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• 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]

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]

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]

• Extensive range related knowledge slightly enhanced range experience. • Practical BEV driving experience enhanced range experience. • Range stress was reduced by the encounter of a critical range situation for inexperienced and also experienced BEV drivers. • Results have implications for designing strategies aimed at reducing range stress. The challenge of reducing worldwide greenhouse gas emissions has become increasingly important in recent years. Battery electric vehicles (BEVs) are a promising alternative having the potential for a CO 2 neutral and sustainable utilization over the whole vehicle lifetime. However, BEVs limited range is still considered as one important usage barrier. Reducing drivers' experience of range stress is discussed as one possibility to overcome this barrier. The objective of the present research was to investigate the influence of range related knowledge, practical driving experience and the encounter of a critical range situation (i.e., experience of a small range buffer) on drivers' range stress. A specific focus lied on the effects of critical range situation encounters on experienced BEV drivers. A field experiment with 63 participants (20 experienced drivers and 43 inexperienced drivers) who drove a BEV in a critical range situation was conducted. Results indicate that range related knowledge and practical driving experience reduced range stress. The experience of the critical range situation is related to lower range stress, even for the experienced BEV drivers. The results can be useful to derive strategies and design recommendations aimed at reducing the experience of BEV drivers' range stress. [ABSTRACT FROM AUTHOR]

• A compact mixed-integer programming model is developed. • Path deviation and drivers' range anxiety are considered. • An outer-approximation method is proposed to solve the model. • Numerical experiments in a real-life Texas highway network are conducted. This study addresses the location problem of electric vehicle charging stations considering drivers' range anxiety and path deviation. The problem is to determine the optimal locations of EV charging stations in a network under a limited budget that minimize the accumulated range anxiety of concerned travelers over the entire trips. A compact mixed-integer nonlinear programming model is first developed for the problem without resorting to the path and detailed charging pattern pre-generation. After examining the convexity of the model, we propose an efficient outer-approximation method to obtain the ε-optimal solution to the model. The model is then extended to incorporate the charging impedance, e.g., the charging time and cost. Numerical experiments in a 25-node benchmark network and a real-life Texas highway network demonstrate the efficacy of the proposed models and solution method and analyze the impact of the battery capacity, path deviation tolerance, budget and the subset of OD pairs on the optimal solution and the performance of the system. [ABSTRACT FROM AUTHOR]

• How does battery capacity affect the performance of carsharing programs is simulated. • The discrete-event simulation approach is employed. • Limited battery capacity lowers user satisfaction and vehicle utilization in carsharing programs. • Increasing charging speed, maximum range, or vehicle-to-trip ratio helps promote carsharing. • The negative effect of battery capacity is greatly diminished after charging speed reaches a certain threshold. Aided by mobile computing technology, shared electric vehicles (SEVs) have become an accessible and affordable mobility option. However, limited battery capacity remains a major obstacle for large-scale adoption of SEVs, and greatly undermines their popularity. In this study, a discrete-event simulation approach was employed to estimate how battery capacity affects the performance of a carsharing program. Results show that limited battery capacity lowered user satisfaction and vehicle utilization in the program. Increased charging speed, maximum range, and vehicle-to-trip ratio help mitigate these negative effects. Specifically, increasing the maximum range or charging speed contributes to the increment of the average SEV usage time and the percentage of satisfied rental requests. A higher vehicle-to-trip ratio contributes to a greater level of user satisfaction but a lower level of vehicle utilization. Additionally, the negative effects of battery capacity are greatly diminished after charging speed is increased to a certain threshold. These findings help capture the trade-off between charging facility investment, vehicle utilization, and user satisfaction. Increasing charging speed and maximum range are necessary if operators want to maximize vehicle utilization and promote user satisfaction. However, this investment must also account for cost-effectiveness. [ABSTRACT FROM AUTHOR]

