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Electric vehicles have the potential to change the global driving paradigm, significantly reducing the environmental impact of transportation systems. However, charging infrastructure cost and range anxiety impose challenges on rapid technology adoption. This paper proposes an onboard integrated dc charger, leveraging the traction inverter, and motor winding inductance to ensure the minimum incremental mass. The proposed circuit allows the vehicle to connect directly to emerging dc microgrids, utility storage, or renewable energy resources. The system is compatible with power supply voltages higher or lower than the vehicle battery, offering the possibility to charge 400-V batteries from emerging 1000-V supplies, as well as 800-V battery from the existing 600-V dc interfaces. The presented method also provides bidirectional fault blocking capability and bidirectional power transfer, suitable for V2G, G2V, or V2V operation. In addition, this paper proposes simultaneous driving and charging, especially useful for semitrailer trucks applications, significantly increasing operational range. The solution presented in this paper offers a simple and safe charging scheme with the potential to substantially reduce charging infrastructure cost and address range anxiety.

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range...

Electric vehicles (EV) are an emerging mode of transportation, and big cities in the United States have witnessed an ever-growing demand for EV usage. The primary benefit of EVs is the high fuel efficiency by using only electricity, and hence lowers the dependency on fossil fuels and significantly reduces greenhouse gas emissions. Although the number of EVs has increased, the availability of EV charging stations for public use has been disproportionate to its demand. More recently, populations residing in the Southern California region have been faced with challenges such as range anxiety owing to the uneven spatial distribution of charging stations throughout the region. As the EV population continues to expand, identifying hotspots of EV charging and barriers to the equitable access of charging stations have gained much importance. Our study uses a geospatial data fusion approach with spatial statistics to combine EV charging station data, land use information, and American Community Survey (ACS) data at the census block group level in Orange County, California to discover optimal locations to broaden the EV charging network and identify potential equity issues surrounding charging station placements.

Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.

With the development of technology in modern society, more and more electrical and electronic instruments has been invented and used in factories and our daily life. Power supply is the basic and important need of any electrical and electronic appliance, but energy storage technologies for mobile use is far from satisfactory. For instance, one of the most important performance of Electric Vehicle(EV) is the range ability. Because of the constraints of battery capacity and charging consumes a long period of time, range anxiety is the fatal shortcoming of EV.Wireless power transfer(WPT) technology could alleviate the shortcoming of range anxiety to a large extent. With the WPT system’ spread, the charging process could be happening wherever and whenever the EV is parked.In the research area of WPT system, circuits design, coil design, compensation topology and control methods are the four most important parts. Improving the power transfer capacity(including power capacity and the range of transfer distance), system’s reliability and efficiency of the system are the main purposes of this research. By applying medium-frequency and optimized coil design as well as specially designed control strategy, a medium-frequency high-power WPT system is realized.

Range anxiety, lack of charging systems and cost are the main concerns for fast adoption of electric vehicles (EVs). Battery chargers decide the performance and life cycle of the battery packs. Therefore, battery chargers play a vital role to escalate the penetration of the Plug-in EVs (PEVs). PEV battery chargers are classified into onboard and off-board chargers based on its availability inside or outside the vehicle. Onboard chargers do not need any additional infrastructure, which can be easily and directly charged from the ac mains. This paper aims to review the technological status of onboard charging infrastructure including front-end power factor correction (PFC) converters and the battery interface converters. The merits and demerits of all the charger topologies are discussed in detail. Finally, a single-phase 1.4 kW onboard charger with interleaved boost PFC as a front-end converter and phase-shifted full-bridge converter as a battery interface is designed and simulated in the PLECS software platform.

The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO).

Traffic prediction with high accuracy has significance towards traffic facilities scheduling, adaptive traffic control logic, even the urban economic development. EV charging station availability prediction is one of the traffic facilities usage forecasting challenges in the urban planning. With the accurate long-term prediction of each EV charging station availability, drivers could schedule the charging activities wisely and avoid range anxiety. Data driven models are commonly applied to deal with the similar questions. However, due to the complex charging station distribution with topological road network structure and time-variant charging station availability, many widely used algorithms, such as recurrent neural network (RNN), could only extract the pure time-series information during the model training. One direct effect is that the prediction accuracy decreases rapidly over time. The Spatial-Temporal Graph Convolutional Network (STGCN) that consists the Graph Convolutional Network (GCN) considering edge connectivity and the Gated Recurrent Unit (GRU) is proposed to process both spatial and temporal dependence of relevant transportation data in this paper. Then, the spatial-temporal model is deployed on a real testing dataset in Dundee City and experiments show a better performance for EV charging station availability forecasting over a long-term period compared with other baselines and the possibility of real-time application.

