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In order to overcome the problems of electric vehicle (EV) range anxiety and inadequate charging station, a driver assistance technology is presented here. The proposed technology assists a driver to maintain an efficient driving strategy (EDS) that minimizes energy consumption toward increasing the driving range. Additionally, it provides an anticipated range of EV corresponding to its current battery charge which alerts the driver to take a decision for required charging. EDS is determined by solving a multi-objective optimization problem corresponding to the current route characteristics. Optimization problem considers three primary objectives, minimization of energy consumption and travel time and maximize journey comfort, and follows an EV drive model. Effectiveness of the proposed technology is identified by analyzing the effect of adopting EDS in a micro-trip planning with an E-bus based on experimental drive data. A drive model for the E-bus is formulated and validated through comparative analysis of speed, motor current, and voltage that are obtained in simulation and experimentation. Such analysis was performed independently in four micro-trips identified from E-bus drive data following a typical route. It was found that the driver assistance technology reduces both energy consumption and travel time than that found by following a random driving strategy. [ABSTRACT FROM AUTHOR]

This paper analyzes whether introducing more electric vehicle infrastructures, such as charging stations, in urban areas will increase the number of electric vehicles used and use more renewable energy in the transportation sector. The study involves case studies from the USA, Saudi Arabia, South Africa, Germany, and China. A total of 250 participants, 50 from electric vehicles in the country, were identified to participate in the chat and Gmail interviews. The results indicated that establishing electric vehicles infrastructure in urban areas can increase the adoption of renewable energy in the transport sector by reducing congestion, attracting more users, part of the government's plans, ensuring convenience, taking advantage of the low electricity rates, and reducing range anxiety by the drivers. Therefore, countries should be advised to increase the number of electric vehicle infrastructures in urban areas to improve their use of renewable energy in the transportation industry. Policymakers and urban planners can acquire ideas for improving the outlook of the areas from this study. However, the research was limited due to the small sample size. Further research should focus on the right strategies for including more infrastructure in the urban locations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the energy efficiency of pure electric vehicles, the author proposes a thermal energy recovery and automation monitoring system for motor equipment based on new energy vehicles. Based on the analysis of the battery thermal management model, a series heat exchange model between the motor and the battery was established. An integrated thermal management system control strategy based on motor waste heat recovery was proposed for electric vehicles in low temperature environments, and simulation and validation were conducted at different environmental temperatures (-10~0 °C). The simulation results show that this configuration can shorten the heating time by 111-343 seconds compared to traditional battery thermal management systems during the heating stage. During the insulation stage, frequent activation of positive temperature coefficient thermistor can be avoided and the battery temperature can be maintained around 20 °C, which is not only conducive to extending the battery life but also reduces the comprehensive energy consumption by 4.39-7.70%. On the other hand, this configuration can reduce the impact of low temperature on comprehensive energy consumption from 9.77-2.07%. It is proved that heat recovery can effectively alleviate the range anxiety caused by ambient temperature. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have gradually gained traction in India over the past decade, driven by advancements in battery and electric powertrain technologies, affordable battery prices, improved charging infrastructure, and a shift in consumer preference. However, despite this progress, there are still obstacles-both technical and psychological-hindering the widespread adoption of EVs. This research aims to address these challenges by focusing on the acceptance of standardized battery packs (SBP) for battery swapping. A descriptive research survey was conducted among key stakeholders in India's ecosystem. The survey utilized a simple random sampling method and employed various statistical tools, including ANOVA, Chi-square, Correlation, and Regression, using the IBM SPSS statistical tool. The results of this research shed light on the potential for growth in battery swapping technology using SBP within the electric vehicle segment. This growth has the potential to alleviate range anxiety and will support the expansion of battery swapping infrastructure, further promoting the adoption of EVs. [ABSTRACT FROM AUTHOR]

