Search

International audience; To alleviate the range anxiety of drivers and time-consuming charging for electric buses (eBuses), opportunity fast-charging has gradually been utilized. Considering that eBuses have operational tasks, identifying an optimal charging scheduling will be needed. However, in the real world, arrival time and state of charge (SOC) of eBuses are uncertain. Therefore, it is challenging for the charging station to efficiently schedule charging tasks. To solve the problem, this paper develops a two-stage stochastic eBus charging scheduling model. In the first stage, eBuses are assigned to designated chargers. After the arrival time and SOC are realized, the second stage determines the charging sequence of eBuses on each charger. The objective is to minimize the penalty cost of tardiness by determining the charging start time and the corresponding charging duration time. Then, a sample average approximation (SAA) algorithm is applied. Additional numerical experiments are performed to verify the efficiency of the stochastic programming model and algorithm.

One of the growing innovations in the electric vehicle market concerns light electric vehicles (LEVs), promoted at local and national level by many initiatives, such as the European project ELVITEN, involving six cities, which is analysed in the present paper in relation to the Genoa pilot case study. In Italy, LEVs have been increasingly successful, as the number of their registrations shows (+76% in 2019 compared to 2018). In this context, the city of Genoa, where a considerable fleet of mopeds and motorcycles (214,499 in its metropolitan area in 2018) circulates, lends itself well to the experimentation of two-wheeled LEVs. The monitoring of the use of LEVs within the framework of the ELVITEN project has shown that the average daily round trips recorded in the metropolitan area of Genoa are equal to 15–20 km, thus reinforcing the idea that LEVs represent a valid alternative to Internal Combustion Engine (ICE) private vehicles. Moreover, the characteristics of the travel monitored and the users’ feedback highlight that the question of range anxiety is less present than expected. Finally, and contrary to our expectations, the data analysis indicates that the use of LEVs in Genoa during two months of Covid-19 pandemic lockdown—March and April 2020—shows a decrease of 21%, while the average decrease recorded by the six cities globally considered is 51%.

As the environmental problems increasing seriously, governments have to take environmental protection as one of the most important development directions in the future. Large emissions of greenhouse gases (GHGs) will accelerate the greenhouse effect. Comparing with the traditional fuel vehicles, electric vehicle (EV) is a cleaner technology with lower emissions, which can slow the pace of global warming effectively and it has been promoted by governments around the world vigorously. However, there are still some challenges in the development of EV, such as range anxiety, battery safety and so on which will influence consumers’ choices. In order to promote the promotion of new energy vehicles, the government has promulgated a series of incentive policies on EV area. Scholars from all over the world have also studied the policy impact in the field of new energy vehicles. By using CiteSpace literature visualization tool, this paper analyzes relevant literature on the web of science (WOS) to determine the policy influence in EV area. The results indicate the research background of this field. In another hand, costumer and energy supply are the two important impact factors in this area.

Electric vehicles (EVs) form an important part of the energy internet, as they connect a transportation network with an electricity network. EV uptake largely depends on the optimization strategies of charging infrastructures such as battery swapping stations (BSSs). These stations can potentially reduce the upfront expenses of EV owners, range anxiety, long charging times and electricity grid strain. Currently, the major challenge in BSSs is the creation of robust business strategies. This chapter proposes BSS stochastic optimization strategies that consider EV uptake uncertainties and power distribution company decisions. Two stochastic optimizations involving two stages are investigated: (a) optimization with recourse and (b) bilevel optimization. The recourse optimization recommends initial battery investment even before the station visits are known in the planning stage and recommends battery allocations in the operation stage. This optimization links a transport network to a distribution line network, providing energy arbitrage and curtailment tractability. The bilevel optimization further links the transport network to a transmission line network using aggregated EV batteries as a form of flexible load to compensate for intermittent renewable source generation. The flexible load is a lower-level decision made by distribution company operators, and the same flexible load is a constraint in the upper-level decisions made by BSS owners. Furthermore, this optimization can link the transportation and electricity networks to a gas network in the presence of gas as a power source with varying marginal prices. The proposed strategies provide a pathway for integrating EVs in the energy internet.

Because of the limitation of battery technology and charging station infrastructure, the electric vehicle has those disadvantages such as the short range of travel, the constraint of capacity, the long charging time, fewer charging stations, the range anxiety of driver et al. Therefore, the study of the electric vehicle routing problem needs to consider more limiting factors. In this paper, the electric vehicle routing problem with time windows mathematical model with minimum total cost objective function which considering the factors that include visiting charging station, partial charging, charging cost was established. The effectiveness of this model was validated with an example that extracted from the Solomon benchmark instances. The result shows that the final routing will be more realistic if we considering more characteristic factors about the electric vehicles in the EVRPTW.

Plug-in electric vehicle (PEV) has experienced major transformations since the last few decades. The success of smart electric grid with the addition of renewable energy solely depends on the extensive diffusion of PEV for a carbon-free and sustainable transport sector. Current technical studies concerning numerous optimization methods connected to PEV-integrated smart electric grid such as battery charging and control, unit commitment, vehicle-to-grid (V2G), solar and wind energy integration along with demand-side management have proved that vehicle electrification is a fast developing arena of research. Charging optimization of PEV is an emerging field which is gradually being implemented in many charging infrastructures at a global scale. A near-comprehensive understanding of smart charging capability is crucial for large participation of PEV. Only proper charging can ensure PEV users to be free from ‘range anxiety’ and switch into the new revolution of green vehicle with less CO2 emissions. This chapter discusses on the aspects of bio-inspired computational intelligence (CI)-based optimizations for efficient charging of PEVs. A holistic assessment of significant research works using bio-inspired CI techniques for PEV charging is presented. A summary of future optimization techniques is also discussed, covering cuckoo search (CS), artificial fish swarm algorithm (AFSA), artificial bee colony (ABC), etc., with broad reviews on previous applied techniques and their overall performances for solving various practical problems in the domain of PEV charging. Furthermore, noteworthy shifts in the direction of hybrid and multi-objective CI techniques are also highlighted in this chapter.

