven by a combination of environmental, economic, and policy factors. This shift is aimed at reducing the country's dependency on fossil fuels, lowering carbon emissions, and promoting cleaner air, particularly in urban areas plagued by pollution. Government initiatives such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme.
Con la creciente aceptación de los vehículos eléctricos (VE) y el interés de los gobiernos por implementar incentivos para la transición hacia modelos de electrificación de la movilidad, la planeación y proyección adecuada de estaciones de carga han adquirido gran relevancia, ya que son las encargadas de proporcionar la energía necesaria para cargar las baterías de los vehículos. Es por ello que , por lo quegarantizar una prestación eficiente y accesible de este servicio es indispensable para impulsar aún más este mercado en expansión. La implementación de los Sistemas de Información Geográfica (SIG) ha facilitado significativamente el análisis y la representación de la información necesaria para determinar la localización más adecuada para estas estaciones de carga. Por esta razón, este trabajo se centra en la planificación estratégica de estaciones de carga a lo largo de la red de carreteras de Colombia. El análisis parte de la recopilación de datos relevantes sobre la demanda actual de estaciones de recarga, la infraestructura existente, el tamaño de la población y las políticas gubernamentales relacionadas con la movilidad eléctrica. A partir de esta información, se procede a estimar la cantidad de estaciones de carga necesarias, considerando factores como la distancia promedio recorrida de los VE, la potencia proyectada para las futuras estaciones, la capacidad promedio de las baterías, la autonomía promedio de los vehículos más vendidos en Colombia y el tiempo diario durante el cual se espera que las estaciones estén operativas. Finalmente, para seleccionar las ubicaciones óptimas de las estaciones de carga, se evaluaron diversos factores, como la red de vías de primer orden que conectan las ciudades estudiadas y las opciones de localización que cumplen con los criterios establecidos para asegurar la accesibilidad, la cobertura adecuada y eficiencia en la distribución de las estaciones.
This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms.
Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21].
Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .
The global increase in electric vehicle (EV) usage is evident; however, inadequate infrastructure in many regions still creates range anxiety among drivers. To address this concern, this paper presents a two-stage methodology for analyzing green transformation projects at existing gas stations towards determination of the most appropriate location and capacity decisions. The first stage of the methodology involves a state of charge tracking optimization model to enhance the process of making location decisions and the optimization model is run several times by considering different parameter values to evaluate potential locations for electric vehicle charger installation. In the second stage, a simulation model is constructed to explore capacity decisions based on various scenarios related to regional densities. Afterwards, extensively tested experiments are performed through a real case from Trabzon province of the Karadeniz Coastal Road, a geographically significant area in Turkey's Eastern Black Sea region, to obtain remarkable insights and policy recommendations. As a result, policy recommendations derived from this study emphasize the prioritization of charging station installations at high-frequency locations and tailoring the number of charging units to regional density. The significance of this study is to offer adaptable solutions for various locations, enhance practical utility and support informed decision-making in infrastructure planning by providing a proposed two-stage methodology for sustainable transportation planning.
In recent times, the limited availability of fossil fuels and growing concerns regarding the emission of greenhouse gases (GHGs) have directly impacted the shift from conventional automobiles to electric vehicles (EVs). Additionally, there have been notable advancements in new energy research, which have significantly improved the viability of EVs. Consequently, EVs have gained widespread recognition and have been rapidly adopted in many countries worldwide. However, the rapid growth of EVs has given rise to several challenges, such as insufficient charging infrastructure, unequal distribution, high costs, and a lack of charging stations, which have become increasingly significant. The limited availability of charging facilities is hindering the widespread adoption of EVs. However, as more people embrace EVs, there has been a growth in the installation of electric vehicle charging stations (EVCSs) in public locations. Recent research has focused on identifying the ideal locations for EVCSs in order to assist the electrification of transport systems and meet the growing demand for EVs. A well-developed EVCS infrastructure can help address some key issues facing EVs, such as pricing and range limitations. Researchers have used various methodologies, objective functions, and constraints to formulate the problem of identifying the best sites for EVCSs. Current research is focused on determining the best locations for EVCSs. This endeavor intends to ease the transition to electrified transport networks while also addressing the growing demand for EVs. This review article explores various optimization techniques to achieve optimal solutions while considering the impact of EV charging load on the distribution system (DS), environmental implications, and economic impact. The research used a standard IEEE 33-bus radial distribution system (RDS) with a full variety of potential energy sources to improve understanding of the subject. The use of the bald eagle search algorithm (BESA) and cuckoo search algorithm (CSA) aided in the best identification of energy source locations and their relative capacities. In addition, the examination of EV charging techniques, including both the traditional charging technique (TCT) and the innovative charging technique (ICT), is being undertaken to assess the effectiveness of these approaches. The study included ten separate scenarios, each of which was thoroughly evaluated to demonstrate the individual and synergistic usefulness of various energy sources in mitigating the effects of EV charging on the DS. The collected data from the empirical inquiry was aggregated and thoroughly analyzed.