The growing interest in EVs as a sustainable transportation option is evident, but hurdles like battery technology limitations, charging infrastructure, range anxiety, and manufacturing sustainability persist. However, the research community is attempting to actively address these barriers with innovative solutions. This paper explores the intricate challenges that are impeding the widespread adoption of Electric Vehicles (EVs) and explores the concerted efforts of the research community towards addressing these obstacles. The challenges addressed are classified as technical, economical, and sustainable for in-depth analysis and the literature that throws light on the direction in which the research is carried out to tackle each challenge is compiled. Also, an analysis is conducted to evaluate the economic viability of EVs based on daily range considerations. From the analysis, it is observed that the customers with higher daily commuting distances are likely to benefit from EV adoption and this distance will have a huge impact on savings over a period of 5 years as high as INR 0.67 million for a daily commuting distance of 100 km. Furthermore, as part of initiatives and plans for accelerating EV adoption in India, policies that are aimed at establishing a harmonious and balanced EV ecosystem are discussed in detail. Overall, the paper sheds light on the evolving pathway towards a future where EVs can thrive as a mainstream mode of transportation. [ABSTRACT FROM AUTHOR]

• The Systematic Review analyses 17 articles concerning Range Anxiety (RA). • RA is faced from a psychological, Human-Factors and Socio-Technical prospective. • RA was measured with PASA questionnaire, scenarios and behavioural analysis. • RA interact with HMI features, coping skills and information, attitude, experience. • Adaptive HMI and consideration of users driving-style are strategies to overcome RA. The use of Battery Electric Vehicles is one of the biggest transformations in the field of sustainable development, holding the potential to address many sustainability challenges. Despite their high energy efficiency, the utilisation of these vehicles remains largely underexplored due to several factors. One of the prominent factors is the phenomenon of range anxiety (RA). This review aims to provide an overview of the range anxiety phenomenon, identifying its primary influencing variables and strategies for mitigating it. The review places particular emphasis on the methodologies and tools employed for assessing range anxiety and related factors. Following PRISMA guidelines, a systematic search was performed in PubMed, Scopus, and APA PsycInfo, using the following keywords: "range" AND ("stress" OR "anxiety") AND ALL ("electric" AND "vehicle"). The protocol was registered with OSF, registration number is https://doi.org/10.17605/OSF.IO/FN4VW. The articles were selected by two independent authors. The methodological quality and risk of bias were assessed. A total of 17 studies were included in the systematic review. Studies were analysed by sample size, conceptual framework, assessment method and results. The findings highlight that an approach grounded in Human-Factors principles and psychological metrics can significantly contribute to addressing range anxiety. More, individual (level of experience, self-efficacy, threatening perceptions, attitude, technology commitment and personality) and interface features (range estimation, State of Charge, adaptability, GPS, charging points map, road information) have a role in influencing the EV usage and the onset of RA. Among the commonly employed methods for assessing range anxiety were questionnaires (PASA), scenario-based analyses, and behavioral observations (including facial expression and gaze analysis). This review shows that the best solution to reduce the range anxiety may be to design improvements that minimize alterations to lifestyle and behaviour in comparison to the traditional vehicle. The most effective approach for mitigating range anxiety might involve designing improvements that minimise disruptions to lifestyle and behaviours when compared to traditional vehicles. As a novel contribution, this research presents a diagram illustrating the significant relationships between range anxiety and individual, socio-technical, and Human-Factors variables. [ABSTRACT FROM AUTHOR]

