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Over the past decade, Chinese government has invested large amounts of government subsidies to support the development of new energy vehicle (NEV) industry. Whether the government subsidy has achieved the expected effect and how to further adjust the subsidy policy to better promote the development of the NEV industry have received wide attention. This paper considers the characteristics of technological innovation and financing constraints of enterprises at different position of the NEV industry chain, and empirically test the incentive effect of government subsidies on R&D investment from the perspective of the industry chain. The research findings show that government subsidies promote the R&D investment of all enterprises in the NEV industry chain, however, the promotion effect on upstream and midstream enterprises is more significant. With the reduction of consumption subsidies, the promotion effect of government subsidies on the R&D investment of listed companies in the NEV industry has been strengthened. Further research finds that government subsidies promote R&D investment by alleviating the financing constraints of upstream and midstream enterprises. There is an industry chain synergy effect in the technological innovation of new energy vehicle enterprises, and government subsidies can promote this synergistic effect from upstream to downstream. [ABSTRACT FROM AUTHOR]

The coevolution of technological capabilities and institutional arrangements is critical for the success of innovations. Yet, it remains unclear how actors' shifting relations during institutional change affect innovation. The paper examines the dynamics in how actors shape the institution by integrating the institutional logics perspective with policy mix literature. Insights are drawn from the Online Electric Vehicle and Fuel Cell Electric Vehicle cases in Korea, where involved stakeholders revealed contrasting identities and practices throughout the innovation stages. While both cases witnessed shifts in institutional logics, the repercussions on innovation differed depending on the characteristics of actors' identity mix. Findings contribute to institutional theory by expanding the traditional dyadic perspective on incumbent and emerging institutional logics. We also add to the policy mix literature by introducing a neo-institutional perspective. The paper concludes by underlining how the overarching characteristics of the institutional mix trigger different coordination failures. [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 tourism is increasingly becoming a realistic prospect for drivers. There has, however, been little research focused on EV tourism. This paper discusses the results of a comprehensive narrative literature review. It suggests that the current paucity of literature is, at least in part, a consequence of two questionable assumptions; first, that EVs are inadequate for tourism travel, and second that they are unlikely to result in substantial changes to drive tourism. This paper challenges these assumptions and explores instrumental, infrastructural, symbolic, and experiential aspects of EV tourism. It suggests that a shift to EVs could prompt instrumental changes to the kinds of trips tourists take, their destinations, rhythms, and planning. It discusses infrastructure upgrades, including consideration of complex financial concerns. It explores possible changes in the symbolic associations between cars and leisure travel and other elements of social life. Finally, it discusses embodied and experiential aspects of drive tourism, including their consequences for safety. A shift to EV tourism is very likely, and very unlikely to leave drive tourism unchanged. This paper therefore argues that it is time to move beyond perceptions that EVs are boring and inadequate, and to take seriously the possibilities for EV tourism research. [ABSTRACT FROM AUTHOR]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions. [ABSTRACT FROM AUTHOR]

The modern quest for sustainable cities increasingly relies on using distributed energy resources (DERs), which requires new planning practices. This paper proposes an optimisation strategy to solve the fast charging station (FCS) allocation of electric vehicles (EVs). A mixed-integer programming (MIP) model minimises investment and operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The embedded set covering problem has special aspects that allow the development of a novel approach to evaluate candidate sites to accommodate FCSs. A preprocessing strategy is developed to fine-tune the entire solution space. A multiobjective approach is used to obtain an optimal compromise solution for the MIP model when it is required to serve the maximum number of EV owners at the lowest possible cost. The combined strategies reduce the computational burden, allowing full-scale studies of EV charging system planning. The results of studies using a real-world Brazilian case certify the benefits of the proposed strategy in the FCS allocation problem and in optimising the operation when considering renewable alternatives. [ABSTRACT FROM AUTHOR]