With the intensification of environmental pollution and energy shortage problems, electric vehicles as an alternative fuel vehicle are receiving more and more attention. Compared with traditional fossil fuel vehicles, electric vehicles can significantly reduce the dependence on traditional fuels and greenhouse gas emissions. However, the market share of electric vehicles is still relatively low and the development of electric vehicles is impeded by various limitations, including shortened driving range, lack of charging infrastructure, and long battery recharging period. The construction of additional charging facilities can ease the range anxiety of electric vehicle travelers and promote the development of electric vehicles. Accessibility refers to the ability of users to reach their destinations and complete expected activities. It is an important indicator for evaluating the service quantity of a transportation system. Based on the space-time-electricity accessibility of electric vehicle travelers, this study optimizes the distribution of charging facilities including charging stations and wireless charging segments. Specifically, our research includes the following aspects:
(1) Definition of space-time-electricity accessibility for electric vehicles
Based on the existing space accessibility and space-time accessibility indicators, we extend the electricity dimension and derive a space-time-electricity accessibility indicator. If the electric vehicle traveler can reach the destination from the origin satisfying the travel time budget and battery capacity restrictions, we denote that this origin-destination (OD) pair is space-time-electricity accessible. Otherwise, this OD pair is space-time-electricity inaccessible. To judge the space-time-electricity accessibility, we construct a mathematical programming model in the space-time-electricity network. We use the number of space-time-electricity accessible OD pairs to evaluate the accessibility. More space-time-electricity accessible OD pairs mean better accessibility of electric vehicle travelers in the transportation network.
(2) Accessibility-oriented charging station location optimization
Based on the space-time-electricity accessibility, a multi-commodity network flow model in the space-time-electricity network is constructed for this problem. By dualizing the coupling constraints between location and routing variables to the objective function, the Lagrangian relaxed problem can be decomposed into a series of least-cost path subproblems and a knapsack subproblem. These least-cost path subproblems can be efficiently solved by a time-dependent forward dynamic-programming algorithm and the knapsack problem can be solved by a standard solver (such as Gurobi). At each iteration, Lagrangian multipliers are adjusted by the subgradient optimization method and feasible solutions are generated using a heuristic method. The Lagrangian relaxation-based decomposition method is tested in three transportation networks, and the influence of key parameters is analyzed.
(3) Accessibility-oriented wireless charging segment location optimization
With the development of charging-while-driving techniques, electric vehicles can recharge on the road segments without stopping. Since electric vehicles do not need to spend a long period at fixed stations, dynamic charging infrastructures can significantly improve the mobility and accessibility of electric vehicles. If wireless charging segments are widely applied, the driving range of electric vehicles will be unlimited. For the accessibility-oriented wireless charging segment location problem, a multi-commodity network flow model in the space-time-electricity network and a column-based model are formulated. Based on the column-based model, a column generation-based decomposition approach is developed. At each iteration, a restricted master problem and a set of pricing subproblems should be solved. We design an efficient dynamic-programming algorithm to solve these least-cost path subproblems. In experimental experiments, the characteristics of this problem are analyzed and the performance of the proposed method is tested.