This paper uses U.S. nationally representative data from the 2017 National Household Travel Survey to present a series of facts about electric vehicles (EVs) in multi-vehicle households. First, as of the time of the survey, 89% of households with an EV also had a non-electric vehicle in addition to the EV. Second, 60% of households with an EV also had a non-electric SUV, truck, or minivan. Third, 66% of households with an EV also had a non-electric vehicle that was driven more miles per year. The paper argues that these patterns have significant implications for the environmental impact of EVs and underscore the importance of better understanding how multi-vehicle households substitute between vehicles. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) is the solution to many problems of carbon emissions, cost of maintenance, raising fuel costs etc. An important problem hindering the rapid adoption of EV is the range anxiety problem. Range anxiety problem can be solved by providing the guarantee to find the charging point within the driving range of the vehicle with other constraints of waiting time, reliability, cost etc. This work proposes a P2P secure and reliable electric charge trading framework on top of VANET infrastructure. Different from existing energy trading solutions, this work is distributed, secure, reliable and provide non repudiation to the transactions. The proposed electric charge trading framework is built on convergence of three concepts of vehicular social networks, named data networking and block chain to address the factors causing range anxiety to EV users. Reliability is addressed through cooperative trust computation model built on vehicular social networks. Security is ensured through authentication of entities before trading and secure exchange of trading parameters. Higher availability of reliable charging stations with multi criteria station selection is facilitated using named data networking. Through simulation analysis, the proposed solution is found to provide 13% more charging supplier selection coverage, filter fake supplier by 18% and 17% lower energy trade establishment time compared to existing works. [ABSTRACT FROM AUTHOR]

The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style. [ABSTRACT FROM AUTHOR]

Range anxiety seriously restricts the development of electric vehicles (EVs). To address the above issue, a hybrid battery swapping system (HBSS) is developed in this paper. In the system, EVs can swap their battery at battery swapping stations or by the roadside via battery swapping vans. The proposed scheduling strategy aims to achieve the best service quality for the HBSS by controlling the mobile swapping service fee. In the model, the uncertainty of EV selection is managed by leveraging the Sigmoid function. Based on proving the uniqueness of the solution, the particle swarm optimization algorithm is used to solve the problem. Simulations validate the effectiveness of the proposed strategy in alleviating range anxiety. Moreover, the impacts of maximum service capacity and the operating rule have been analyzed. [ABSTRACT FROM AUTHOR]

In the last decade, car manufactures invested a lot of effort to align their products to the latest energy directives which encourage the production and usage of electrified vehicles to reduce the greenhouse gases production. This resulted in several important developments, which enhanced the advantages of electric vehicles in terms of local emissions (zero tailpipe emissions), efficiency, convenience in urban areas and others and ultimately led to their ever-increasing adoption. However, there are still some challenges that need to be addressed. One example is the negative influence of low (winter) and high (summer) atmospheric temperatures on electric vehicle range due to the cabin temperature heating and cooling. This requires more efficient ways of using energy to avoid sacrificing the passenger thermal comfort for an increased vehicle range. The present study proposes a new strategy for heating the seats in electrically powered vehicles using an uneven distribution of the heating elements. The uneven positioning of the heating elements is based on the thermal sensitivity of the human skin measured data and scientific literature. For this, a thermal sensitivity test device was developed to map the human skin thermal sensitivity. To test the new solution, a vehicle seat was equipped with heating pads (arranged according to the position of the relevant human skin thermal sensitivity points). For the next step, comparative measurements (power consumption, temperature distribution—with an IR camera—and human subjectivity test) were carried out between a classical vehicle seat heating system and the newly proposed heating solution. The outcome of the study revealed that the proposed heating system will supply at least the same thermal comfort sensation as the standard vehicle seat but using only half of the energy consumption, which translates in an increase of the electrically powered vehicle range between 1.2% and 1.5%, depending on the climate and driving conditions (over the WLTC). For example, a vehicle with a 16 kWh battery driving over the WLTC in Frankfurt climate conditions can gain in 1 year between 139.6 and 164.5 km. [ABSTRACT FROM AUTHOR]

This paper aims to find out the optimal mileage of battery electric vehicles (BEVs) by considering the trade-off between range anxiety and charging times, since frequent charging is a customary way to ease range anxiety for BEV drivers in practice, but declines the life cycle of battery and increases the charging cost. We propose a power function to measure the range anxiety and then solve two types of optimal mileages. The results show that the increment of BEVs' cruising range increases the optimal absolute mileage but decreases the optimal relative mileage, while the improvement of the driver's tolerance to range anxiety increases both. It is concluded that improving the driver's tolerance, such as by expanding charging infrastructures and raising drivers' practical experience with BEVs, is more effective than the increment of BEVs' cruising range. The findings help to understand the optimal mileage of EVs and provide recommendations on the design of BEVs' cruising range. [ABSTRACT FROM AUTHOR]