• A new en-route charging station location problem for intercity highway networks is defined and formulated, the prominent feature of which is that the heterogeneous driving range of electric vehicles is distributed in an arbitrary continuous form. • The impact of limited driving ranges on both route and trip/mode choice behaviors is explicitly considered and minimization of both travel efficiency and preference is set into the model. • A branch-and-bound algorithm and a neighborhood search heuristic, both of which embeds a multi-criteria label-correcting algorithm for deriving the underlying route-and-charge flow pattern, are designed, implemented and tested. This paper addresses a new optimal charging station location problem for intercity highway networks where electric vehicles are of heterogeneous driving ranges and usually need to be charged multiple times in their long-haul trips. Driving range heterogeneity can be measured in practice by sampling the driving population under a variety of physical, environmental and psychological conditions and is characterized in this study by a continuous distribution of some appropriate forms. The behavioral assumption underlying this problem is that all individual drivers choose their optimal route-and-charge choices if they can make a trip by driving an electric vehicle or cancel their trips or switch to other transportation modes if they cannot. By explicitly taking into account the impact of limited driving ranges on both route and trip/mode choices, we constructed a mixed integer linear programming model for formulating the charging station location problem, the goal of which is to maximize the networkwide travel efficiency and preference subject to a limited infrastructure investment budget. To tackle this integer programming model, we developed a branch-and-bound algorithm and a neighborhood search heuristic, in both of which a multi-criterion label-correcting algorithm is embedded for deriving the underlying route-and-charge flow pattern. For justifying the effectiveness and efficiency of the proposed algorithms, a synthetic network and two real-world networks are employed as numerical examples. The computational results obtained from the numerical analysis show that the heuristic is capable of obtaining optimal solutions in most test scenarios and is much more computationally efficient than the branch-and-bound algorithm, with a computing time that is only about one tenth of the latter. [ABSTRACT FROM AUTHOR]

This paper studied the range-constrained traffic assignment problem (RTAP), where heterogeneous range anxiety is considered among the driving population by electric vehicles (EVs). In order not to get stranded en-route, each EV driver is assumed to have his/her own driving range limit for being able to complete the trip. As a result, two types of multi-class RTAP can be defined through discrete or continuous distributed range anxiety. Given path-based side constraint structures, we proposed two variational inequality (VI) formulations for modeling discrete and continuous RTAPs, where the former is finite-dimensional according to a discrete number of user classes and the latter is infinite-dimensional accounting for an infinite number of user classes. We reformulate the continuous RTAP into finite-dimensional by merging adjacent EV drivers into one group. A unified path-based solution framework is developed to solve the two RTAPs, built upon the gradient projection algorithm. We design column generation and dropping schemes to adaptively maintain compact path sets and an inner equilibration strategy to accelerate convergence. Finally, a small network is used to examine the correctness and effectiveness of proposed models, and a large Winnipeg network is adopted to evaluate the impacts of stochastic driving range on network flows and computation costs. Numerical results provide compelling evidence of the outstanding superiority of the proposed algorithm, and show that EV drivers with heightened sensitivity towards range anxiety may contribute to the emergence of critical links experiencing severe congestion. • General VI formulations for range-constrained traffic assignment problem (RTAP). • Unified path-based solution framework is developed to solve two types of RTAPs. • We design column management and inner equilibration strategies. • Model features and solution efficiency of RTAPs are illustrated using two networks. [ABSTRACT FROM AUTHOR]

This article proposes a method to improve the range of an electric vehicle (EV) by controlling its speed depending on the upcoming traffic signal status. A conventional Adaptive Cruise Control (ACC) controls an EV's speed such that there is a certain distance between the EV and the preceding vehicle. However, if the preceding vehicle drives at a non-optimum speed given the upcoming traffic signal status, as it nears the upcoming traffic signal the preceding vehicle will have to dissipate the unnecessarily gained kinetic energy by braking. If the EV blindly follows such a preceding vehicle using a conventional ACC, the EV will also have to dissipate a part of its kinetic energy by braking. In this work, this problem is addressed by proposing an Efficient Cruise Control (ECC). In the proposed ECC, if the EV is within a certain distance of the upcoming traffic signal, its speed reference is calculated based on the traffic signal status, while maintaining a minimum safe inter-vehicular distance from the preceding vehicle. The ECC is designed using Model Predictive Control theory and it is studied in a simulation environment using a Tesla S vehicle model. The simulation results show that the proposed controller improves the EV's energy efficiency and therefore range, where the latter is an important bottleneck in widespread adoption of EVs. [ABSTRACT FROM AUTHOR]

Highlights • We developed a model to simulate BEV trips based on travel surveys. • BEVs in 2017 could already cover 85–90% of trips in Finland and Switzerland. • Charging infrastructure at households should be expanded faster. • With infrastructure policies in place, BEVs can cover 99% or more of all trips. Abstract Limitations of battery capacity in battery electric vehicles (BEVs) contribute to what is known as range anxiety, and therefore poses an obstacle to their mass-market adoption. While high-range BEVs have been recently introduced, it is not clear whether they will be able to cover all possible trips without long recharging detours, and what the infrastructure needs of those vehicles are. To understand the impact of range limitations in Switzerland and Finland, we constructed a simulation model that is based on representative national travel surveys. We use it to calculate the potential of BEVs to cover any trips and investigate options to increase this coverage. The options discussed in this paper are ways to facilitate easy recharging, such as infrastructure development policies. We complement our results with insights from three focus groups. The results suggest that 85–90% of all national trips could have already been covered with BEVs prevalent in 2016. If the charging station infrastructure is developed appropriately and high-range BEVs are adopted, it is possible to reach a potential coverage of 99% or more in both countries. Deploying charging stations at users' homes and in residential areas does contribute significantly to this improvement and is desirable from a car user's perspective. Providing fast-charging stations in other locations is necessary to maximise the potential. We recommend to focus policy efforts on the development of residential charging options and to increase the visibility of electro-mobility using fast-charging stations. [ABSTRACT FROM AUTHOR]