(4) Accessibility-oriented optimization of charging stations and wireless charging segments
Charging stations and wireless charging segments have their own advantages, so it is necessary to jointly optimize the two types of charging facilities. First, a multi-commodity network flow model in the space-time-electricity network is constructed for the joint optimization problem. Based on the augmented Lagrangian relaxation and alternating direction method of multipliers, a problem decomposition framework is proposed for this problem. The standard Lagrangian relaxed problem and the augmented Lagrangian relaxed problem are finally decomposed into several subproblems. By solving a series of simple subproblems, high-quality feasible solutions can be obtained for this problem. In experimental experiments, several key parameters are analyzed including the charging speed, construction budget, travel time budget, and battery capacity.
(5) Wireless charging segment location optimization under fully accessible demand
If wireless charging segments are widely applied in the transportation network, the driving range of electric vehicles will be unlimited by the electricity resources. The difficulty is how to determine the number and distribution of wireless charging segments so that all electric vehicle travelers can complete the trip and the total construction cost is minimal. For this problem, a full cover charging segment location model is formulated in the space-time-electricity network and a path-based model is further achieved. Based on the path-based model, we design a column generation-based decomposition method for this problem. At each iteration, a restricted master problem and a series of pricing subproblems need to be solved. In the experimental experiments, the influence of different parameters on the total construction cost and distribution of wireless charging segments is analyzed.
Electric vehicles (EVs) have gained increasing attention as a more sustainable and environmentally friendly mode of transportation. Many countries should include electrification of their transport networks in their future smart city plans to ensure environmentally sustainable growth. The number of EVs in metropolitan areas is expected to experience extensive growth. EVs are often considered an alternative to addressing the challenges posed by increasing carbon emissions and dependence on fossil fuels. However, the acceptance of EVs tends to be slow due to concerns such as range anxiety, prolonged charge times, inconvenient charging locations, and inadequate charging infrastructure. To address these challenges, this paper discusses the planning and optimization of EV charging infrastructure based on existing literature. In recent years, there has been a significant increase in the number of publications focusing on EV charging stations. This demonstrates the growing interest and research activity in the field of EV charging infrastructure. Furthermore, the literature is categorized into recurring topics, specifically EV charging planning and optimization for EV charging infrastructure. This particular review paper includes empirical work on charging network planning for EVs. Therefore, this analysis provides the latest trends and findings for EV charging infrastructure planning.
The rapid proliferation of electric vehicles (EVs) has promoted the process of electrified transportation, which deepened the interdependency of power and transportation networks. Considering the routing preference of EV travelers, this paper proposes an environment-aware dispatch model to coordinate coupled power-transportation networks toward higher economic benefits and renewable energy utilization. Specifically, discrete choice models with environmental factor are developed to characterize the user behavior. Based on the constructed user behavior models, an integrated optimal traffic-power flow (IOTPF) model is proposed and an environment-aware user equilibrium (EUE) can be achieved. A decentralized optimization algorithm based on Gauss-Seidel iterative process is developed to solve the equilibrium flows. Numerical results analyze the influence of environment-aware user behavior on the traveling cost and the utilization of renewable energy, the effectiveness of the IOTPF model in coordinating the optimal strategy of the coupled network is demonstrated.
This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users’ daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.
This paper presents a combined activity/travel choice model and proposes a flow-swapping method for obtaining the model's dynamic user equilibrium solution on congested road network with queues. The activities of individuals are characterized by given temporal utility profiles. Three typical activities, which can be observed in morning peak period, namely at-home activity, non-work activity on the way from home to workplace and work-purpose activity, will be considered in the model. The former two activities always occur together with the third obligatory activity. These three activities constitute typical activity/travel patterns in time-space dimension. At the equilibrium, each combined activity/travel pattern, in terms of chosen location/route/departure time, should have identical generalized disutility (or utility) experienced actually. This equilibrium can be expressed as a discrete-time, finite-dimensional variational inequality formulation and then converted to an equivalent "zero-extreme value" minimization problem. An algorithm, which iteratively adjusts the non-work activity location, corresponding route and departure time choices to reach an extreme point of the minimization problem, is proposed. A numerical example with a capacity constrained network is used to illustrate the performance of the proposed model and solution algorithm.