The increasing popularity of electric vehicles (EVs) due to their limited onboard battery power and eco-friendliness has created a pressing need for widespread availability of charging stations. High-speed charging stations, in particular, can strain the power grid by potentially overloading it during peak hours, causing sudden power shortages and voltage drops. This research paper examines the ANSYS twin-Builder modeling approach to create a comprehensive model of an integrated system consisting of photovoltaic (PV) power generation, battery energy storage (BES), and a multiport device-based charging station. The integration of these components enables advanced stabilization techniques such as power gap equalization, peak shaving, valley filling, and voltage sag correction. The primary objective is to reduce the burden on the power grid by meeting the daily charging demand using sufficient daylight PV output. The simulation results have been validated to showcase the benefits of employing the PV-BES configuration in multiport EV charging circuits across various operational modes. To further improve performance, a transfer system is introduced, which incorporates gate turn-off thyristors and insulated-gate bipolar transistors into the EV charging station. The paper also analyzes power losses and efficiency for different modes and configurations, comparing them with simulations of conventional Si device-based charging networks. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming very popular nowadays to reduce the consumption of oil and environmental pollution. The use of battery and super-capacitor is increasing as the power-source of EVs. The bi-directional DC-DC converter is essential to integrate the battery and super-capacitor with the motor of the vehicle. However, a conventional second-order bi-directional DC-DC converter cannot give very high/low output voltage due to its efficiency reduction at extreme duty cycles, which is prerequisite for EVs. The problems of achieving high efficiency in wide-input-wide-output (WIWO) voltage range bidirectional operation using a conventional bidirectional converter are summarized in this paper. Additionally, the available converters from the literature for WIWO voltage range operation are discussed in detail. The various wide voltage range DC-DC converters are classified into different categories based on their operating principle. Additionally, the advantage and disadvantages of these various wide voltage range converter topologies are explained for the EV application. Finally, a general conceptual solution to achieve a WIWO converter with high efficiency is proposed. It is essential to change the voltage level of the motor input as per the speed requirement. Hence, this paper presents a clear idea for the WIWO bi-directional converters for EV application. [ABSTRACT FROM AUTHOR]

As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery's raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles. [ABSTRACT FROM AUTHOR]

This paper focuses on the joint optimization of the charging station location problem (CSLP) and discrete network design problem (DNDP) in a transportation network. We present a variational inequality (VI) formulation to describe the user equilibrium (UE) state of gasoline vehicles (GVs) and electric vehicles (EVs). Based on the mixed-UE model, a mathematical program with equilibrium constraints (MPEC) model is formulated for integrating the decisions of deploying EV charging stations (EVCSs) and adding new links to minimize the total travel cost (TTC) of all vehicles. A modified genetic algorithm is developed to tackle the MPEC model with an adaptive path generation procedure to address the mixed-UE model. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. Specifically, we propose a two-step optimization model and explore a performance comparison between the joint and two-step optimization approaches, while the joint optimization exhibits superiority in minimizing the TTC. [ABSTRACT FROM AUTHOR]

The in-wheel motor (IWM) is a promising advancement in electric vehicle propulsion since it offers greater packaging efficiency and torque vectoring capabilities than a chassis-mounted motor. However, the increased unsprung mass degrades the vehicle dynamics by increasing body vertical acceleration (BVA) and tyre dynamic load (TDL), worsening ride comfort and road-holding respectively. Another important consideration is magnetic gap deformation (MGD), which results from the deflection between the motor stator and rotor. Reducing MGD improves the motor reliability and longevity. This paper addresses these issues by incorporating inerters into an existing IWM model. They are incorporated into the strut and bushes to control BVA, TDL and MGD. The system optimised for MGD resulted in a deflection reduction of over 98% without compromising performance of the other indices. Optimising the strut and bushes for BVA and TDL reduced them by 20% and 9% respectively without degrading MGD. A sensitivity analysis of the results is presented. Here the damping and inertance values are varied by 20% from the optimum, and the resulting performance still improves upon the default. It is concluded that there are significant potential improvements where inerters are included in an in-wheel motor system. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Recently, the transition from conventional to renewable energy sources (RESs), from internal combustion engine vehicles to electric vehicles (EVs), and from the main grid to microgrids (MGs) are essential goals to both reduce greenhouse gas emissions and ensure the stability of power systems. However, the transitions cause new concerns in the grid including technical challenges and financial viability. This study examines the RES-based MG under realistic conditions considering the uncertainty in fleet size of EVs, emergency generator capacity, solar irradiation, and wind speed in island mode. The work aims to provide effective solutions including conventional methods alongside today's trend namely doubly fed electrical generators (DFIG), vehicle-to-grid mechanism (V2G), maximum power point tracking controller (MPPT), voltage source inverter (VSI) with high switching frequency for technical challenges, and virtual synchronous generator (VSG) mechanism for financial viability. The paper provides a guide for resizing the emergency generators capacity depending on system instability. The observations verify that the control mechanisms reinforce the system to remain stable by decreasing the range of frequency fluctuation from 3.1 to <0.05 Hz, the peak point of frequency from 51.8 to 50.05 Hz, and the emergency generator capacity from 0.7605 to 0.3420 MVAr at MATLAB and Simulink. [ABSTRACT FROM AUTHOR]