Highlights • Survey of adult drivers in US cities on intent to purchase an electric vehicle. • Distinct profiles of respondents preferring different types of electric vehicles. • Plug-in hybrid vehicle interest is grounded in economic utility. • Image and perceived environmental impact drive battery electric vehicle interest. Abstract This study analyzes data from a survey of drivers (n = 1080) administered in late 2013 to assess factors that influence potential car buyers to consider two different types of plug-in electric vehicles (PEVs) in the United States: plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs). The results indicate distinct profiles of respondents preferring PHEVs, which have a gasoline backup engine, versus battery BEVs, which rely solely on a battery for power. Respondents interested in selecting a PHEV consider it more for its economic benefits, such as reduced gasoline and maintenance expenditures. Respondents preferring a BEV are drawn to its environmental and technological appeal. The absence of range anxiety for PHEV is a major factor influencing potential PEV buyers. [ABSTRACT FROM AUTHOR]

Energy consumption management is a referential approach to alleviate range anxiety. In this paper, the data-driven modeling and analysis of energy consumption based on low-frequency real-world electric bus data is implemented. Specifically, two quantitative metrics of energy consumption are identified and calculated regarding to the characteristics of low-frequency real-world data. Also, the Road-Driver-Vehicle-Ambient factors system is proposed, forming a comprehensive digital description of the driving process. Supervised learning models with different levels of complexity are then established to predict the energy consumption, with the mean absolute percentage error of 9.2%, 7.5% and 7.7% respectively. Last but not least, the relationship between each factor and energy consumption is explained through quantitative, qualitative and statistical analysis, which intuitively shows the influence of Temperature, Driver/Vehicle-related and Road-related factors on energy consumption. [ABSTRACT FROM AUTHOR]

Highlights • A network user equilibrium model of BEVs considering flow-dependent energy consumption is proposed. • UE conditions are formulated as a nonlinear complementarity model and a variational inequality model, respectively. • An iterative solution procedure is proposed to solve the model efficiently. • A robust optimization model is developed for the congestion pricing problem of BEVs. • UE conditions are further formulated as a generalized nonlinear complementarity model. Abstract This paper addresses the equilibrium traffic assignment problem involving battery electric vehicles (BEVs) with flow-dependent electricity consumption. Due to the limited driving range and the costly/time-consuming recharging process required by current BEVs, as well as the scarce availability of battery charging/swapping stations, BEV drivers usually experience fear that their batteries may run out of power en route. Therefore, when choosing routes, BEV drivers not only try to minimize their travel costs, but also have to consider the feasibility of their routes. Moreover, considering the potential impact of traffic congestion on the electricity consumption of BEVs, the feasibility of routes may be determined endogenously rather than exogenously. A set of user equilibrium (UE) conditions from the literature is first presented to describe the route choice behaviors of BEV drivers considering flow-dependent electricity consumption. The UE conditions are then formulated as a nonlinear complementarity model. The model is further formulated as a variational inequality (VI) model and is solved using an iterative solution procedure. Numerical examples are provided to demonstrate the proposed models and solution algorithms. Discussions of how to evaluate and improve the system performance with non-unique link flow distribution are offered. A robust congestion pricing model is formulated to obtain a pricing scheme that minimizes the system travel cost under the worst-case tolled flow distribution. Finally, a further extension of the mathematical formulation for the UE conditions is provided. [ABSTRACT FROM AUTHOR]

In this article, we investigate the potential for battery life prolongation through optimized charging under consideration of individual mobility requirements. Based on a comprehensive battery aging model we introduce a continuous quadratic programming model to derive battery life optimal charging (OPT). The strategy indicates when and how much to charge to maximize the potential range throughout the battery life. We find that OPT has the potential to more than double the expected battery life compared to simple and often abundant recharging activities as observable today. The degree of battery life prolongation strongly depends on the operating temperature. Since optimal charging would require deterministic knowledge of future trips and corresponding charging levels we investigate a more convenient charging heuristic derived from “As-Late-As-Possible” (ALAP) charging. ALAP charging considers range buffers between 5% and 60% over the range required until the next re-charging opportunity. We analyze the trade-off between (long-term) battery life and (short-term) range flexibility. We find that for decreasing temperatures the trade-off between battery life and flexibility is solved with increasing range buffers. From our results battery degradation aware charging heuristics can be easily derived and applied in real-world settings. [ABSTRACT FROM AUTHOR]