Modeling the effects of congestion pricing has become the focus of many researchers, and several have analyzed the cordon-based congestion pricing problem. However, few have attempted to investigate area-based congestion pricing because it is considered difficult to analyze precisely. Cordon-based pricing can be expressed easily in a traffic assignment procedure by the addition of a charge to the inbound links of a cordon area, but the impedance of area-based pricing cannot be expressed with a link-based formulation. The objectives of this study are to propose a sound network model for evaluating area-based pricing and to compare the effects of area-based pricing and cordon-based pricing. First, it is pointed out that an exact treatment of area-based congestion pricing requires consideration of the nonadditive trip-chain-based path cost. Such consideration appears complicated, but a simple trip-chain-based network equilibrium model with nonadditive path cost is formulated here, and a convex minimization problem that is equivalent to the model is presented. This formulation enables the evaluation of the effect of area-based congestion charging more exactly than the traditional trip-based model, even for a large network. Finally, the model is applied to a real urban area (Okinawa, Japan) and area-based pricing is compared with cordon-based pricing. In this example application, the optimal toll level for area-based pricing is found to be higher than that for cordon-based pricing.
This paper deals with the design of a grid-friendly ultrafast electric vehicle charging demonstrator. High charging power and short charging times impose peaks to an electricity distribution system, which necessitate over-dimensioning of the grid connection. A mitigation option lies in partial decoupling the load from the grid, achieved with the application of energy storage elements. A calculation methodology for energy storage elements is proposed and their interconnection possibilities to an ultrafast EV charging spot discussed.
This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.
Automatic teller machines and gasoline service stations are two examples of a growing number of "discretionary service facilities." In consuming service from these facilities, a significant fraction of customers do so on an otherwise preplanned trip (e.g., on the daily commute to and from work). A system planner, in determining the best locations of such facilities, is more concerned with placing the facilities along paths of customer flow rather than, say, near the center of a cluster of residences or work places. We formally model this problem and present a method for determining the optimal locations of m discretionary service facilities so as to intercept the maximum possible potential customer flow. We also show how to determine the minimal number of facilities required to intercept a prespecified fraction of total customer flow. Computational results are included.
The Flow Refueling Location Model (FRLM) is a flow-intercepting model that locates p stations on a network to maximize the refueling of origin–destination flows. Because of the limited driving range of vehicles, network vertices do not constitute a finite dominating set. This paper extends the FRLM by adding candidate sites along arcs using three methods. The first identifies arc segments where a single facility could refuel a path that would otherwise require two facilities at vertices to refuel it. The other methods use the Added-Node Dispersion Problem (ANDP) to disperse candidate sites along arcs by minimax and maximin methods. While none of the methods generate a finite dominating set, results show that adding ANDP sites produces better solutions than mid-path segments or vertices only. Copyright Springer Science+Business Media, LLC 2007
Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.
Billboards, well located, are an exellent advertising medium. The advertising industry has heretofore assessed the quality of billboard locations by counting the traffic passing through them, without considering the advantages or drawbacks of redundant exposure to billboards. The authors present a way of locating billboards that explicitly considers the advantages of multiple exposures of the advertising message. They introduce and demonstrate a linear integer program for locating billboards. They then present a heuristic programming approach for solving the model for large applications. They apply several versions of the model to locating billboards in the street network of Edmonton, Canada. They conclude with a plea to advertising researchers to refine our knowledge of the importance of multiple exposures in billboard advertising.