We document non-linear stock effects in the relationship linking emerging technology adoption and network infrastructure increments. We exploit 2010–2017 data covering nascent to mature electric vehicle (EV) markets across 422 Norwegian municipalities together with two complementary identification strategies: control function regressions of EV sales on flexible polynomials in the stock of charging stations and charging points, and synthetic control methods to quantify the impact of initial infrastructure provision in municipalities that previously had none. Our results are consistent with indirect network effects and the behavioral bias called "range anxiety," and support policies targeting early infrastructure provision to incentivize EV adoption. [ABSTRACT FROM AUTHOR]

As the market share of electric vehicles (EVs) continues to grow, driving range capability has emerged as a primary concern for drivers, car manufacturers, and policymakers. Accurate real-time energy consumption prediction is essential in mitigating range anxiety and fostering the adoption of EVs. Consequently, an EV energy consumption prediction framework that comprehensively considers vehicle and environmental factors, with special attention to individual driving styles and driving conditions, constructed based on long short-term memory (LSTM) and Transformer models is proposed. This framework addresses the issue of long-term dependencies and captures characteristics of time-series data with increased efficiency. Upon rigorous evaluation, the model achieved the mean absolute percentage error (MAPE) of 4.63 % for state of charge (SOC) of EV's battery in experimental dataset, outperforming LSTM and multivariate regression (MLR). The ablation experiment shows that the MAPE of the model is reduced by 18.47 % and 15.27 % respectively after considering the individual driving style of drivers and the driving conditions of vehicles for both two types of vehicles. Based on this framework, a long-distance EV energy consumption prediction strategy based on short-distance is proposed, with a MAPE of 6.7 % when SOC value is reduced from 90 to 40 in the selected dataset. • Different types of factors influencing EV energy consumption are discussed. • A novel framework for predicting EV energy consumption is proposed. • A motion segment division method for EV sampling data is discussed. • A long-distance energy consumption prediction strategy is proposed. [ABSTRACT FROM AUTHOR]

Indonesia has the third-largest motorcycle population in the world. These motorcycles have been known as a source of air pollution. Therefore, shifting the motorcycle to electric-driven based technology is inevitable. However, it is quite challenging as the residents prefer higher performance (in terms of power and speed) of the electric model which is unavailable in the market. Unfortunately, the demand for higher performance models creates other problems, such as the requirement for a bigger battery. This is because there is a range of anxiety phenomena in-vehicle usage. However, the capability to accurately estimate the electric motorcycle’s range does not exist. Therefore, this paper focuses on how to develop an electric scooter model to simulate its performances, especially its range estimation. The modeling approach was the use of an electric scooter longitudinal model developed in MATLAB/Simulink environment. Based on the dimensions and targeted performance, the developed model was simulated to determine its power and energy requirements. It is then validated using an experimental test on a dynamometer and on-road conditions. Based on the experimental data analysis, it can be concluded the developed model is valid and can be used as a basis for the next development of any electric motorcycle.

Electric vehicle (EV) drivers require reliable distance to empty (DTE) indication to plan their trips. In the current study, feed forward neural networks based soft sensors were designed to accurately predict DTE in a Ford Escape EV. The proposed DTE soft sensors were trained on actual drive cycle data and rated DTE using Levenberg Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms. Regression models were also developed for comparisons. Primary results show that the Bayesian Regularization trained soft sensor network with eleven hidden layer neurons achieved the highest testing accuracy (99.64%) among the two layered networks, followed by the Levenberg Marquardt (two layered, eleven hidden layer neurons, testing accuracy 99.62%) and Scaled Conjugate Gradient trained networks (two layered, seven hidden layer neurons, testing accuracy 99.49%). The linear and non linear regression models attained 96.19% and 97.53% accuracies respectively. Deeper soft sensor networks yielded better prediction accuracies at higher computation times. The five layered Bayesian Regularization trained network (with ten neurons in each hidden layer) maximized DTE prediction accuracy to 99.89%, but at the cost of 1175% more training time as compared to the best performing two layered network soft sensor. An optimal choice of prediction accuracy considering reasonable computation timescales can help reduce range anxiety of EV users significantly.