Electric vehicles (EVs) are a solution to combat greenhouse gas emissions (GHG) and help renewable integration. The biggest issue with EV in countries where the infrastructure of EV charges is not yet largely implemented is the called “range anxiety”, but in Brazil, this problem has another factor as the high cost of the EV Charger as the vehicles itself. The high cost of EV in Brazil is a limiting factor to growth the infrastructure. This project tackles this difficulty, developing and implementing the electrification of a highway in southern Brazil, which crosses the State of Parana from east to west, connecting cities with EV infrastructure. This study analyzed the EVs available in the region and used its effective range to calculate the distance between EV charging stations trying to reduce the so called “range anxiety”. This project offers charging stations with several types of connectors (in order not to restrict EVs models), also, to encourage the use of electric vehicles on this electrified road, there is no fee during the project period (2 years).

Traction motor forms an indispensable part of electric vehicle system. Making its right choice will ascertain the range anxiety, maximum speed and battery ratings required for the electric vehicle. The radical increase in the cost of rare earth magnet materials has pressurized all the countries to come up with rare earth magnet free motor as the traction choice. Interior permanent magnet synchronous motor (IPMSM) is the most widely used one although it suffers from the risk of demagnetization at higher temperatures. Due to the aforesaid reasons its popularity is diminishing. This scenario clearly indicates the requirement of feasibility check for Synchronous reluctance motor (SynRM) as the traction choice. This paper provides an in-depth discussion about the performance analysis of SynRM under the following heads: Peak torque capability, continuous thermal envelope, inverter rating and drive cycle analysis. Here the efficiency maps across the entire speed and torque range is studied with Worldwide Harmonized Light Vehicles Test Procedure (WLTP) Class 2 drive cycle. Thereafter these performance indies are compared with an IPMSM of comparable power rating. Another important aspect highlighted here is the tradeoff study between cost, weight and efficiency of the designed motor that will bring in a holistic picture.

Electrical Vehicles are being made mandatory in many countries and India hopes to accomplish this feat by 2030. Out of the many problems like charging infrastructure, range anxiety, cost, the main hurdle to be considered is the raw materials required for the battery. The 2W segment looks promising in India compared to other markets owing to a lot of reasons. Few major players have introduced electric models in the 4W segment and many are expected to release new ones in the coming months. It is also seen that incentives from the Government have boosted the sales in many countries and the same is being done in India. This paper focuses on the supply chain of the batteries, Energy required for the complete transition to EV and also discusses cyclic economy in the EV industry which could churn out a lot of opportunities and reduce the cost of the vehicles. The paper also specifies the challenges being faced currently and also emphasizes ways in which the problem becomes an opportunity in a side-by-side basis.

Electric vehicles (EV) are gaining popularity due to their reduced pollution, fewer emissions, and energy savings but has big challenges like driver's range anxiety, that slows down the penetration of electric vehicles. This work focuses on analyzing the impact of different factors affecting the driving range of EVs and developing a deep learning-based optimized system for range prediction using neural networks. An electric vehicle model is developed in MATLAB Simulink to analyze the impact of various factors on the driving range and validation of the range prediction model. A comparative study on different neural network models is done using the Keras framework to select the best suited regression model to predict the driving range based on prediction error. The analysis reveals the impact of various factors like driver behaviour, exploitation environment, battery parameters, and auxiliary loads on the driving range. For the test data, the bidirectional long short-term memory shows the minimum error compared to other sequential models with a mean square error of 0.029 km in the prediction of driving range. The work can be further extended by integrating to driver assistance systems for best route selection, charge scheduling, trip planning etc.