The environmental and economic impact of electric vehicles (EVs) will depend on the fraction of users that can accept an EV of a given capability, and then in turn on how those EVs are actually used. Historically, estimates of the fraction of total travel that could be electrified as a function of EV range are based on vehicle usage data for large populations of vehicles, most often the National Household Travel Survey (NHTS). Two assumptions implicit in such estimates are subject to question: (1) that any user could accept an EV as a primary vehicle and would use it for all trips within its range, and (2) that the usage patterns of any individual EV user are the same as that exhibited by entire population. The first assumption is clearly unrealistic; willingness to accept an EV is dependent on the transportation needs and alternatives readily available to each individual user. As a surrogate for a priori knowledge of individual preferences, we use a crude metric of acceptance defined as a threshold frequency of need for alternative transportation above which all users would not accept the inconvenience. To test the validity of the second assumption and better estimate market and electrification potential, we analyze roughly 1 year of usage data for each of 133 instrumented vehicles in Minneapolis–St. Paul. We find a characteristic individual usage pattern that does not resemble the average over a large number of vehicles. Using the surrogate metric of EV acceptance and a simple payback model, we show that although the market acceptance and electrification potential of EVs are severely limited by battery cost, it is possible to determine an optimal EV range. Using the same usage data and payback model, we show that plug-in hybrid electric vehicles (PHEVs) can be much more effective than all-electric vehicles in electrifying personal transportation.
The limited driving ranges, the scarcity of recharging stations and potentially long battery recharging or swapping time inevitably affect route choices of drivers of battery electric vehicles (BEVs). When traveling between their origins and destinations, this paper assumes that BEV drivers select routes and decide battery recharging plans to minimize their trip times or costs while making sure to complete their trips without running out of charge. With different considerations of flow dependency of energy consumption of BEVs and recharging time, three mathematical models are formulated to describe the resulting network equilibrium flow distributions on regional or metropolitan road networks. Solution algorithms are proposed to solve these models efficiently. Numerical examples are presented to demonstrate the models and solution algorithms.
The transition to electric vehicles (EV) faces two major barriers. On one hand, EV batteries are still expensive and limited by range, owing to the lack of technology breakthrough. On the other hand, the underdeveloped supporting infrastructure, particularly the lack of fast refueling facilities, makes EVs unsuitable for medium and long distance travel. The primary purpose of this study is to better understand these hurdles and to develop strategies to overcome them. To this end, a conceptual optimization model is proposed to analyze travel by EVs along a long corridor. The objective of the model is to select the battery size and charging capacity (in terms of both the charging power at each station and the number of stations needed along the corridor) to meet a given level of service in such a way that the total social cost is minimized. Two extensions of the base model are also considered. The first relaxes the assumption that the charging power at the stations is a continuous variable. The second variant considers battery swapping as an alternative to charging. Our analysis suggests that (1) the current paradigm of charging facility development that focuses on level 2 charging delivers poor level of service for long distance travel; (2) the level 3 charging method is necessary not only to achieve a reasonable level of service, but also to minimize the social cost; (3) investing on battery technology to reduce battery cost is likely to have larger impacts on reducing the charging cost; and (4) battery swapping promises high level of service, but it may not be socially optimal for a modest level of service, especially when the costs of constructing swapping and charging stations are close.
This paper explores integrated pricing of electricity and roads enabled by wireless power transfer technology. We envision that high-power, high-efficiency wireless power transfer technologies are mature in the near future, which electrify roads to be charging infrastructures. The prices of electricity at electrified roads will affect electric vehicles’ route choices while the energy requirement of those vehicles will in return affect the operations of the power network and thus the prices of electricity. To determine the optimal prices of electricity and roads to maximize social welfare, first- and second-best pricing models are proposed under different authoritarian regimes. More specifically, assuming that a government agency manages both transportation and power systems, we develop the first-best pricing model, based on which a marginal-cost pricing scheme is derived. The second-best pricing model is proposed if the agency participates in a competitive wholesale power market while being able to impose tolls on electrified roads. The toll design is formulated as a mathematical program with complementarity constraints, and is solved by a manifold suboptimization algorithm. Numerical examples are presented to offer insights on integrated pricing of roads and electricity and demonstrate its effectiveness on improving social welfare.
This paper develops an equilibrium modeling framework that captures the interactions among availability of public charging opportunities, prices of electricity, and destination and route choices of plug-in hybrid electric vehicles (PHEVs) at regional transportation and power transmission networks coupled by PHEVs. The modeling framework is then applied to determine an optimal allocation of a given number of public charging stations among metropolitan areas in the region to maximize social welfare associated with the coupled networks. The allocation model is formulated as a mathematical program with complementarity constraints, and is solved by an active-set algorithm. Numerical examples are presented to demonstrate the models and offer insights on the equilibrium of the coupled transportation and power networks, and optimally allocating resource for public charging infrastructure.