In order to alleviate the range anxiety of electric vehicles（EVs）and reduce the impact of long charging time on daily use, the charging strategy of private EVs is studied. Aiming at the discreteness of private EV usage scenarios, an elastic charging method is proposed. It plans charging scheme to reduce delays to trips by taking advantage of usage gaps. An optimal algorithm for selecting the charging station and capacity is proposed, which makes optimal charging decisions according to the characteristics of the EV battery and the resource competition of surrounding charging stations. The effectiveness of the algorithm is verified by simulating real EV data and usage scenarios. It indicates that the elastic charging strategy can significantly save travel time and eliminate range anxiety for EV users. [ABSTRACT FROM AUTHOR]

Range anxiety remains one of the main hurdles to the widespread adoption of electric vehicles (EVs). To mitigate this issue, accurate energy consumption prediction is required. In this study, a hybrid approach is proposed toward this objective by taking into account driving behavior, road conditions, natural environment, and additional weight. The main components of the EV were simulated using physical and equation-based models. A rich synthetic dataset illustrating different driving scenarios was then constructed. Real-world data were also collected using a city car. A machine learning model was built to relate the mechanical power to the electric power. The proposed predictive method achieved an R 2 of 0.99 on test synthetic data and an R 2 of 0.98 on real-world data. Furthermore, the instantaneous regenerative braking power efficiency as a function of the deceleration level was also investigated in this study. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) will be the next technological leap for urban mobility, however the market penetration rate depends on several factors, including the major hurdle of limited EV charging stations. Equity is also an important factor – ensuring EV charging stations are widespread and available for all segments of the population. This paper outlines an innovative methodology to systematically determine the locations of EV charging stations while considering equity and efficiency to maximize accessibility and usage. The methodology has two levels. First, solving what is known as the Set Covering Location Problem (SCLP) by determining a threshold so that the distance (or travel time) between the consumer and the EV charging station will be less than or equal to a given value. This is a policy-based decision and provides a framework to ensure EV charging stations are ubiquitous and equitable. Second is solving the Maximum Covering Location Problem (MCLP) by considering a series of evaluation criteria to satisfy the demand of the early adopters. Following evaluation of the SCLP and MCLP, selected locations are aggregated by partitions to develop larger scale hubs. These hubs will not only include EV charging stations but will also function as connection points that integrate different modes of transportation.

The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style.

In this study, a secure and coordinated blockchain based energy trading system for Electric Vehicles (EVs) is presented. The major goals of this study are to provide secure and efficient energy trading between EVs and Charging Stations (CSs), and to ensure efficient coordination between EVs. In this study, a consortium blockchain based energy trading algorithm is presented that handles the essential energy requests, discards the redundant requests and calculates the distance between EVs and CSs. Moreover, Matching Pool (M-Pool) and Pairing Pool (P-Pool) are used to store energy and payment requests. Furthermore, a blockchain based mechanism is proposed for ensuring efficient coordination between EVs at the unsignalized crossroads and intersections. The scheme involves efficient communication between EVs and the timely sharing of important messages. In the proposed work, EVs are authenticated using a Registration Authority (RA) before they become part of a Vehicular Energy Network (VEN). The increase in the number of EVs results in an increase in the number of messages leading to data redundancy issue. To solve the issue, message filtration is performed. The delays incurred in the VEN are also mathematically formulated in this study. In addition, the range anxiety issue is discussed in the proposed work. Besides, a Local Aggregator (LAG) is used as an energy broker to manage energy trading events and transaction validation. To promote the users to take part in the proposed network, they are provided with incentives. The proposed model is tested against selfish mining attack and the security analysis is performed through Oyente tool. The simulation results show that the proposed study excels in providing a secure, efficient and coordinated energy trading and data sharing system for EVs. The results show that due to proper coordination, the risk factor is reduced by almost 25–30%. Moreover, almost 40–50% reduction in time is observed when storing less redundant data. [ABSTRACT FROM AUTHOR]