In two-stage electric vehicle (EV) battery chargers, ac-dc conversion stage contributes significantly in determining the power quality. At this stage, multilevel rectifiers (MLRs) featuring switches with lower voltage rating and better input voltage waveform quality can be adopted. However, balancing capacitor voltage in MLR is an important challenge. In this paper, a single-phase five-level power factor correction (PFC) rectifier with self-balancing capacitor is implemented for EV battery charging. The complimentary switching scheme adopted for rectifier reduces number of gate driver circuits and ensures capacitor voltage balancing. The rectifier shows added benefit of reduced voltage stress across switches and not requiring an external filter at the dc side as the load is parallelly connected with one of the capacitors. The operating principle, control strategy, modulation technique and design features of the implemented five-level rectifier is discussed in detail. The implemented control scheme enhances rectifier performance by establishing precise output voltage regulation during load fluctuations while maintaining unity power factor at input side. The five-level rectifier is validated experimentally and compared with other existing multilevel and non-multilevel rectifier topologies. The peak efficiency of 93.8% is achieved at 2 kW of output power. [ABSTRACT FROM AUTHOR]

This paper presents a 7-day Travel Demand Model (TDM) for UK rural areas to aid the Electric Vehicle (EV) transition in these regions. Utilising data from both the UK Census Survey and UK National Travel Survey (NTS), private passenger vehicle travel patterns for a rural village in the Peak District National Park (UK), were modelled. This model is adaptable to any rural community within the UK, requiring only publicly available information on households and vehicles for that community. Using a novel approach through the development of lifestyle scenarios to understand the required household activities, the TDM incorporates five different trip purposes as the building blocks for a vehicle's activity. Over a period of one week, 13,520 miles were driven by 84 vehicles across 49 households, that shows an EV fleet serving this community would consume 3562 kWh energy per week. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns and transform the transportation sector. This paper examines the state-of-the-art EVs, encompassing their history, definition, and different types. The advantages and limitations of each type are compared and contrasted, providing insights into their unique characteristics and potential applications. Critical components of EVs are thoroughly analyzed. The paper explores various types of batteries, emphasizing their features and potential for improving energy density, range, and charging capabilities. Charging infrastructure is discussed, highlighting the importance of establishing a robust network of charging stations. The impact of EVs on power grids is examined, emphasizing grid integration, load management, capacity upgrades, and power quality. Challenges and barriers hindering the wide adoption of EVs are identified, including technological limitations, infrastructure constraints, economic considerations, consumer perception, and policies. The paper delves into the environmental and economic impacts of EVs, including lifecycle analysis, air pollution reduction, and cost-effectiveness. Thus, the paper provides valuable insights into the current state of EVs and their potential to shape the future. The findings highlight the need for collaborative efforts from industry stakeholders, governments, researchers, and consumers to overcome challenges and accelerate the transition to a sustainable transportation system. [ABSTRACT FROM AUTHOR]

Similar to product design of function and quality, product emotional appeal becomes a more and more important factor influencing consumer purchases. This paper proposes a two-step method for product emotional elements reasoning based on image metaphor. It aims to solve the depth deficits and boundary constraints of traditional methods. Firstly, based on image metaphor and guided interview, the emotional needs of consumers for products are initially extracted. Secondly, tacit emotional needs are transformed into explicit design elements by using a three-level image metaphor decoding method of symbolic-index-perceptual. The effectiveness of the proposed method is implemented and verified by taking the emotional design of electric vehicle as an example. The results could be a reference for electric vehicle development. The findings suggest that image metaphor could be one of the ideal ways to reduce the deviation between designers' goals and users' emotional needs, and the proposed method can provide more innovative support for designers and engineers. Highlights The concept of image metaphor is introduced into the field of product emotion design, and a cross-domain mapping method of emotional needs based on image metaphor is proposed. This method can describe consumers' emotional needs easily and provide the possibility to mine extra-domain elements. A three-level emotional needs decoding method based on image metaphor is proposed, which can convert users' implicit emotional needs into explicit product design elements. The feasibility of the proposed method is verified by a case study of emotional design of electric vehicle. The findings suggest that image metaphor can solve some depth deficits and boundary constraints of traditional methods in emotional design, so as to provide more innovative support for product designers and engineers. [ABSTRACT FROM AUTHOR]