Key to the mass adoption of electric vehicles (EV) is establishing a sufficient recharging infrastructure network, based on customer behavior and psychology. This study examines the battery charging station location problem, considering users’ range anxiety and distance deviations, two major barriers to the mass adoption of EV. The problem is formulated as a bi-level integer programming model based on a range anxiety function. Then, the problem is solved using an adaptive large-neighborhood search, combined with a k-shortest path algorithm and an iterative greedy heuristic. Finally, the effects of parameters are analyzed in the context of a real-world road network. [ABSTRACT FROM AUTHOR]

Range anxiety describes the drivers’ stress regarding the available battery range while driving an electric vehicle. Considering this issue as a barrier against general acceptance of electric vehicles, several researches has been reviewed. The results show that there is no direct communication among current networks of charging stations which causes isolation in these networks. Thus, the users are not able to use cross-network facilities which leads to range anxiety. To overcome, a protocol is suggested to be used in development of RESTful web services to provide direct communication among the networks of charging stations. [ABSTRACT FROM AUTHOR]

While electric vehicle (EV) use has proliferated in developed countries, emerging nations have lagged behind because they lack the resources to establish effective EV charging networks. To address this issue, this paper has developed a framework through scenario analysis that aims to minimize the costs involved in establishing such networks and provide policy makers with insights on how to accomplish this. A given set of public institutions possessing EV fleets were first grouped into zones using clustering algorithms. Mathematical models were then developed to identify optimal EV charging station locations serving the EV fleets of these organizations, with an objective to minimize the total cost. Factors such as EV range anxiety and human walking distance were imbedded into the framework, which was examined using real world data from three major cities in Morocco: Rabat, Casablanca, and Fes. With the framework, policy makers can make better planning decisions on EV transitioning. [ABSTRACT FROM AUTHOR]

It is notoriously known that range anxiety is one of the major barriers that hinder a wide adoption of plug-in electric vehicles, especially battery electric vehicles. Recent studies suggested that if the caused driving range limit makes any impact on travel behaviors, it more likely occurs on the tour or trip chain level than the trip level. To properly assess its impacts on travel choices and traffic congestion, this research is devoted to studying a new network equilibrium problem that implies activity location and travel path choices on the trip chain level subject to stochastic driving ranges. Convex optimization and variational inequality models are respectively constructed for characterizing the equilibrium conditions under both discretely and continuously distributed driving ranges. For deriving the equilibrium flow solutions for these problem cases, we suggested different adaptations of a well-known path-based algorithm—the projected gradient method. While the problem instance with a discrete number of driving ranges can be simply treated as a multi-class version of its deterministic counterpart, the one with continuous driving ranges poses a much more complicated situation. To overcome this arising modeling and algorithmic complication, we introduce a couple of newly defined variables, namely, path-indexed travel subdemand rate and traffic subflow rate , by which the demand and flow rates as well as their corresponding feasible path sets can be dynamically indexed in the solution process with reference to path lengths. An illustrative example with various types and forms of driving range distributions demonstrates the applicability of the proposed modeling and solution methods and various impacts of the heterogeneity of range anxiety on network flows and computational costs. The numerical analysis results from this example show that stochastic driving ranges confine network flows in a different way from deterministic or no driving ranges and the projected gradient procedure relying on dynamically indexed subdemand and subflow rates is generally preferable to its counterpart on pre-indexed ones for both the discrete and continuous driving range cases. [ABSTRACT FROM AUTHOR]

Key to the mass adoption of electric vehicles is the establishment of a sufficient battery service infrastructure network on the basis of customer behavior and psychology. Motivated by EV service infrastructure network design under the battery leasing/electric car sharing service business models, we present an electric vehicle battery service network design problem considering a customers satisfaction related to “range anxiety” and “loss anxiety”. The problem is formulated as a linear integer programming model under deterministic and fuzzy scenarios. A Tabu Search heuristic combined with GRASP is proposed to efficiently solve the problem. Finally, we conduct parametric analysis on real-world road networks. [ABSTRACT FROM AUTHOR]