Remote communities in Australia face unique mobility challenges that may be further complicated by the transition from Internal Combustion Engine vehicles to Electric Vehicles (EVs). EVs offer numerous advantages including lower maintenance requirements and independence from costly, dangerous and polluting petroleum imports. Yet the adoption of EVs in Australia has been slow by international standards, and what policiesdo exist tend to focus on incentivising uptake among urban residents with the means to afford new technologies, potentially leaving remote communities in the 'too hard basket'. In this study, we assess the feasibility of EVs for communities in remote Australia using Geographic Information System analysis of travel distances between communities and service hub towns utilising present-day EV specifications and charging technologies. We show that, while EV travel is often not currently feasible for trips to large service hub towns using low-range vehicles, over 99% of communities and residents would be able to travel to their nearest small service hub town with existing long-range EVs. This suggests that while the barriers to the electrification of transport in remote communities are significant, they are not insurmountable and are deserving of consideration in national and state policy developments in the deployment of charging infrastructure. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Extreme fast charging (XFC) is one of the most direct means to improve the competitiveness of electric vehicles (EVs) against gasoline vehicles in terms of mileage covered per unit of time (including time to replenish power source). Solid-state batteries (SSBs) with high energy density are more capable of addressing the challenges of range anxiety and XFC safety than traditional lithium-ion batteries (LIBs). However, inadequate interfacial contact, lithium intrusion, and high tortuosity of Li+/e- transport limit the performance of SSBs at high current densities. In this review, we comprehensively explore the multi-layered mechanisms that restrict the XFC capability of SSBs and analyze possible attempts to enhance the acceptable charging current density. We also highlight the unique role of coupled strategies of state-of-the-art characterization techniques and numerical simulation, as well as intelligent charging protocols in addressing the XFC challenges for SSBs. In addition, we systematically summarise the latest achievements of battery companies in developing fast-charging SSBs. Finally, we present several potential strategies for the future development of fast-charging SSBs to alleviate range anxiety and realise the vision of EV ubiquity. [ABSTRACT FROM AUTHOR]

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing. [Display omitted] • An EVCS siting framework along highways is proposed incorporating expert opinions. • The method covers economic, technological, geographical, environmental, and social factors. • The weighted sum method is implemented to solve the EVCS siting problem. • The method is implemented as a case study for the most heavily used highway of Turkey. • 18 out of 31 points are identified as the optimal EVCS locations along the highway. [ABSTRACT FROM AUTHOR]

It is well known that range anxiety faced by electric vehicle (EV) drivers nowadays can be effectively mitigated by either increasing the driving range of vehicles or increasing the distribution density of charging stations. To enact an effective research and development plan, vehicle manufacturers should understand well the relationship between the EV adoption and usage rates and the performance level of battery and energy management technologies. On the other hand, the underlying relationship between EV adoption and usage rates and the number and distribution of charging stations is also critical for charging infrastructure investors in determining their construction plans. This paper is dedicated to analytically identifying two critical driving range thresholds and further reducing these driving range thresholds as much as possible by optimally deploying charging station locations in intercity highway networks. These critical thresholds represent the turning points of individual routing convenience conditions or satisfaction levels for long-distance trips, implying that the inconvenient travel restrictions may be largely overcome if the driving range of EVs goes beyond these thresholds. For this purpose, we develop two integer programming models and dynamic programming algorithms for the driving range threshold identification problems , and extend the modeling framework to accommodate two relevant driving range threshold minimization problems as well, which are solved by a branching algorithm encapsulating the aforementioned dynamic programming procedures. The output of this research offers a set of new analytical tools for EV manufacturers to make strategic decisions on extending the driving range of their products and for charging infrastructure investors to make planning decisions on selecting charging station locations. • Introduce the feasibly and ideally optimal routing conditions and define two driving range thresholds for these conditions. • Formulate two critical driving range threshold identification problems and solve them as optimal network routing problems. • Formulate two critical driving range threshold minimization problems and solve them with a branching algorithm. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver's charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver's charging choice behavior and range anxiety performs better. [ABSTRACT FROM AUTHOR]