Interactive users' preferences and waiting time together have great impact on charging station network design of electric vehicles (EVs), but only waiting time was considered in previous studies. To fill this research gap, this paper addresses a location planning problem for EV charging stations, which considers users' preferences and waiting time simultaneously. The problem is formulated as a multi-objective bi-level programming model, the upper level model determines locations and capacity options of charging stations with the objectives of minimising total cost and minimising total service tardiness, and the lower level model determines the allocation of users to stations with the objective of minimising total travel time. A hybrid non-dominated sorting genetic algorithm II (HNSGA-II) with embedded level determination algorithm (LDA) and a partial enumeration algorithm (PEA) are proposed, respectively, to solve the model. Furthermore, managerial analysis is implemented to verify the advantages of considering users' preferences in reducing charging service tardiness and saving cost compared with the mode of no considering users' preferences. And sensitivity analysis is also performed to provide managerial insights for EV charging station location practice. Finally, a real-world case study is conducted to verify the applicability of the proposed approach in solving practical location planning problems. [ABSTRACT FROM AUTHOR]

Growing awareness of electric vehicles insists on the necessity of Permanent Magnet Brushless Direct Current Motor (PMBLDCM) worldwide. This paper involves building a simulation model of the complete PMBLDCM drive system using Matlab/Simulink to control the speed and Torque. Using the Simulink model, speed parameters with various controllers of the drive system are studied and analyzed. The conventional PI controller is capable of controlling the speed of the PMBLDC motor. H1owever, it cannot give assurance of the stability of the motor throughout the load variation. In a speed loop, Improved Brain Emotional Learning Based Intelligent Controller (IBELBIC) is proposed in this paper. The proposed IBELBIC-based PMBLDC motor drive system with a rating of 3 phase, 30 V, 400 W, 3000 RPM is implemented using Spartan 3 Field Programmable Gate Array (FPGA) from Xilinx. This paper presents an IBELBIC design for enhancing the speed performance of the drive system at various set speeds and different load conditions of 1.2 Nm load, 0.6 Nm load, and no load. The proposed PMBLDCM drive system is realized with the help of a Very high speed integrated circuit Hardware Description Language (VHDL) programming algorithm of digital Pulse Width Modulation (PWM) generator topology. Various test cases are evaluated under different operating conditions to demonstrate the learning capability and the applicability of the proposed controller. In this paper, simulation validation is done using hardware setup, and experimental results are monitored on a computer using a customized program developed using LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench). Instead of a Data Acquisition (DAQ) card, the Virtual Instrument Software Architecture (VISA) tool of LabVIEW software is used in this work and results in cost minimization of the experimental setup. [ABSTRACT FROM AUTHOR]

The direction and eternal theme of the automotive industry's sustainable development is protection of the environment, energy efficiency, and safety. Especially the traffic accidents, the world's energy shortages and the increasingly deteriorating environment are particularly important today. Especially in current era, because of the dual pressures of energy and the environment, various forms of electric vehicles (EVs) are becoming the focus of global automotive industry. This paper presents a significant method of road recognition method. The key control is to have the option to precisely recognize the ideal slip rate and current street surface attachment coefficient to control the wheel slip. By utilizing the Burckhardt strategy, the slant change of the bend could sort out the ideal slip rate and pinnacle attachment coefficient of street surface distinguishing proof examination is generally utilized in steadiness control of EV. On road tests, the road conditions are very complicated. It is necessary to take identify methods with strong robustness. Therefore, it is an important method to use fuzzy technique with good robustness to recognize optimal slip rate (OSR). The fuzzy road recognition algorithm could identify the friction co- efficient and the OSR superiorly. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have gained increasing attention as a more sustainable and environmentally friendly mode of transportation. Many countries should include electrification of their transport networks in their future smart city plans to ensure environmentally sustainable growth. The number of EVs in metropolitan areas is expected to experience extensive growth. EVs are often considered an alternative to addressing the challenges posed by increasing carbon emissions and dependence on fossil fuels. However, the acceptance of EVs tends to be slow due to concerns such as range anxiety, prolonged charge times, inconvenient charging locations, and inadequate charging infrastructure. To address these challenges, this paper discusses the planning and optimization of EV charging infrastructure based on existing literature. In recent years, there has been a significant increase in the number of publications focusing on EV charging stations. This demonstrates the growing interest and research activity in the field of EV charging infrastructure. Furthermore, the literature is categorized into recurring topics, specifically EV charging planning and optimization for EV charging infrastructure. This particular review paper includes empirical work on charging network planning for EVs. Therefore, this analysis provides the latest trends and findings for EV charging infrastructure planning. [ABSTRACT FROM AUTHOR]