In recent years, electric vehicles have become a new direction for the development of urban low-carbon transportation. However, the unreasonable layout of charging facilities has gradually led to problems such as difficulty in charging in some areas, which has brought range anxiety to EV users and potential buyers and hindered the development of EV to a certain extent. Therefore, an in-depth study of EV users' charging behavior is of great significance to optimize the layout of charging stations. In this paper, the charging behavior of EV users was investigated and analyzed by means of questionnaire survey, and then the influence of personal attributes, travel characteristics and charging behavior on the selection of charging location was studied and discussed by using the multivariate Logit model. Charging locations are divided into three categories: living work area charging stations, shopping malls charging stations and social public charging stations. Living work area charging stations include residential area charging stations and unit/company charging stations. The results show that family annual income, travel time consumption per trip, average number of daily trips, travel purposes, and whether quick charging is usually used have significant effects on users' choice of charging locations.

In the current transition to a smarter and more efficient transportation system, battery electric vehicle mileage and the time required for charging are still two main constraints that need to be overcome to enable a larger penetration of electric vehicles. Moreover, the few charging stations available are a consequence of the “supply and demand” problem. Consequently, wireless dynamic recharging can be a complementary solution to address the problems of light-duty electric mobility and an added-value towards autonomous vehicles. Consequently, this paper presents an innovative approach based on real world mobility patterns collected for a sample in the city of Lisbon, Portugal, to assess users’ electric vehicle feasibility by assessing different recharging scenarios, comparing stationary and dynamic recharging scenarios. The results indicate that at least 15 % more drivers would be eligible to own an electric vehicle if wireless charging was available. Moreover, wireless charging reduces the range of battery used, with stationary charging requiring circa 3.2 times more battery range. The developed approach confirms that wireless dynamic recharging can significantly change the framework of current electric mobility limitations, reducing range anxiety issues, contributing to redesign electric vehicle battery capacity and overcome barriers in stationary charging deployment and availability.

The construction of charging infrastructure has a positive effect on promoting the diffusion of new energy vehicles (NEVs). This study uses natural language processing (NLP) technology to explore consumer preferences for charging infrastructure from consumer comments posted on public social media. The findings show that consumers in first-tier cities pay more attention to charging infrastructure, and the number of comments accounted for 36% of the total. In all comments, consumers are most concerned about charging issues, national policy support, driving range, and installation of private charging piles. Among the charging modes of charging piles, direct current (DC) fast charging is more popular with consumers. The inability to find public charging piles in time to replenish power during travel or high energy consumption caused by air conditioning is the main reason for consumers’ range anxiety. Increasing battery performance, improving charging convenience, and construction of battery swap station are the main ways consumers prefer to increase driving range. Consumers’ preference for charging at home is the main reason for their high attention to the installation of private charging piles. However, the lack of fixed parking spaces and community properties have become the main obstacles to the installation of private charging piles. In addition, consumers in cities with different development levels pay different amounts of attention to each topic of charging infrastructure. Consumers in second-tier and above cities are most concerned about charging issues. Consumers in third-tier and above cities pay significantly more attention to the installation of private charging piles than consumers in fourth-tier and fifth-tier cities. Consumers in each city have almost the same amount of attention to driving range.

Electric vehicles are on the rise, but are still far from reaching the global market share required to achieve climate objectives. While financial and technological adoption barriers are increasingly removed, psychological barriers remain insufficiently addressed on a large scale. Here, we show that car owners substantially underestimate the compatibility of available battery capacities with their individual mobility needs, increasing the demand of long battery ranges and reducing willingness to adopt. We test a simple behavioral intervention in two randomized online experiments in Germany and the U.S.: providing tailored compatibility information reduced range anxiety and increased willingness to pay for electric vehicles. Compatibility information more strongly increased preferences than information about privileged access to charging infrastructure, and selectively increased preferences of car owners for whom an electric vehicle would yield higher financial benefits. This scalable intervention may complement classical policy approaches in achieving a resource-conscious and global electrification of mobility.

Smart autonomous systems are expected to be cognisant, taskable, adaptive and ethical. Digital twin (DT) and parallel intelligence (PI) techniques are ideal candidates to further advance the smart autonomous systems to the next level. The DTPI techniques are under fast deployments in both the energy and transportation sectors due to the inevitable transition from fossil fuel vehicles to electric vehicles (EVs). The dramatic increase in EV charging demand yields a huge power supply gap. Currently, the low coverage and outdated management system of charging infrastructure have led to poor user experience and increased user range anxiety. To this end, we propose a cognitive charging station architecture for future charging infrastructure, which consists of power generation network, energy storage network, and charging network. DTPI techniques enable the cognitive charging stations to provide smart functions such as energy management, energy storage system health management, load management, intelligent maintenance, and smart user services.