A frequently discussed phenomenon in the context of limited range as a usage barrier for battery electric vehicles (BEVs) is range anxiety (i.e., range stress). The objective of the present research was (1) to examine if the effect of first-time experience of a critical range situation on inexperienced BEV drivers’ range experience (e.g., range stress) is rather positive or negative, (2) to examine if providing minimal coping information can enhance this effect and (3) if a positive adaptation effect can be found under different criticality levels regarding the available range. A field experiment was conducted, in which 74 participants drove a BEV in a critical range situation (i.e., experience of a small range safety buffer) on an unaccompanied 94 km round trip. Results indicate that the first-time experience of a critical range situation has a moderate positive effect on range stress (i.e., reduced range stress after the trip compared to range stress before the trip), that coping information can partly enhance range experience and that these positive effects can be found under both examined criticality levels (critical vs. highly critical range situation). The results can be useful to inform strategies aimed at reducing the experience of range stress in the early period of BEV usage. [ABSTRACT FROM AUTHOR]

Automotive electrification is a vital component of the carbon-neutrality agenda. To counter the influence of shrinking public incentives for purchasing electric vehicles (EVs), it is crucial to deepen the understanding of the existing users' use cases and clear the barriers for potential EV owners in daily usage. To this end, our study analyzes an 11-month usage data from a large sample of EVs, including 3,777 battery EVs and 5,973 plug-in hybrid EVs. Based on their daily travel intensity, the EVs are clustered into two groups used primarily for private and commercial purposes. We leverage the data to uncover how, when, and where users in each group charge their EVs; investigate EVs' energy consumption efficiency and its relationship to charging; and discuss EV-related policy implications. The outcome of this study may aid in the development of more targeted EV promotion policies and thus contributes to the carbon neutrality. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) has been a main component of car sharing market because of their environment friendly attributes. However, imbalance and range anxiety, as frequent problem in traditional one-way EVs sharing system, make a poor operation sometime. For perform a practical one-way car sharing system, considering the special attributes like charging demand, charging modes and range anxiety resulting from limited battery volume about EVs, we propose a mixed integer programming(MIP) model to satisfy the demand generated by customers who change reserved drop-off station. Moreover, we address the location problem with the allocation of two types of charging piles to meet the different types of demand (i.e. the fast charging mode meet the urgent rent demand). Further, we make a numerical study combining practical data collected in Beijing with practical data to decide operation variables above for one-way car sharing system. We find that preference has an influence on the depots location and fleet size of the one-way EVs sharing system. [ABSTRACT FROM AUTHOR]

It is commonly held that range anxiety, in the form of experienced range stress, constitutes a usage barrier, particularly during the early period of battery electric vehicle (BEV) usage. To better understand factors that play a role in range stress during this critical period of adaptation to limited-range mobility, we examined individual differences in experienced range stress in the context of a critical range situation. In a field experiment, 74 participants drove a BEV on a 94-km round trip, which was tailored to lead to a critical range situation (i.e., small available range safety buffer). Higher route familiarity, trust in the range estimation system, system knowledge, subjective range competence, and internal control beliefs in dealing with technology were clearly related to lower experienced range stress; emotional stability (i.e., low neuroticism) was partly related to lower range stress. These results can inform strategies aimed at reducing range stress during early BEV usage, as well as contribute to a better understanding of factors that drive user experience in low-resource systems, which is a key topic in the field of green ergonomics. [ABSTRACT FROM AUTHOR]

Battery electric vehicle (BEV) drivers frequently report concerns of becoming stranded as a result of battery depletion, widely referred to as range stress, which constitutes a main obstacle to the adoption of these vehicles. One promising solution identified to mitigate range stress is to provide appropriate and accurate information through in-vehicle information systems (IVISs). Therefore, we aim to gain a broader understanding on the influence of specific IVISs on drivers’ range stress perception. To accomplish this, we performed a BEV field experiment in real traffic situations. By placing participants in a critical range situation, we could measure their psychometric range appraisal and psychophysiological feedback. The results of the psychometric evaluation revealed that individuals perceived the critical range situation as less challenging and threatening with the provided IVISs. Physiological indicators were found to be useful in complementing questionnaire-based evaluation methods within the field of range stress. Although the findings of the psychophysiological measurement revealed that IVISs-equipped participants exhibited increases in stress perception over time due to volatile range feedback, the average stress level was observed to be lower throughout the entire driving task. [ABSTRACT FROM AUTHOR]

This study investigates the routing aspects of battery electric vehicle (BEV) drivers and their effects on the overall traffic network performance. BEVs have unique characteristics such as range limitation, long battery recharging time, and recuperation of energy lost during the deceleration phase if equipped with regenerative braking system (RBS). In addition, the energy consumption rate per unit distance traveled is lower at moderate speed than at higher speed. This raises two interesting questions: (i) whether these characteristics of BEVs will lead to different route selection compared to conventional internal combustion engine vehicles (ICEVs), and (ii) whether such route selection implications of BEVs will affect the network performance. With the increasing market penetration of BEVs, these questions are becoming more important. This study formulates a multi-class dynamic user equilibrium (MCDUE) model to determine the equilibrium flows for mixed traffic consisting of BEVs and ICEVs. A simulation-based solution procedure is proposed for the MCDUE model. In the MCDUE model, BEVs select routes to minimize the generalized cost which includes route travel time, energy related costs and range anxiety cost, and ICEVs to minimize route travel time. Results from numerical experiments illustrate that BEV drivers select routes with lower speed to conserve and recuperate battery energy while ICEV drivers select shortest travel time routes. They also illustrate that the differences in route choice behavior of BEV and ICEV drivers can synergistically lead to reduction in total travel time and the network performance towards system optimum under certain conditions. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium. [ABSTRACT FROM AUTHOR]