Electric vehicles and hybrid vehicles are the gateway to the future of transportation. The policymakers all around the world are pushing for a transition to electric and hybrid vehicles as soon as possible because of its environment-friendly nature and also reducing dependency on the fossil fuels. But with the introduction of electric vehicles comes the challenge of developing the charging stations (CS). If the transition to smart electric vehicles has to happen, the need to develop smart charging stations becomes even more important. The location, nature and technology used in the charging station will affect the performance and ultimately decide the success of the electric and hybrid vehicles. This paper presents different constraints, optimization techniques and optimal location of the charging stations. This paper provides the comprehensive survey of outcomes obtained in the optimal placement of electric vehicle charging stations. [ABSTRACT FROM AUTHOR]

The low-frequency torsional vibration caused by a sudden input torque change seriously affects electric vehicles' comfort and dynamic performance (EVs). This paper proposes a novel torque controller with robust global finite-time stability based on terminal sliding mode control to achieve the dual objectives of anti-jerk control and traction torque control. For this purpose, a general second-order system of EVs drivetrain with a disturbance term is established, and a new finite-time terminal sliding surface is designed. The proposed finite time torque controller (FT-TC) includes a finite-time sliding mode disturbance observer for real-time disturbance estimation and a finite-time sliding mode motor controller for calculating motor torque control law. In addition, the elimination of inherent chattering of the controller is considered in this paper. Finally, the proposed FT-TC is verified and compared with parallel double-loop PID controller and sliding mode controller on the Matlab/Simulink platform and an electric SUV platform under the condition of suddenly pressing/releasing the accelerator pedal during starting. The results indicate that the proposed FT-TC is effective and has a better synthetic evaluation performance in achieving the dual objectives. [ABSTRACT FROM AUTHOR]

It is an open question of whether patent thickets coincide or differ across jurisdictions. Taking Battery Electric Vehicle (BEV) technology as an example, this paper applies the social network analysis method to study the diversity and coincidence of patent thickets. The evolution of patent thicket density in the USA has a similar pattern with Europe and Japan, but it differs from China, Germany and South Korea. This paper uses the degree centrality, which is the number of one node directly connecting to the other nodes in a given network, to explore the patent thicket structure. We find critical multinational companies with higher degree centrality coincides across patent thickets, while most patentees with lower degree centrality do not co-occur in other patent thickets. The diversity of patent thickets implies companies should implement differential patent strategies in various markets. In contrast, the coincidence means patent holders who co-occurred in different patent thickets could be potential patent licensors. [ABSTRACT FROM AUTHOR]

In this paper, we address the electric vehicle routing problem with time windows and propose two branch-and-price-and-cut methods based on a column generation algorithm. One is an exact algorithm whereas the other is a heuristic method. The pricing sub-problem of the column generation method is solved using a label correcting algorithm. The algorithms are strengthened with the state-of-the-art acceleration techniques and a set of valid inequalities. The acceleration techniques include: (i) an intermediate column pool to prevent solving the pricing sub-problem at each iteration, (ii) a label correcting method employing the ng-route algorithm adopted to our problem, (iii) a bidirectional search mechanism in which both forward and backward labels are created, (iv) a procedure for dynamically eliminating arcs that connect customers to remote stations from the network during the path generation, (v) a bounding procedure providing early elimination of sub-optimal routes, and (vi) an integer programming model that generates upper bounds. Numerical experiments are conducted using a benchmark data set to compare the performances of the algorithms. The results favour the heuristic algorithm in terms of both the computational time and the number of instances solved. Moreover, the heuristic algorithm is shown to be specifically effective for larger instances. Both algorithms introduce a number of new solutions to the literature. [ABSTRACT FROM AUTHOR]

