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Locating Battery Swapping Stations for a Smart e-Bus System
Abstract and Figures
With the growing interest and popularity of electric vehicles (EVs), the electrification of buses has been progressing recently. To achieve the seamless operation of electric buses (e-Buses) for public transportation, some bus stations should play the role of battery swapping station due to the limited travel range of e-Buses. In this study, we consider the problem of locating battery swapping stations for e-Buses on a passenger bus traffic network. For this purpose, we propose three integer programming models (set-covering-based model, flow-based model and path-based model) to model the problem of minimizing the number of stations needed. The models are applied and tested on the current bus routes in the Seoul metropolitan area of South Korea.
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... A linear programming model was developed in [6] to maximize the daily operation profit of a BSS while taking into account the BSS demand of users and the charging and discharging balance of batteries in the BSS. The problem of locating a BSS for electric buses on a passenger bus traffic network was studied in [7], with an objective of minimizing the number of stations needed. In [8], an optimization model was proposed for the BSS operating model considers the day-ahead scheduling process, so as to inform stakeholders on the design and operation of BSS stations, allowing market decisions that can exploit storage capabilities of the BSS. ...
Penetration of electric vehicles (EVs) into the market is expected to be significant in the near future, leading to an significant increase in EV charging demand, and that will create a surge in the demand for electrical energy. In this context, there is a need to find intelligent and cost effective means to make better use of electricity resources, improve the system flexibility, and slow the growth in demand. Therefore, swapping EV batteries rather than traditionally charging them can serve as flexible sources to provide capacity support for the power distribution grid when they are charged during off-peak periods prior to their swapping at the station. This paper presents a novel mathematical optimization model to assess distribution system margins considering different EV charging infrastructures. The proposed model maximizes the distribution system margins while considering the flexibility of battery swapping station loads and distribution grid limitations. To demonstrate the effectiveness of the proposed model, simulation results that consider the National Household Travel Survey data and a 32-bus distribution system are reported and discussed. Unlike charging EV batteries, swapping them would not affect system margins during the peak hours.
... The main charging mode for electric buses (E-buses) is wired charging, which has two options: plug-in charging (e.g., at charging stations or bus terminals) [3][4][5] and battery-swapping charging [6][7][8]. Although less common, static and dynamic wireless charging [9][10][11] is also a viable option. ...
This paper investigates the design problem of an electric bus (E-bus) route with charging stations to smooth the operations between E-bus service and charging. The design variables include the locations of E-bus stops, number of charging piles at charging stations, fare, and headway. A mathematical programming model is proposed to maximize social welfare in consideration of the uncertain charging demand at charging stations. The model solution algorithm is also designed. The model and algorithm are demonstrated on the E-bus route 931 in the city of Suzhou, China. The results of the case studies show that (i) the right number of stops on a bus route can contribute to the highest social welfare; (ii) the pile–bus ratio decreases with the increase of E-bus fleet size, thereby improving the E-bus charging efficiency at charging stations; and (iii) deploying charging stations at one end of a bus route can achieve a shorter waiting time for E-bus compared with deployment at two ends.
... The electric vehicle, which, in this case, is an electric bus, arrives at the exchange station and interacts with the exchange system, which replaces the discharged battery with a fully charged one within a few minutes. In these cases, the proposed models serve to offer a hypothesis on the timing and positioning of the battery exchange stations [13][14][15][16]. The aim of this solution is to increase the battery life through ensuring a lower degree of degradation during the charging phase compared to fast charging. ...
Urban mobility scenarios are constantly evolving, and today’s solutions may not be adequate in the future. Through innovative analysis and design methods encapsulated by the IDeS methodology, it is possible to plausibly hypothesize a number of key scenarios to be analyzed, for which vehicles can be designed in order to solve the main problems. Scenarios such as the steady growth in public mobility, based on the sharing of electric mini-buses at the expense of the privatization of the means of transport, lead to the gradual rethinking of citizens’ needs and the supporting infrastructure. Problems such as the lack of privacy of public vehicles, the efficiency of the infrastructure and recharging modes of e-buses, and autonomous driving are addressed here through methods such as QFD (quality function deployment) and SDE (stylistic design engineering), with the aim of outlining a proposal that, to date, is futuristic but is designed to be concrete and feasible within the next decade. These methodologies were applied to the design of a sustainable urban transport system consisting of an electric mini-bus, effected by rethinking the layout of the interior spaces in favor of areas enabling greater privacy and a mobile recharging system (MBS) capable of offering a new management strategy for the non-stop recharging phase. Through the use of an MBS, which functions as a mobile ‘energy bank’ module that is capable of autonomously reaching a mini-bus in need of recharging and extending its autonomy by connecting and recharging it, the proposed system can potentially be enabled to perform its required service during the day without any need to spend time making intermediate stops for the purpose of recharging.