Oil prices and increased carbon emissions are two of the key issues affecting mainstream transportation globally. Hence, EVs (Electric Vehicles) are becoming popular as they do not depend on oil, and the GHG (Greenhouse Gases) do not contribute to GHG emissions. In fact, their integration with smart grids makes them even more attractive. Although EV adoption is becoming widespread, three groups of challenges need to be addressed. These challenges are associated with EV technology adoption, integration of EVs and smart grids, and the supply chain of EV raw materials. Regarding the EV technology adoption, the risks and challenges include EV battery capacity, drivers’ range anxiety, the impact of auxiliary loads, EV drivers’ behavior, EV owners’ unwillingness to participate in the V2G (Vehicle-to-Grid) program, economic barriers to adopting EVs, difficult EV maintenance, EV performance mismatch between the lab and the real world, need for government regulation, lack of charging infrastructure such as not enough charging stations, and expensive batteries. There are additional challenges concerning the integration with the smart grids such as system overload, high-cost investment in V2G technology, load mismatch, and unmanaged recharging of EV batteries. Finally, there are challenges regarding the consistent supply of the raw materials needed for EVs. This chapter examines these risks and challenges, suggests solutions and provides recommendations for future research.

One suggestion to overcome the range anxiety of electrical vehicle owners is the use of a network of battery swapping stations. To improve the network’s performance, managers can purchase spares and place them in the network’s stations. The battery allocation problem, therefore, is finding the allocation that optimizes the network’s performance. For the performance measure, we consider the window fill rate, that is, the probability that a customer that enters a swapping station will exit it within a certain time window. For the battery swapping network this time window is defined as the customer’s tolerable wait. In our derivation of the window fill rate formulae, we differ from earlier research in that we assume that the time to remove and install a battery is not negligible. We numerically analyze the battery allocation problem for a hypothetical countrywide application in Israel and demonstrate the importance of estimating correctly customers’ tolerable wait. We find that the window fill rate criterion leads to two classes of stations, those that are assigned spares and those that are not. Additionally, we show the savings attained by reducing the swapping time. Finally, we compare between a balanced and imbalanced system (in terms of customer arrival to the various stations) and show that the advantage of each system crucially depends on the length of the tolerable wait.

Dynamic charging technology has the potential to solve the range anxiety problem of electric vehicles. The FABRIC project is assessing the feasibility of using the dynamic charging technology on a large scale. The project developed the ICT platform to support dynamic charging. This paper presents some ICT systems developed within FABRIC.

Several studies have demonstrated that the power system can be significantly impacted by the high penetration levels of PEV charging. Other the other hand, coordinated discharging contains tremendous benefits to the grid. In order to efficiently implement such design principles, many challenging issues exist which include PEV mobility modeling, transmission network selection, tradeoff balancing between the power system technical limitations and drivers’ preferences, and the business revenue modeling for V2G and V2V transactions.

The world is witnessing an accelerating expansion of urban areas and intensive urbanisation. The robust relation between transport infrastructure and urban planning is reflected in how integrated and reliable a system is within the urban fabric. Designing an integrated infrastructure to support full electric vehicle (EV) use is a crucial matter, which worries planning authorities, policy makers, as well as current and potential users. Reducing range anxiety by facilitating access to public recharging facilities is designed to overcome the main barrier that stops potential users to utilise EVs. The uncertainty of having a reliable and integrated charging infrastructure also presents hurdles, and slows down the growing trend of smart ecosystems and sustainable urban communities as a whole. Automotive, battery and utility technologies have formed the cornerstone of the EV industry to compete with currently mainstream means of transport, and to gain more prominence within many regions. Strategically locating public EV charging points will help to pave the way for better market penetration of EVs. This paper analyses real information about EV users in one of these metropolitan areas. A case study of 13 charging points with 48 EV users located in the inner urban core (NE1 postcode district) of a metropolitan area in North East England, the city of Newcastle upon Tyne, incorporating space-time analysis of the EV population, is presented here. Information about usage and charging patterns is collected from the main local service provider in North East England, Charge Your Car (CYC) Ltd. The methodology employed is a clustering analysis. It is conducted as a dimensional analysis technique for data mining and for significant analysis of quantitative data sets. A spatial and temporal analysis of charging patterns is conducted using SPSS and predictive analytics software. The study outcomes provide recommendations, exploring design theory and the implementation of public EV recharging infrastructure. The chapter presents a methodological approach useful for planning authorities, policy makers and commercial agents in evaluating and measuring the degree of usability of the public electric mobility system.

Hawaii’s geographic isolation and historical dependence on imported fossil fuels are the primary cause of its residents having to pay the highest energy prices in the USA. To reduce oil dependence in transportation, the State of Hawaii introduced EV-friendly policies (in 2009) and financial incentives (in 2010) for an early adoption of plug-in electric vehicles (EV) and deployment of associated charging infrastructure. In 2011, University of Hawaii Maui College led a consortium of grant and cost-share partners to plan for mass EV deployment for Maui County in a 2-year project called “Maui Electric Vehicle Alliance.” The “planning” involved regular meetings and discussions among stakeholders, continuous outreach and education, and learning through implementation. An organized group of stakeholders acting as a central repository of information and coordinator of EV-related events while providing opportunities to educate and engage the community is essential to building confidence in this new EV technology and cultivating a change of driver attitude and behavior.