In recent years there has been a significant increase in the production of electric vehicles (EVs), in the global strive to reduce polluting gases produced by conventional fossil-fuel driven vehicles. Therefore, many optimization algorithms have been proposed for EV mobility and the charging of battery packs in the stations connected to power grids. However, there are situations in which experimental results are not sufficient, and simulations are needed. In this work, we address the effects of the charge demands of an EV fleet on the grid by considering the attitude of EV drivers, and especially their range anxiety. This influences their decision of when to recharge the battery pack. To this end, an agent-based model has been developed for the simulation of a power grid considering different scenarios based mainly on the state of charge (SOC) of battery packs at the time of the charging requests of EVs at service stations. The results indicate that in general a high battery SOC at the beginning of charging increases the probability of reaching higher power peaks on the grid.

Due to the global crisis of climate change and the increasingly strict emission regulations, governments and industrial sectors have regarded electric vehicles (EVs) as the future replacement of conventional vehicles with internal combustion engines to fulfill the future consumer market requirements. Nevertheless, range anxiety is still one of the biggest obstacles in the transportation electrification process. Therefore, increasing powertrain efficiency is still one of the most critical topics in the research community. This paper compares two of the most widely used energy management strategies with the recently developed nonlinear model predictive control strategy concerning their ability to increase powertrain efficiency. The simulation results demonstrate the performance differences from different points of view.

Personal transport is of high importance in our society and the 2020 pandemic situation has reinforced this situation. At the same time, transport contributes to local emissions, which need to be reduced in the face of climate change. Changing from vehicles with internal combustion engines to light electric vehicles could be one promising approach. Therefore, we need to understand mobility patterns and attitudes towards E-mobility to create sustainable transport solutions that will be broadly accepted. An online survey with N = 432 participants across Europe was conducted. The majority of respondents came from Germany, followed by Italy, Austria and Sweden. Generally, cars are the main vehicle for personal transport. PTWs are used for commuting as well as leisure activity. Driving experience, easier parking and lower maintenance compared to cars are major reasons to choose a PTW. No differences between younger and elderly participants were observed. E-PTWs are primarily avoided due to high costs, range anxiety and expected problems with the charging infrastructure. To support sustainable mobility, these obstacles need to be overcome. One aspect is definitely the provision of better charging infrastructure or electric vehicles with increased range. Hence, given typical trip lengths and purposes, it might seem equally important to tackle prejudices and increase the knowledge about E-mobility with all its potential benefits in the population.

Electric vehicles (EVs) have emerged as the green energy alternative for conventional vehicles. While various governments promote EVs, people feel “range anxiety” because of their limited driving range or charge capacity. A limited number of charging stations are available, which results in a strong demand for predicting energy consumed by EVs. In this paper, machine learning (ML) models such as multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector regression (SVR) were used to investigate the total energy consumption (TEC) by the EVs. The independent variables used for the study include changing real-life situations or external parameters, such as trip distance, tire type, driving style, power, odometer reading, EV model, city, motorway, country roads, air conditioning, and park heating. We compared the ML models’ performance along with the error analysis. A pairwise correlation study showed that trip distance has a high correlation coefficient (0.87) with TEC. XGBoost had better prediction accuracy (~92%) or R2 (0.92). Trip distance, power, heating, and odometer reading were the most important features influencing the TEC, identified using the shapley additive explanations method.

Range anxiety has been one of the largest issues for battery electric vehicle (BEV) adoption. Accurate estimation of EV remaining driving range (RDR) can alleviate this issue by informing drivers of appropriate timings and energy needed to propel their EVs to meet their needs, particularly during a long trip. Vehicle platooning with a short inter-vehicle distance has demonstrated significant energy saving benefits on high-way operation by reducing aerodynamic drag force. On the other hands, vehicle platooning also introduces uncertainties to aerodynamic drag coefficient estimation and model-based RDR estimation algorithms. Therefore, it is a challenging task to precisely estimate the RDR for EVs in vehicle platoon. This work proposes a novel EV RDR estimation method that integrates the real-time estimation of aerodynamic drag coefficient and the operation mode from advanced driver-assistance systems (ADAS) or platooning into a physics-based EV model to improve RDR estimation accuracy in highway operation. Simulation results demonstrate that the proposed estimation method can potentially reduce EV RDR estimation errors when compared to the baseline EV RDR estimation method which is based on historical data.