Many decision-making problems in transportation system planning and management can be formulated as bilevel programming models, which are intrinsically nonconvex and hence difficult to solve for the global optimum. Therefore, successful implementations of bilevel models rely largely on the development of an efficient algorithm in handling realistic complications. In spite of various intriguing attempts that were made in solving the bilevel models, these algorithms are unfortunately either incapable of finding the global optimum or very computationally intensive and impractical for problems of a realistic size. In this paper, a genetic-algorithms-based (GAB) approach is proposed to efficiently solve these models. The performance of the algorithm is illustrated and compared with the previous sensitivity-analysis-based algorithm using numerical examples. The computation results show that the GAB approach is efficient and much simpler than previous heuristic algorithms. Furthermore, it is believed that the GAB approach can more likely achieve the global optimum based on the globality and parallelism of genetic algorithms.
This paper assesses the potential energy profile impacts of plug-in hybrid electric vehicles and estimates gasoline and electricity demand impacts for California of their adoption. The results are based on simulations replicating vehicle usage patterns reported in 1-day activity and travel diaries based on the 2000–2001 California Statewide Household Travel Survey. Four charging scenarios are examined. We find that circuit upgrades to 240V not only bring faster charging times but also reduce charging time differences between PHEV20 and PHEV60; home charging can potentially service 40–50% of travel distances with electric power for PHEV20 and 70–80% for PHEV60; equipping public parking spaces with charging facilities, can potentially convert 60–70% of mileage from fuel to electricity for PHEV20, and 80–90% for PHEV60; and afternoons are found to be exposed to a higher level of emissions.
Congestion tolls are considered to be Pareto-improving if they reduce travel delay or improve social benefit and ensure that, when compared to the situation without any tolling intervention, no user is worse off in terms of travel cost measured, e.g., in units of time. The problem of finding Pareto-improving tolls can be formulated as a mathematical program with complementarity constraints, a class of optimization problems difficult to solve. Using concepts from manifold suboptimization, we propose a new algorithm that converges to a strongly stationary solution in a finite number of iterations. The algorithm is also applicable to the problem of finding approximate Pareto-improving tolls and can address the cases where demands are either fixed or elastic. Numerical results using networks from the literature are also given.
Increased interest in alternative fuels is attributable, in part, to rising oil prices and increasing concern about global warming. A lack of a refueling infrastructure, however, has inhibited the adoption of alternative-fuel vehicles. Little economic incentive exists to mass-produce alternative-fuel vehicles until a network of stations exists that can refuel a reasonable number of trips. The flow refueling location model (FRLM) was developed to minimize the investment necessary to create a refueling infrastructure by optimizing the location of fueling stations. The original uncapacitated FRLM assumes that the presence of a refueling station is sufficient to serve all flows passing through a node, regardless of their volume. This article introduces the capacitated flow refueling location model that limits the number of vehicles refueled at each station. It also introduces a modified objective function maximizing vehicle-miles traveled instead of trips, applies both models to an intercity network for Arizona, and formulates several other extensions.
Range of electric vehicles (EVs) has long been considered a major barrier in acceptance of electric mobility. We examined the nature of how range is experienced in an EV and whether variables from other adaptation contexts, notably stress, have explanatory power for inter-individual differences in what we term comfortable range. Forty EVs were leased to a sample of users for a 6-month field study. Qualitative and quantitative analyses of range experiences were performed, including regression analyses to examine the role of stress-buffering personality traits and coping skills in comfortable range. Users appraised range as a resource to which they could successfully adapt and that satisfied most of their daily mobility needs. However, indicators were found that suggested suboptimal range utilisation. Stress-buffering personality traits (control beliefs, ambiguity tolerance) and coping skills (subjective range competence, daily range practice) were found to play a substantial role in comfortable range. Hence, it may be possible to overcome perceived range barriers with the assistance of psychological interventions such as information, training, and interface design. Providing drivers with a reliable usable range may be more important than enhancing maximal range in an electric mobility system.