Predicting the remaining range of a battery reliably, accurately and simply is imperative for effective power management of electrified vehicles and reducing driver anxiety resulting from perceived low driving range. Techniques for predicting the remaining range of an electric vehicle exist; in the best cases they are scaled by factors that account for expected energy losses due to driving style, environmental conditions and the use of on-board energy consuming devices such as air-conditioning. In this work, experimental results that establish the dependence of remaining electrical energy on the vehicle battery immediate cycling history are presented. A method to estimate the remaining energy given short-term cycling history is presented. This method differs from the traditional state of charge methods typically used in battery management systems by considering energy throughput more directly. [ABSTRACT FROM AUTHOR]

In electric vehicle, rechargeable battery served as energy source for all its system operation which include electric motor for propulsion system and also other auxiliary components. Therefore, it becomes an important issue to be tackled in EV technology in order to enhance the battery energy capacity for long range operation. In general public view, people tend to be very concern in purchasing the electric car. One of the concerns lies on the question of how far they can travel with only battery for their car propulsion means. Therefore, this study tries to investigate the relation between battery types and the range anxiety faces by electric car makers. The investigations reveals that, Li-ion as the battery with high energy density cover more area or distance travel. [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. [ABSTRACT FROM AUTHOR]

Battery Electric Vehicle (BEV) is a promising candidate in reducing air pollution and fossil fuel dependencies. It is a growing market for the automobile manufacturers. Although there are many advantages of driving a BEV, it is still not widely accepted in the market due to the limited driving range. Other than just improving the technologies that drive the vehicle, an additional range estimation system can calm the ‘range anxiety’ caused by the limited range of BEVs. Merely predicting the range based on the state of charge of the battery, the average driving speed, and the average power consumption is inadequate. This paper proposes a new range estimator, the dynamic range estimator, which also takes into account the driving behavior, in addition to the slopes of the trip for its energy estimation. The driving behavior is obtained based on the response to speed error and the time delay between throttle pedal and brake pedal switching. In this way, the driving behavior is a fixed response for any driving speeds on the same route thus, allowing the energy consumption to be compared for different speeds. [ABSTRACT FROM AUTHOR]

The environmental issues in the transport sector are numerous and CO2 capture is not even plausible for vehicles at the moment. This report describes a number of different emergent power train technologies (ICE, BEV, PHEV, FCEV) before providing an inter-comparison of these technologies within a technical and economic context. The economical benefits are discussed in terms of the "Difference of Total Cost of Ownership" (DTCO) and take: electric driving distances, energy (fuel, electricity, hydrogen) prices, batteries and fuel cells costs. To simulate electric driving distances, the model uses several functional parameters such as the battery range and the 'range anxiety' based on the assumption of one recharge per day. The potential electric driving distances are evaluated according to the segmentation statistics of daily trips. The results show the yearly mileages, as well as the range and cost of batteries and fuel cells, together with their relative impact on the DTCO and on the competitiveness of electric vehicles. The price of electric vehicles remains high with strong dependency on the battery's capacity, but the benefits in terms of fuel cost savings can be considerable. The price of electricity is currently noticeably lower than petroleum-based fuels, which balances the high costs of the batteries. 50% or more of LDV yearly mileages can be electric-driven, even for limited battery ranges (ca. under 50 km). There are stakes for the battery costs (competitiveness under €215/kWh) and lifetimes, while the low battery ranges (100 km in our case) provide the best margins. As regards FCEVs, the hydrogen target price at the pump should be achievable (less than €6.5/kg) with reasonable gasoline prices (€1.7/liter at the pump) and fuel cell costs (€20/kW). CO2 taxes and ICE efficiency gains will lead to opposite impacts of the H2 target prices at the pump. [ABSTRACT FROM AUTHOR]