The destiny of the electric vehicle (EV) marketplace will depend upon the behaviour of potential buyers and on how emerging markets allow EVs to be perceived as a mobility solution to the externalities generated by internal combustion (IC) vehicles. To this end it is important to ascertain the role played by psychological factors, along with instrumental attributes, especially among younger adults (future purchasers) in not yet mature markets. Our paper analyses and models the propensity to purchase an EV with respect to an equivalent IC vehicle. It contributes to the existing literature investigating younger adults' behaviour in an emerging market, focusing on the role of attitudes and perceptions. A stated preferences survey, built on real commercial scenarios (Renault ZOE vs Renault Clio), was designed and disseminated at the University of Cordoba (Argentina). Respondent behaviour was modelled within the random utility paradigm. First, heterogeneity among users was investigated through mixed multinomial logit formulation; The role of psycho-attitudinal factors was then explored through the specification of hybrid choice models with latent variables. Estimation results indicate the significant role of attitudes and perceptions in emerging markets. [ABSTRACT FROM AUTHOR]

The charging coil plays an important role in the wireless charging system system. However, there are still some problems in the design of the wireless charging coil, such as the low coupling coefficient. In order to improve the coupling effect, this paper proposes an optimized design scheme for the metal shielding layer structure of DD coil. Firstly, the influence of the structure size of DD coil metal plate on the coupling coefficient is studied by the mathematical modeling analysis. Futhermore, the maxwell simulation is used to study the metal plate coupling coefficients with varying structure sizes, addressing receiving uniformity issues. The optimal size of the shielding layer is designed for optimal magnetic coupling in wireless charging. Finally, this paper validates the feasibility of the improved metal shielding layer through experiments. The research in this paper may have a reference role in the wireless charging system of electric vehicles. [ABSTRACT FROM AUTHOR]

The term 'Just Transition' (JT) gained global prominence through the Silesia Declaration on Solidarity and Just Transition agreed at the 2018 UN Climate Change Conference (COP24). However, there has been a gap in both theory and practice regarding how to develop the global solidarity called for in this declaration. Some have even viewed JT as another form of neo-colonialism, benefitting the Global North through exploiting the Global South. This paper discusses how to avoid perpetuating colonial dynamics through JT by developing worker-led civil society solidarity across the Global North and South. It focusses on Mexico where vast increases in lithium extraction will be necessary to enable the enormous increase in the use of Electric Vehicles proposed for the Global North. While the main beneficiaries of this technology transition will be the companies, governments and consumers of the Global North, the costs will be borne by low-income communities in Mexico, among other Global South localities. Strategies are suggested for a de-colonised JT led by the relevant unions, including car workers. [ABSTRACT FROM AUTHOR]

This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm [ABSTRACT FROM AUTHOR]

Advancements in electric vehicle (EV) technology and integration of renewable energy systems into EV has emerged as a transformative standard for sustainable and efficient transportation have led to the widespread adoption of Permanent Magnet Synchronous Motors (PMSMs) for vehicle drive system. The CAGR Analysis 2023–2030 predicts an 11% annual growth rate for the global electric vehicle industry. This review paper comprehensively explores the multidimensional aspects of inverter-based integration, highlighting their significance in enabling seamless hybrid renewable energy utilization and uninterruptedly charges the EV batteries which delivers an opportunity to optimize the drive process of PMSM effectively. A relentless torque is extremely essential in the electric vehicle applications to attain efficient motor speed. This review paper sights the various aspects of inverter fed PMSM drive controller along with a hybrid Photovoltaic (PV)-dynamo input source system and discusses the development of a productive EV battery charging structure. The findings suggest that the combination of PMSM motors along with a hybrid PV-dynamo source can significantly enhance the charging efficiency by 97% and improve overall PMSM performance. A comprehensive overview of renewable energy-powered EVs and their role in reshaping the future of sustainable mobility. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the design aspects and performance analysis of high-frequency inverters used in inductive power transfer application (IPT) for electric vehicles. The paper's main scope is focused on the design of inverters used explicitly for wireless charging with misalignment variations and coil dimensions. The research articles in the literature focus on the high-power transfer and coil structures for the existing topologies. The magnetic resonance coupling in the IPT system depends on the resonance phenomenon. The operating frequency of the inverter must be the same as the resonant frequency in the compensation network. On complete assessments, the paper emphases on suitable inverter specifications for wireless charging and its related issues. In addition, the resonant inverters can be replaced with H-bridge inverters in consideration of voltage stress and harmonic control. Investigations on harmonic elimination strategies for H-bridge and resonant inverters are also discussed in the paper. [ABSTRACT FROM AUTHOR]