The high diffusion of electric vehicles is evidenced by every sector magazine or by the catalog of all the manufacturers inserting incessantly new models. But what are the user's most hidden reactions to the new world of vehicles? Is the user ready for the fifth level of automation (fully automatic driving and absence of the driving position)? The purpose of this paper is to present and discuss the psychological aspects that influence the adoption of electric vehicles by users and beyond. Topics such as the egg and chicken paradox (electric vehicles and charging stations, who was born first) but also performance anxiety (range anxiety) will be addressed. Contradictions and irony will characterize this paper.

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.

The search for enhanced energy density is fueling global research in battery science and engineering, where applications spanning consumer electronics, electric vehicles, and grid-scale energy storage are stimulating enormous industrial growth. While higher-energy-density batteries will combat range anxiety hampering EV adoption, it is not the only metric of importance; across a breadth of applications from implanted devices to space exploration, there are various requirements for safer, cheaper, and more durable batteries, as well as those that can be sourced from widely available materials and readily recycled. In this issue of Joule, Zhou and co-workers outline a 500 Wh/kg battery based on anionic redox between Li2O and Li2O2, demonstrated at the pouch cell level, providing a new benchmark for gravimetric energy density.

In this issue of Joule, Dahn and coworkers present the development of a hybrid lithium-ion/lithium-metal cell that allows for an extra boost of capacity when needed. This battery has the potential to help eliminate range anxiety with electric vehicles.

Electric vehicles (EVs) are widely considered to be a solution to the problems of increasing carbon emissions and dependence on fossil fuels. However, the adoption of EVs remains sluggish due to range anxiety, long charging times, and inconvenient and insufficient charging infrastructure. Various problems with EV service operations should be addressed to overcome these challenges. This study reviews the state-of-the-art mathematical modeling-based literature on EV operations management. The literature is classified according to recurring themes, such as EV charging infrastructure planning, EV charging operations, and public policy and business models. In each theme, typical optimization models and algorithms proposed in previous studies are surveyed. The review concludes with a discussion of several possible questions for future research on EV service operations management.

Viewing the relationship between vertical integration and product architecture as the “mirroring hypothesis”, we examine the case of Tesla Motors. Our analysis demonstrates that, by and large, Tesla pursues a high degree of integrality in product architecture and employs significant vertical integration. Such congruence offers support for the mirroring hypothesis in the introductory stage of the electric vehicle industry. Besides dedicating an idiosyncratic architecture to battery issues and customers’ range anxiety, Tesla tailors its organization and strategy to mesh with the demands of its other key stakeholders in the larger electric vehicle ecosystem by gradually mastering an effective systems integration.

Electric freight vehicles have strong potential to reduce emissions stemming from logistics operations; however, their limited range still causes critical limitations. Range anxiety is directly related to the total amount of energy consumed during trips. There are several operational factors that affect the energy consumption of electric vehicles and should be considered for accurate route planning. Among them, ambient temperature arises as a key factor because cabin heating or cooling may significantly increase the energy discharged from the battery during the trip and reduce the driving range. Additionally, cold temperatures decrease the battery efficiency and cause performance losses. In this study, we investigate the effect of ambient temperature on the fleet composition, energy consumption, and routing decisions in last-mile delivery operations. First, we first present the mathematical programming formulation of the problem. Next, we perform an extensive computational study based on benchmark data from the literature. For solving the small-size instances we use a commercial solver. For solving the large-size instances we employ an adaptive large neighborhood search algorithm. Our results show that the route plans made without considering the ambient temperature effect may lead to inefficient operations and disruptions. Specifically, the fleet size and energy consumption can increase by 46% and 81%, respectively, in small-size problems on average due to ambient temperature whereas the average increase can reach 15% and 68%, respectively, in large-size problems. Finally, we present a case study from a logistics company operating in Southern Turkey to provide managerial insights to both researchers and practitioners.