... Battery swapping services are already in place in different regions of the world adapted to different types of vehicles, like scooters, cars or buses, to increase the uptake of EVs (Huang, 2020;Moon et al., 2020). In the Chinese EV market, car manufacturers such as Nio and Beijing Automotive Industrial Corporation (BAIC) have a battery swapping service. ...
The materials and energy density of current electric vehicles (EV) battery technology means that the vehicles are heavier and have a shorter range in comparison to internal combustion engine vehicles (ICEV). Battery cost also means EVs are relatively expensive for the consumer, even with government incentives, and dependent on sometimes-rare resources being available. These factors also limit the applicability of battery-electric technologies to heavy-duty vehicles. However, a number of next generation technologies are under laboratory development which could radically change this situation. Using a follow-the-money methodology, the strategic innovations of companies and public institutions are examined. The chapter will review the potential for changes in resource inputs, higher-density batteries and cost reductions, considering options such as lithium-air, metal-air and solid-state technologies. The innovations outlined in these technologies are considered from an economic perspective, identifying their advantages and disadvantages in commercialisation. At the same time, innovations, and investments in infrastructure electrification (Electric Road Service) and battery exchange point with swapping technology will be also considered due their implications and contribution to solving battery-related challenges and shortcomings. It is concluded that only a joint investment in effort on technologies would allow the use of EVs to be extended to a broad public in terms both of users and geography.
... Assumptions: (a) Due to battery aging, it is assumed that BEV battery's life is 12 years, since the battery will have lost a significant part of its capacity after this period; this is an acceptable assumption since published data on battery lifespan ranges from 8 to 15 years [29][30][31]. Battery swapping models are complex and currently not foreseen for passenger vehicles (although they could be viable for electric buses in public transportation [32]), so it is assumed the vehicle needs to be scrapped after the 12 years lifetime, even if the maximal life cycle mileage has not been achieved. (b) Therefore, for each data point, LCA calculations are based on the life cycle maximal mileage, if achieved. ...
Electric mobility is considered a solution to reduce carbon emissions. We expanded a lifecycle assessment with data on technical limitations and driving habits (based on real-world data) in order to identify the environmentally optimal drivetrain for each individual driving behavior with current and projected technologies, focusing on CO2 emissions. By combining all data, an environmentally optimal European drivetrain mix is calculated, which is dominated by fuel-cell electric vehicles (50% in 2020, 47% in 2030), followed by plug-in hybrid-electric vehicles (37%, 40%), battery-electric vehicles (BEV) (5%, 12%), and Diesel vehicles (2%, 1%). Driving behavior defines the most environmental drivetrain and the coexistence of different drivetrains is currently still necessary. Such information is crucial to identify limitations and unmet technological needs for full electrification. If range is not considered a limitation, the environmentally optimal drivetrain mix is dominated by BEVs (71%, 75%), followed by fuel cell electric vehicles (25%, 19%) and plug-in electric vehicles (4%, 6%). This confirms the potential environmental benefits of BEVs for current and future transportation. Developments in battery energy density, charging, and sustainable production, as well as a change in driving behavior, will be crucial to make BEVs the environmentally optimal drivetrain choice.
Electric vehicle development faces the challenges of range anxiety and the high construction and operating costs of charging and battery-swap infrastructure. To address these issues, this paper describes a proposed battery swap network comprising battery-swapping-van service points and battery-swap stations to provide local, fast and cost-effective battery-swapping services. A data-driven approach was developed to analyse and model vehicle trajectory data to obtain realistic battery-swap demand distributions. An optimisation model based on location cover was then developed to minimise the cost of a battery-swapping network using a greedy algorithm. Real traffic network driving distance was adopted to improve authenticity and accuracy. In a case study, a battery-swapping network used a one-week trajectory dataset of taxis in Beijing and tested the effects in eight scenarios. The results showed that the mesh size of service points, service radius and coverage rate all had an impact on service quality and cost. It is concluded that a battery-swapping network could be cost-effectively deployed by adjusting these parameters to suit the needs of electric vehicles at different development stages. This paper provides useful a reference for decision-makers and operators to formulate policies and strategies.