Abstract: Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but have a limited utility due to factors including driver range anxiety and access to charging infrastructure. In this paper we apply NREL's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V) to examine the sensitivity of BEV utility to range anxiety and different charging infrastructure scenarios, including variable time schedules, power levels, and locations (home, work, and public installations). We find that the effects of range anxiety can be significant, but are reduced with access to additional charging infrastructure. We also find that (1) increasing home charging power above that provided by a common 15 A, 120 V circuit offers little added utility, (2) workplace charging offers significant utility benefits to select high mileage commuters, and (3) broadly available public charging can bring many lower mileage drivers to near-100% utility while strongly increasing the achieved miles of high mileage drivers. [Copyright &y& Elsevier]

Highlights: [•] Optimize public charger location considering BEV drivers’ travel activity constraints. [•] Assess BEV feasibility for the heterogeneous traveling population. [•] Public chargers could effectively reduce range-constrained days and trips. [•] Infrastructure budge dictates the type and number of chargers to be installed. [ABSTRACT FROM AUTHOR]

Due to higher energy efficiency and lower emissions, electric vehicles (EVs) have become attractive transportation means in developing cleaner mobility systems. Moreover, many future automated vehicles (AV) can be electrified. Hence, existing market will experience a drastic growth in automated electric vehicles (AEVs). For infrastructure enabled automation (IEA), charging facility planning is required to accommodate the increasing AEV charging demand. The planning process must also account for their impact on the power grid. This study presents an integrated demand coverage optimization model over a coupled power-transportation (CPT) network. This model aims to pinpoint candidate locations of AEV charging stations that would serve the most charging demand in the transportation network, considering the upcoming technologies in AEV also will affect the charging behavior that can influence the charging system. Besides, power grid limitations at each charging station are considered for the minimal power cost of the network. The developed model is applied to Utah state road network to determine the optimal charging station locations. • This study aims to advance infrastructure enabled and electrified automation. • AEV charging facility planning is conducted for promoting cleaner mobility. • Users' range anxiety and land use constraints are considered in planning. • The optimization is over a coupled power-transportation (CPT) network. • Power grid limitations at each charging station are considered. [ABSTRACT FROM AUTHOR]

The Electric Vehicles (EV) adaptability is at the early stage in India due to many performances and commercial constraints among the potential customers. Battery Electric Vehicles (BEV) are slowly picking up in the Indian market and likely to grow due to stringent Government regulations and Green Mobility initiatives. The primary constraint in EV purchase is the driving range offered by Original Equipment Manufacturers (OEM) and EV range anxiety. This paper discusses the current battery pack integration among Indian Original Equipment Manufacturers (OEMs), design challenges, and the need for Standardised Battery Pack envelope (SBP). This study analyses the India OEMs vehicle data and, using a statistical and engineering assessment model, recommends the critical standard dimensions, type, size for the Standardised Battery Pack (SBP). This is possible by means of Electric Vehicle (EV) skateboard platform architecture, and platform sharing proliferate among EV manufacturers and start-ups. The model verifies the results by analytical and Simulation techniques. The results show the proposed model with SBP gives higher flexibility, improved driving range ,convenient battery swapping operation, improved operation time, and helps in second-life applications. This study's limitation is that due to the rapid advancement in battery technology, a specific battery chemistry is under consideration throughout the study. • Derivation of geometrical Form factor for Battery Electric Vehicle Battery. • Established Standardized Dimensions for Battery Pack. • Verification of Electric Vehicle Range for the Proposed Design. • Helps in Battery Swapping Process. [ABSTRACT FROM AUTHOR]

• This paper explores the role of fast charging in reducing range anxiety. • 1.7 million charging and driving records of electric vehicles in Beijing were used. • The charging rate is negatively related to range anxiety. • Temperature increases the effects of charging rate on the range anxiety. • The effect of range anxiety decreases with the growth of charging piles. Range anxiety is a psychological condition in which people worry that the limited battery capacity of electric vehicles (EV) will result in running out of power before reaching their destination. In this paper, with the application of quantile regression, 1.7 million charging and driving records for EVs in Beijing are used to explore how fast charging impacts on range anxiety. As state of charge (SOC) is highly related to range anxiety, this paper mainly focuses on the relationship between the charging rate and the SOC, which can be further extended to range anxiety. The results show that the charging rate is negatively related to the SOC, which indicates that fast charging could improve the charging rate and then reduce both the SOC and range anxiety. Temperature positively moderates the effects of the charging rate on range anxiety, which indicates that fast charging would be more effective in reducing range anxiety when it is a cold weather. Additionally, the quantity of charging piles negatively moderates the relationship between the charging rate and range anxiety. However, range anxiety may also influence the choice of charging rate, which might bring a reverse causality problem between the charging rate and the SOC, and result in an exaggerated significance of our analysis. After discussing the endogeneity of our regression results, we believe the possible bias is still totally acceptable. [ABSTRACT FROM AUTHOR]