Electrification of transportation is needed soon and at significant scale to meet climate goals, but electric vehicle adoption has been slow and there has been little systematic analysis to show that today's electric vehicles meet the needs of drivers. We apply detailed physics-based models of electric vehicles with data on how drivers use their cars on a daily basis. We show that the energy storage limits of today's electric vehicles are outweighed by their high efficiency and the fact that driving in the United States seldom exceeds 100 km of daily travel. When accounting for these factors, we show that the normal daily travel of 85-89% of drivers in the United States can be satisfied with electric vehicles charging with standard 120 V wall outlets at home only. Further, we show that 77-79% of drivers on their normal daily driving will have over 60 km of buffer range for unexpected trips. We quantify the sensitivities to terrain, high ancillary power draw, and battery degradation and show that an extreme case with all trips on a 3% uphill grade still shows the daily travel of 70% of drivers being satisfied with electric vehicles. These findings show that today's electric vehicles can satisfy the daily driving needs of a significant majority of drivers using only 120 V wall outlets that are already the standard across the United States.

Electrification of transportation is needed soon and at significant scale to meet climate goals, but electric vehicle adoption has been slow and there has been little systematic analysis to show that today's electric vehicles meet the needs of drivers. We apply detailed physics-based models of electric vehicles with data on how drivers use their cars on a daily basis. We show that the energy storage limits of today's electric vehicles are outweighed by their high efficiency and the fact that driving in the United States seldom exceeds 100 km of daily travel. When accounting for these factors, we show that the normal daily travel of 85-89% of drivers in the United States can be satisfied with electric vehicles charging with standard 120 V wall outlets at home only. Further, we show that 77-79% of drivers on their normal daily driving will have over 60 km of buffer range for unexpected trips. We quantify the sensitivities to terrain, high ancillary power draw, and battery degradation and show that an extreme case with all trips on a 3% uphill grade still shows the daily travel of 70% of drivers being satisfied with electric vehicles. These findings show that today's electric vehicles can satisfy the daily driving needs of a significant majority of drivers using only 120 V wall outlets that are already the standard across the United States.

Electric Vehicle (EV) technology is the key to emission reduction and energy efficiency improvement targets while decreasing the dependence on fossil fuel. Despite the fact that replacing conventional vehicle powered by internal combustion engine with EVs reduce greenhouse gas emissions, limited range of EVs has been leading their mass deployment towards saturation. This paper performs a scoping review of articles on range anxiety induced among EV users. It explores user's perception and actual affecting factors on range of EVs, and proceeds on to measures suggested in the articles to mitigate range anxiety. [ABSTRACT FROM AUTHOR]

The evolution of electric mobility is a promising strategy for decarbonizing the automotive sector. India is one of the countries that support having at least 30% of new vehicle sales be electric by 2030. To achieve the EV ecosystem to a considerable level, many efforts are required in EV charging infrastructure. Battery swapping is treated as a promising solution to accelerate the mass adoption of EVs in India. It is swapping model as an alternative to a lack of charging stations and to reduce range anxiety. This review paper discuss the role of batter swapping, its importance, pros & cons in 4-wheeler passenger car segments. It highlights the policy guidelines in India related to battery swapping, comparison of swapping vs. charging & ideal places for battery swapping station and role of EV battery pack. The review summary recommends the need to standardize the battery pack to enhance swapping among a single OEM across various name plates and among all OEMs with a collaborative effort. [ABSTRACT FROM AUTHOR]