In light of the growing concerns of global climate change, the pace of transportation electrification has greatly accelerated in recent years as an effort towards net-zero greenhouse gas (GHG) emissions. However, it remains unclear how to effectively deploy and operate public charging infrastructure to best serve an electrified transportation system within a multi-modal context while maximizing the benefits of decarbonization. This is especially true when considering the GHG emitted by generating one kWh of electricity, i.e. the electricity carbon intensity, varies across a day due the change of generation mix between renewable and fossil fueled resources. To address this question, we propose a mechanism of shared charging hubs that can provide holistic energy management for both electric buses (EBs) and passenger electric vehicles (EVs). The deployment and operation of shared charging hubs is determined by a new spatio-temporal optimization model which aims to minimize GHG emission given a budget limit while avoiding the occurrence of massive spikes in peak power demand. This is achieved by coherently accommodating the charging demand of EBs and EVs, and explicitly integrating the time-varying electricity carbon intensity and vehicle-to-grid (V2G) technology. To demonstrate its effectiveness, the model is applied to the bus fleets operated by seven transit agencies and the park-and-ride facilities (for EVs) near twelve rail transit stations in Contra Costa County, California, USA. The results show that the shared charging hubs can lead to significant GHG emission reduction while mitigating the peak electricity demand. This research will help policymakers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure.
The continuous improvement of charging technologies makes fast charging applicable to overcoming the limited driving range of electric vehicles by providing en-route opportunity charging. Fast chargers can be deployed at bus stops to top up the battery for an electric bus during its dwelling time when passengers board and alight, which allows the usage of small size battery and avoids deadheading trips to depot or charging station for recharging. This paper examines the joint optimisation problem of locating fast chargers at selected bus stops and developing charging schedule to support electric bus operation considering time-varying electricity price and penalty cost if passengers’ extra waiting time, the delay time of starting a trip or the excessive travel time of completing a trip is caused by charging activities. To handle the uncertainties associated with the travel time and passenger demand, a robust model is proposed and formulated. The proposed methods are applied to multiple bus routes in Sydney in the numerical studies. The numerical results show that frequent charging activities occur at both intermediate and last stops during bus service trips, indicating that it is beneficial for buses to utilise passengers’ boarding and alighting time at intermediate stops and resting time at the last stop between two adjacent trips to top up battery. Sensitivity analysis on a series of system parameters, such as the acquisition cost of chargers, amortized battery price and charging power, is conducted. The numerical results verify the feasibility of deployment of fast chargers and corresponding top-up charging activities at both intermediate and last stops. Electric buses outperform the conventional diesel buses regarding energy consumption cost, especially considering the increasing diesel fuel price and its costly environmental impacts. Moreover, charging activities at intermediate stops will become more common when passenger demand (the volume of boarding and alighting passengers) increases at intermediate stops.
The environmental impact of battery electric vehicles (BEVs) largely depends on the environmental profile of the national electric power grid that enables their operation. The purpose of this study is to analyze the environmental performance of BEV usage in Korea considering the changes and trajectory of the national power roadmap. We examined the environmental performance using a weighted environmental index, considering eight impact categories. The results showed that the weighted environmental impact of Korea’s national power grid supply would increase overall by 66% from 2015 to 2029 using the plan laid out by the 7th Power Roadmap, and by only 33% from 2017 to 2031 using the 8th Power Roadmap plan. This change reflects the substantial amount of renewables in the more recent power mix plan. In 2016, BEV usage in Korea resulted in emissions reductions of about 37% compared with diesel passenger vehicles, and 41% compared with gasoline vehicles per kilometer driven (100 g CO2e/km versus 158 g and 170 g CO2e/km, respectively) related to transportation sector. By 2030, BEV usage in Korea is expected to achieve a greater emissions reduction of about 53% compared with diesel vehicles and 56% compared with gasoline vehicles. However, trade-offs are also expected because of increased particulate matter (PM) pollution, which we anticipate to increase by 84% compared with 2016 conditions. Despite these projected increases in PM emissions, increased BEV usage in Korea is expected to result in important global and local benefits through reductions of climate-changing greenhouse gas (GHG) emissions.
Globally, transportation sector has emerged as one of the major sources of air pollution. Correspondingly, green mobility (with electric bus) is gaining increasing attention as an essential step to mitigate emission concern. As such, a proper electric bus network design and fleet planning is important especially for bus operator to acquire an adequate number of electric bus, right on time, in order to operate electric bus system viably. Thus, this paper aims to examine the possibility to operate electric bus as a replacement for the conventional bus operation (with natural gas buses for the study area in Putrajaya, Malaysia). In order to determine a proper-designed electric bus operating system in terms of electric bus route, service frequency and quantity, the proposed methodology is developed with the aid of a traffic modeling software to cater various scenarios. Based on the existing (conventional) traffic and transit system in Putrajaya, the developed electric bus operating model is calibrated accordingly by considering various operational concerns including battery capacity and charging facility. The resultant findings revealed that the developed electric bus operating system in Putrajaya outperforms the conventional bus operation, not only in generating a higher profit margin for the bus operator, but also satisfying the passengers in a better manner (by carrying more passengers per unit of bus with a lower energy consumption).
Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
In China, a significant amount of activity is focused on electric vehicles (EV) in recent years. Propelled by the national government, electric buses powered by installed lithium-ion batteries are operating on some metropolitan streets of China. With the drawbacks of short range and long recharge time, the technical features and operating characteristics of battery electric buses and conventional diesel vehicles are different. These differences lead to great changes of the vehicle scheduling method. It is necessary to learn more about the specific scheduling method of electric buses to update traditional bus operation, management rules and scheduling methods. This paper proposes a study to establish a single depot vehicle scheduling model (SDVS) with specific constraints concerning the operation features of electric buses to solve the electric vehicle scheduling problem. Two independent objective functions of minimizing the capital investment for the electric fleet and the total charging demand in stations are involved in the model. To solve the problem, a modified multi-objective optimization method adopting the basic idea of Non-dominated Sorting Genetic Algorithm (NSGA- II) is established. This analysis is performed by solving the scheduling problem for the electric bus demonstration project to be undertaken in Shanghai. Results are reported and verify the practicability of the method proposed in this paper. These findings were valuable to assist the electric bus operation and EV applications. (C) 2013 The Authors. Published by Elsevier Ltd.
Battery electric buses are seen as a well-suited technology for the electrification of road-based public transport. However, the transition process from conventional diesel to electric buses faces major hurdles caused by range limitations and required charging times of battery buses. This work addresses these constraints and provides a methodology for the cost-optimized planning of depot charging battery bus fleets and their corresponding charging infrastructure. The defined problem covers the scheduling of battery buses, the fleet composition, and the optimization of charging infrastructure in a joint process. Vehicle schedule adjustments are monetized and evaluated together with the investment and operational costs of the bus system. The resulting total cost of ownership enables a comparison of technical alternatives on a system level, which makes this approach especially promising for feasibility studies comprising a wide range of technical concepts. Two scenarios of European cities are analyzed and discussed in a case study, revealing that the cost structure is influenced significantly by the considered bus type and its technical specifications. For example, the total energy consumption of the considered lightweight bus is up to 32% lower than the total consumption of the high range bus, although the deadheading mileage increases. However, the total costs of ownership for operating both bus types are relatively close, due to the increased fleet size and driver expenses required for the lightweight bus system. The case study furthermore reveals that a mixed fleet of different bus types could be advantageous depending on the operational characteristics of the bus route.
To reduce greenhouse gas emissions in transportation sector, battery electric vehicle (BEV) is a better choice towards the ultimate goal of zero-emission. However, the shortened range, extended recharging time and insufficient charging facilities hinder the wide adoption of BEV. Recently, a wireless power transfer technology, which can provide dynamic recharging when vehicles are moving on roadway, has the potential to solve these problems. The dynamic recharging facilities, if widely applied on road network, can allow travelers to drive in unlimited range without stopping to recharge. This paper aims to study the complex charging facilities location problem, assuming the wireless charging is technologically mature and a new type of wireless recharging BEV is available to be selected by consumers in the future other than the traditional BEV requiring fixed and static charging stations. The objective is to assist the government planners on optimally locating multiple types of BEV recharging facilities to satisfy the need of different BEV types within a given budget to minimize the public social cost. Road users’ ownership choice among multiple types BEV and BEV drivers’ routing choice behavior are both explicitly considered. A tri-level programming is then developed to model the presented problem. The formulated model is first treated as a black-box optimization, and then solved by an efficient surface response approximation model based solution algorithm.
Introduction
Variants of the Location Routing Problem
Exact Algorithms for the Solution of the Location Routing Problem
Heuristic Algorithms for the Solution of the Location Routing Problem
Metaheuristic Algorithms for the Solution of the Location Routing Problem
References
This chapter overviews the most relevant contributions on location-routing problems. Although there exist many different models where location and routing decisions must be made in an integrated way, the chapter focuses on the so-called classical location-routing problems without entering into the details of other related problems that might be included in the location-routing area from a more general point of view. Reflecting the imbalance in the existing literature and available approaches, the case of problems with node routing is treated in detail throughout the chapter, while results concerning arc routing problems are concentrated in a single section.
As climate change and severe air pollution becomes life-threatening, numerous attempts are made to encourage the use of electric vehicles as alternative fuel vehicles. One of such efforts would be the introduction of electric taxis supported by the battery exchange station (BES) system. This paper presents the framework on how the BESs should be located in a city considering the demand and practical constraints using Seoul, South Korea as the case study. As a solution approach, p-median models are applied for 25 candidate locations and modeling results are presented and interpreted.