Electric vehicle is a sustainable development which is capable of transforming the transport sector. It is a sustainable alternative to internal combustion engine-based vehicle. Its technology is based on utilization of sustainable energy resources which are eco-friendly and replenishes in nature. Wide deployment of electric vehicle is expected to minimize the challenge of fossil fuel depletion and greenhouse emissions are expected to be reduced. However, despite all aforementioned advantages of the electric vehicle, its wide deployment faces some challenges including cost, size and range anxiety. Electric vehicles have limited range which is one of the major factors affecting its market penetration. Researchers highlighted several strategies/methods of extending its driving range. Thus, this work presents a review of different strategies proposed on range extension of the electric vehicle. The strengths and weaknesses of some of the proposed methods are also presented in this work.

Limited range and charging infrastructure leads to range anxiety of electric vehicle drivers. Current range estimation algorithms are deemed unreliable and large safety margins are reserved to prevent the risk of stranding. Range estimation in general depends on two factors: current battery energy content and the energy consumption forecast on the route to destination. This paper aims at improving the latter by enhancing the forecast with a notion of uncertainty. The prediction algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. Thereby, a central part of the algorithm is the explicit evaluation of the traffic situation by classifying the traffic phases. With the help of this methodology, individual forecasts can be made more precise since they are highly dependent on surrounding traffic. To demonstrate the validity of the algorithms, the performance is evaluated using real test drive data comprising multiple drivers. On the basis of the performance evaluation, both the superiority of stochastic algorithms over deterministic predictions and the improvement of predictive performance by evaluating explicit traffic phases can be shown. Implementing the proposed methodology in modern day electric vehicles could reduce range anxiety and ultimately increase acceptance of electric mobility worldwide.

The purchase of an electric vehicle has become a widely debated topic by the economic community in recent years. However, few personal automobile users, who purchased new cars, have chosen a fully electric vehicle. The novelty of this research came from the assumption that there is a lack of knowledge in this field and fuelled by contradicting information. Potential electric vehicle buyers have developed a real psychosis on the subject, the most consistent motives being among others the charge anxiety. Many researchers consider that the worries of the vehicle users are exaggerated and are not based on reality. Better documentation of the subject would reduce these fears and would increase the process of electrical vehicles absorption. The main objective of this article is to analyse the main anxiety factors by buying an electric vehicle and to reduce the related anxiety. For this purpose, the authors have performed an econometric analysis based on a questionnaire distributed online and face to face in Romania.

The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.

With the growing demand for high specific energy density of lithium-ion battery pack in electric vehicle to relieve range anxiety, thermal stability in abused conditions is becoming increasingly important in battery pack safety design. Most of the fire accidents are resulted from the thermal runaway (TR) of a single cell and then propagate to the battery modules and entire pack. This study focuses on the safety enhancement methods for battery module, which is filled with different interstitial materials. The basic safety unit is composed of 11 commercial 18,650 cylindrical cells, which is isolated from the electric vehicle pack as the test module. The test modules were intentionally triggered into TR by heating wire to evaluate the TR propagation resistance. A model based on finite volume method was established to simulate the TR propagation. The results of both simulation and experiments show that the protection of neighboring cells from different interstitial materials varies significantly. Graphite composite sheet and Al extrusion as interstitial materials could effectively suppress TR propagation. The results also indicate that for safety design of battery pack, thermal path should be effectively controlled, and particularly the combustion of expelled electrolyte must be directed away from adjacent cells.

Widespread adoption of alternative fuel vehicles is being hindered by high vehicle costs and refueling or range limitations. For plug-in electric vehicles, direct-current fast charging (DCFC) is proposed as a solution to support long-distance travel and relieve range anxiety. However, DCFC has also been shown to be potentially more expensive compared to residential or workplace charging. In particular, electricity demand charges can significantly impact electricity cost for fast charging applications. Here we explore technological solutions that can help reduce the electricity cost for electric vehicle fast charging. In particular, we consider thousands of electricity rates available in the United States and real-world vehicle charging load scenarios to assess opportunities to reduce the cost of DCFC by deploying solar photovoltaics (PV) panels and energy storage (battery), and implementing a co-location configuration where a DCFC station is connected to an existing meter within a commercial building. Results show that while the median electricity cost across more than 7000 commercial retail rates remains less than $0.20/kWh for all charging load scenarios considered, cost varies greatly, and some locations do experience significantly higher electricity cost. Co-location is almost always economically viable to mitigate fixed cost and demand charges, but the relative benefit of co-locating diminishes as station size and utilization increase. Energy storage alone can help mitigate demand charges and is more effective at reducing costs for “peaky” or low-utilization loads. On the other hand, PV systems primarily help mitigate energy charges, and are more effective for loads that are more correlated with solar production, even in areas with lower solar resource. PV and energy storage can deploy synergistically to provide cost reductions for DCFC, leveraging their ability to mitigate demand and energy charges.