In order to improve the energy consumption and economy of pure electric vehicles, an integrated heat management system based on motor waste heat recovery was proposed.Based on the analysis of battery thermal management model, a series heat transfer model of motor and battery was established.Aiming at the low temperature environment of electric vehicles, the integrated heat management system control strategy based on motor waste heat recovery was proposed, and the simulation and verification were carried out at different ambient temperatures(-10 ℃～0 ℃).The simulation results show that this configuration can shorten the heating time by 111～343 s compared with the traditional battery thermal management system in the heating stage.In the heat preservation stage, frequent starting of PTC(Positive Temperature Coefficient) can be avoided and the battery temperature can be kept near 20 ℃,which is not only beneficial to prolong the battery life, but also can reduce the comprehensive energy consumption of 4.39%～7.70%.On the other hand, this configuration can reduce the impact of low temperature on comprehensive energy consumption from the 9.77% to 2.07%,effectively alleviating the range anxiety caused by ambient temperature. [ABSTRACT FROM AUTHOR]

Battery electric vehicle (BEV) has been thought as a key factor in decreasing global greenhouse gas emissions and energy conservation. Therefore, recent developments in BEVs have heightened the need for energy consumption prediction. In this paper, an effectively model in virtue of the accurate velocity prediction approach is proposed. Prior to commencing the study, road information and historical driving data are sought from electronic map and realistic road tests respectively. Following processing for these raw data, a semi-physical and semi-empirical model is introduced to tackle the problem for energy consumption calculation. For the velocity prediction, a Markov-chain-based method in conjunction with road information is proposed, which endows energy consumption model with precise velocity profile as input to obtain final results. The feasibility and precision of this method were validated on various road types with acceptable results exhibiting a mean error of less than 2%, highlighting its anticipated preferable performance. [ABSTRACT FROM AUTHOR]

We report an integrated methodology (FMC-XGBoost) that mainly consists of a five-state nonhomogeneous Markov chain model (FMC) and XGBoost model. Unlike those existing methods in which capacity fading processes are assumed to be irreversible, the proposed integrated methodology can combine user-specific driving patterns (UDP) and capacity recovery effects (CRE) to predict battery fading dynamics even with partially available data for an individual battery. The parameters of the constructed FMC model are linked to the known physicochemical and material properties of Li-ion battery fading dynamics, which aims to cognize and predict the primary fading dynamics, and the proposed XGBoost model is to cognizes and predicts the fluctuation dynamics regarding UDP & CRE. To comprehensively verify the capabilities of the proposed integrated methodology, a series of cases and comparisons are conducted and analysed based on partial available fading data by selecting batteries to simulate situations of individual differences and different UDPs & CREs. The averages of MAE, MRE and RMSE are approximately 0.0128, 0.9251%, and 0.0153 respectively even when only 60% of the data are available. All verifications and comparison analyses reveal that the proposed integrated methodology provides an accurate, robust, stable, and general way to cognize and predict battery fading dynamics during usage, and subsequently to alleviate range anxiety for batteries in real applications.

This paper aims to find out the optimal mileage of battery electric vehicles (BEVs) by considering the trade-off between range anxiety and charging times, since frequent charging is a customary way to ease range anxiety for BEV drivers in practice, but declines the life cycle of battery and increases the charging cost. We propose a power function to measure the range anxiety and then solve two types of optimal mileages. The results show that the increment of BEVs’ cruising range increases the optimal absolute mileage but decreases the optimal relative mileage, while the improvement of the driver’s tolerance to range anxiety increases both. It is concluded that improving the driver’s tolerance, such as by expanding charging infrastructures and raising drivers’ practical experience with BEVs, is more effective than the increment of BEVs’ cruising range. The findings help to understand the optimal mileage of EVs and provide recommendations on the design of BEVs’ cruising range.