• We provide empirical evidence on usage of conventional and electric vehicles. • Electric vehicles are driven fewer kilometres than their conventional counterparts. • This holds both for private and shared vehicles. • Non-monetary barriers play a huge role for electric vehicle use and adoption. • Car sharing intensifies usage of conventional cars but not of electric vehicles. We use car-level micro data to provide empirical evidence on the usage of conventional and electric vehicles (EVs) in private and car sharing fleets in Germany. We shed light on both monetary and non-monetary barriers to EV usage by exploiting the feature that variable costs are identical for shared vehicles but different for private car owners across engine types. While drivers respond to monetary incentives when using conventional cars, this does not hold for EVs. We find that EVs are, on average, driven shorter distances than conventional vehicles, both in terms of annual and single-day mileage, even if costs are identical. We also document that car sharing intensifies the usage of conventional cars but not that of EVs. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) owners enjoy many positive aspects when driving their cars, including low running costs and zero tailpipe gas emissions, which makes EVs a clean technology provided that they are sourced through renewable sources, e.g., biomass, solar power, or wind energy. However, their driving behaviour is often negatively affected by the so-called range anxiety phenomenon, i.e., a concern that an EV might not have enough driving range to reach the desired destination due to its limited battery size. The perception of range anxiety may also affect potential buyers in their decisions on whether to purchase an internal combustion engine vehicle as opposed to an EV. This paper investigates some factors that influence range anxiety through a comparative analysis of two target groups: (i) existing EV owners, and (ii) non-EV owners (i.e., potential EV owners). The specially crafted survey was used to collect range anxiety data from more than 200 participants. In particular, participants provided their perceptions on (i) the potential relationship between existing gas station infrastructure and the desired EV charging station infrastructure, and (ii) the potential relationship between range anxiety and two influencing variables, namely the current state of charge and remaining range. Concerning the existing gas station infrastructure, evidence suggests that both target groups think that the distances between gas stations could be increased. Moreover, our analysis shows that the desired distances between charging stations correspond to the distances between the existing gas stations, which indicates that both EV owners and non-EV owners have a common view on the optimal gas station and charging station topology. Furthermore, we find that the type of settlement (urban vs rural) influences preferred distances, where both target groups living in cities desire shorter distances, and that non-EV owners, as opposed to EV owners, are more prone to be affected by the state of charge and remaining range. Quantitatively, we are able to define a measure for range anxiety, which is connected with the preferred distance between two neighbouring charging stations. Throughout our analyses, we find that the mean preferred distance between two neighbouring charging stations is 7 km, but this value significantly differs based on the settlement type of a (potential) EV owner. • Comparative analysis of EV and non-EV owners regarding charging infrastructure. • Analysis of key EV-parameter (SoC and remaining range) influence on range anxiety. • Analysis of charging infrastructure preferences regarding settlement hierarchy; and. • Analysis of influence of EV ownership on range anxiety. [ABSTRACT FROM AUTHOR]

• Study expanded total ownership cost with consumer heterogeneity and range anxiety. • 250–350 km range electric vehicles prevail in cities with plate restriction. • In cities without restriction, internal combustion engine vehicles are cheapest. • 400–450 km range electric vehicles prevails in cold-weather northern China. • Better battery and charging equipment reduce the need for long electric ranges. Plug-in electric vehicles (PEV) appears to have sales momentum in major personal vehicle markets but are still at the early market stage. Opportunities to accelerate PEV adoption can be discovered through comprehensive total cost of ownership (TCO) analysis. Understanding the cost-effective electric ranges of PEVs for consumers, manufacturers, and the society is critical for any discussion of PEV mass markets. This study expanded the traditional TCO approach by (1) fully considering heterogeneous consumer driving patterns, (2) quantifying the charging inconvenience and range anxiety cost of battery-electric vehicles (BEVs), and (3) monetizing both tangible and intangible PEV policies. Uncertainties were handled through Monte Carlo simulation. The results suggest that BEVs with an electric range of 250–350 km have the lowest TCO in cities with government-enacted purchase limitations, and internal combustion engine vehicles (ICEVs) have the lowest TCO in cities without purchase limitations, even when considering PEV subsidies. The lowest TCO for some consumer groups is obtained by BEVs with an electric range of 400–450 km, especially in northern China, where the weather is colder. The cost-effective all-electric range for BEVs in each city in 2025 will decrease due to improved battery performance in cold environments and an expanded charging infrastructure. Based on TCO, plug-in hybrid electric vehicles (PHEVs) are currently more suitable for drivers with a high average daily mileage or a large mileage variance. However, by 2025, BEVs with a long driving range may become a more cost-effective choice for these drivers. [ABSTRACT FROM AUTHOR]