Dynamically charging electric vehicles (EVs) have the potential to significantly reduce range anxiety and decrease the size of battery required for acceptable range. However, with the main driver for progressing EV technology being the reduction of carbon emissions, consideration of how a dynamic charging system would impact these emissions is required. This study presents a demand-side management method for allocating resources to charge EVs dynamically considering the integration of local renewable generation. A multi-objective optimisation problem is formulated to consider individual users, an energy retailer and a regulator as players with conflicting interests. A 19% reduction in the energy drawn from the power grid is observed over the course of a 24 h period when compared with a first-come-first-served allocation method. This results in a greater reduction in CO(2) emissions of 22% by considering the power grid's make-up at each time interval. Furthermore, a 42% reduction in CO(2) emissions is achieved compared to a system without local renewable energy integration. By varying the weights assigned to the players’ goals, the method can reduce overall demand at peak times and produce a smoother demand profile. System fairness is shown to improve with an average Gini coefficient reduction of 4.32%.

Dynamic wireless power transfer (DWPT), or dynamic wireless charging technology, enables charging-while-driving and offers opportunities for eliminating range anxiety, stimulating market penetration of electric vehicles (EVs), and enhancing the sustainability performance of electrified transportation. However, the deployment of wireless charging lanes on highways and urban road networks can be costly and resource-intensive. A life cycle assessment (LCA) is conducted to compare the sustainability performance of DWPT applied in a network of highways and urban roads for charging electric passenger cars. The assessment compares DWPT to stationary wireless charging and to conventional plug-in charging using a case study of Washtenaw County in Michigan, USA over 20 years. The LCA is based on three key sustainability metrics: costs, greenhouse gas (GHG) emissions, and energy burdens, encompassing not only the use-phase burdens from electricity and fuel, but also the upfront deployment burdens of DWPT infrastructure. A genetic algorithm is applied to optimize the rollout of DWPT infrastructure both spatially and temporally in order to minimize life cycle costs, GHG, and energy burdens: (1) spatial optimization selects road segments to deploy DWPT considering traffic volume, speed, and pavement remaining service life (RSL); (2) temporal optimization determines in which year to deploy DWPT on a particular road segment considering EV market share growth as a function of DWPT coverage rate, future DWPT cost reduction, and charging efficiency improvement. Results indicate that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48%, compared to the non-DWPT scenarios. Roadside solar panels and storage batteries are essential to significantly reduce life cycle energy and GHG burdens but bring additional costs. Breakeven analysis indicates a breakeven year for solar charging benefits to pay back the DWPT infrastructure burdens can be less than 20 years for GHG and energy burdens but longer than 20 years for costs. A monetization of carbon emissions of at least $250 per metric tonne of CO2 is required to shift the optimal “pro-cost” deployment to the optimal “pro-GHG” deployment. A roadway segment with volume greater than about 26,000 vehicle counts per day, speed slower than 55 miles per hour (1 mile ≈ 1.609 km), and pavement RSL shorter than 3 years should be given a high priority for early-stage DWPT deployment.

With the widespread growth in the use of electric vehicles (EVs), there is an urgent demand for fast-charging battery charger so as to alleviate the range anxiety for car users. A novel multipulse flexible-topology thyristor-based rectifier (mPFTTR) is proposed in this paper, which can be a suitable candidate for high-power fast-charging battery charger of the EVs. The proposed architecture has five operating modes: Series mode with 24 pulses (SM24P), parallel mode with 24 pulse, hybrid modes with 24 pulses (HM24P), series mode with 12 pulses, and parallel mode with 12 pulses. To meet the practical requirement of fast charging, the predictive current control strategy combined with multistep constant current charging (MSCCC) is also proposed, and followed by the detailed implementation and analysis. Simulation and experimental verifications are carried out, and both results show that the predictive current control strategy combined with the MSCCC method can charge the EVs with wide-range input voltage, and fast charging is also achieved. Finally, efficiency evaluation and comparisons of total harmonic distortions of the input current are developed between the proposed mPFTTR which operates in HM24P and the conventional thyristor-based rectifier that operates in SM24P.