In this paper, we formulate a discrete network design problem as a mathematical program with complementarity constraints and
propose an active set algorithm to solve the problem. Each complementarity constraint requires the product of a pair of nonnegative
variables to be zero. Instead of dealing with this type of constraints directly, the proposed algorithm assigns one of the
nonnegative variables in each pair a value of zero. Doing so reduces the design problem to a regular nonlinear program. Using
the multipliers associated with the constraints forcing nonnegative variables to be zero, the algorithm then constructs and
solves binary knapsack problems to make changes to the zero-value assignments in order to improve the system delay. Numerical
experiments with data from networks in the literature indicate that the algorithm is effective and has the potential for solving
larger network design problems.
Beginning with Hodgson (Geogr.Anal.22(1990) 270), several researchers have been developing a new kind of location-allocation model for “flow capturing.” Instead of locating central facilities to serve demand at fixed points in space, their models aim to serve demand consisting of origin-destination flows along their shortest paths. This paper extends flow-capturing models to optimal location of refueling facilities for alternative-fuel (alt-fuel) vehicles, such as hydrogen fuel cells or natural gas. Existing flow-capturing models assume that if a flow passes just one facility along its path, it is covered. This assumption does not carry over to vehicle refueling because of the limited range of vehicles. For refueling, it may be necessary to stop at more than one facility in order to successfully refuel the entire path, depending on the vehicle range, the path length, and the node spacing. The Flow Refueling Location Model (FRLM) optimally locates p refueling stations on a network so as to maximize the total flow volume refueled. This paper presents a mixed-integer programming formulation for the nodes-only version of the problem, as well as an algorithm for determining all combinations of nodes that can refuel a given path. A greedy-adding approach is demonstrated to be suboptimal, and the tradeoff curve between number of facilities and flow volume refueled is shown to be nonconvex.
This paper presents a conceptual activity-based and time-dependent traffic assignment model. The temporal utility profiles of activities are employed to formulate the temporal activity choice behavior of individuals as a multinomial logit model. Route choice behavior is then described as the ideal dynamic user equilibrium condition. The combined activity/route choice condition is formulated as a time-dependent variational inequality problem, which is solved by a heuristic solution algorithm based on the space-time expanded networks.
This paper compares performances of cordon- and area-road pricing regimes on their social welfare benefit and equity impact. The key difference between the two systems is that the cordon charges travellers per crossing whereas the area scheme charges the travellers for an entry permit (e.g. per day). For the area licensing scheme, travellers may decide to pay or not to pay the toll depending on the proportion between their travel costs for the whole trip-chains during a valid period of the area license and the toll level. A static trip-chain equilibrium based model is adopted in the paper to provide a better evaluation of the area-based tolls on trip-chain demands. The paper proposes a modified Gini coefficient taking in account assumptions of revenue re-distribution to measure the spatial equity impact. The model is tested with the case study of the Utsunomiya city in Japan. The results demonstrate a higher level of optimal tolls and social welfare benefits of the area-based schemes compared to those of the cordon-based schemes. Different sizes of the charging boundary have significant influences on the scheme benefits. The tests also show an interesting result on the non-effect of the boundary design (for both charging types) on their equity impacts. However, when comparing between charging regimes it is clear that the area schemes generate more inequitable results.
CONOPT is a generalized reduced-gradient (GRG) algorithm for solving large-scale nonlinear programs involving sparse nonlinear constraints. The paper will discuss strategic and tactical decisions in the development, upgrade, and maintenance of CONOPT over the last 8 years. A verbal and intuitive comparison of the GRG algorithm with the popular methods based on sequential linearized subproblems forms the basis for discussions of the implementation of critical components in a GRG code: basis factorizations, search directions, line-searches, and Newton iterations. The paper contains performance statistics for a range of models from different branches of engineering and economics of up to 4000 equations with comparative figures for MINOS version 5.3. Based on these statistics the paper concludes that GRG codes can be very competitive with other codes for large-scale nonlinear programming from both an efficiency and a reliability point of view. This is especially true for models with fairly nonlinear constraints, particularly when it is difficult to attain feasibility.
INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