Bi-directional, DC/DC converters find a wide range of applications such as, electric vehicles, plug-in-hybrid electric vehicles, electric vehicle charging stations, residential micro-grid systems, uninterruptable power supplies, charging/discharging of battery storage, the realisation of a hybrid combination of battery with renewable energy sources, etc. Bi-directional DC/DC converter topologies are divided mainly in two categories namely, Isolated and Non-Isolated. Isolated and Non-Isolated topologies are further sub-categorised into nine and sixteen groups, respectively. Respective configurations, table summary, comparative plots of the component count, efficiency and voltage gain are also presented in this paper. Bi-directional DC/DC converters are being developed to improve voltage gain, efficiency, minimise size and cost, increase reliability and introduce modularity. In this work, a detailed topology-wise review is carried out along with respective applications, highlighting comparative features, modern technology trends and future scopes which throw light on specific applications. Based on the review, this paper intends to provide a logical and categorised approach to highlight various technical aspects in this domain. [ABSTRACT FROM AUTHOR]

Given the goal of reducing emissions and saving energy, an increasing number of transit agencies have proposed electrification plans for public transport buses. The two fundamental challenges to adopting electric vehicles in transit operations are the purchase of appropriate electric vehicles to establish the bus fleet and the creation of an efficient schedule and recharging plan. This paper examines the multi-depot and multi-vehicle type electric vehicle scheduling problem with partial mixed-route strategy and partial recharging policy. The partial mixed-route strategy proposed in this paper allows multiple transit routes to operate in a more cost-efficient way. Moreover, it takes into account the bus allocation problem of the transit network fleet to meet the parking restrictions of each depot. The problem is formulated in a mixed-integer programming model, and an adaptive large neighborhood search (ALNS) algorithm with new mechanisms specific to the problem is proposed to apply the model in a more efficient manner. The dataset of a real transit network in Nanjing is used for case study, and the performance of ALNS is tested by using randomly generated instances from the dataset. The results show that the proposed method is effective in finding high quality solutions and adopting partial recharging policy can reduce the fleet size and the total cost while providing advantages depending on the operational parameters of the schedule. In addition, comparison of two schedules using different partial mixed-route strategies shows that there may be two sides of adopting mixed-route scheduling. [ABSTRACT FROM AUTHOR]

As a good energy storage element, lithium-ion batteries are used widely in electric vehicles and electric energy storage systems now, but its own State of Health (SOH) is difficult to measure directly. The Support Vector Regression (SVR) method can use the cell characteristics extracted from the experiment to predict the SOH of the cell.However,the method itself is difficult to determine the hyperparameters and has a large impact on the prediction accuracy.For this reason, this paper proposes the joint algorithm of Bat Algorithms (BA) and Support Vector Regression to predict the SOH of lithium-ion batteries, uses the BA algorithm to find the optimal SVR hyperparameters and uses Grey Relationship Analysis (GRA) to verify their high correlation with the battery SOH correlation.The proposed method was validated using a publicly available experimental dataset, and the results showed that the method has higher prediction accuracy compared with the conventional GA-SVR and PSO-SVR. [ABSTRACT FROM AUTHOR]

Low efficiency of internal combustion engine (ICE)-based vehicles and their adverse effect on environment have compelled a lot of research on its green counterpart i.e. electric vehicles. But the limited range of electric vehicles coupled with longer recharging times has given rise to the advent of hybrid electric vehicles (HEVs). HEVs use two drive trains i.e. a conventional ICE-based drive train and an electric drive train, thus giving it a better efficiency and reliable operating modes. Based on the topological configuration of drive train, HEVs can be categorized into series HEV or parallel HEV. This paper presents a comparative performance analysis of a small HEV in pure ICE configuration, series HEV (SHEV) configuration and parallel HEV (PHEV) configuration. Advanced Vehicle Simulator (ADVISOR) software is used with Matlab/Simulink platform for simulation purpose. The acceleration performance and gradability test have also been carried out and the result is obtained by investigating vehicle emissions. [ABSTRACT FROM AUTHOR]

The problems faced in the electric vehicle (EV) charging station like time consumption and limited space can be overcome by battery swapping station (BSS), which not only saves time but also reduces the range anxiety problem among the users of EV. Batteries are the backbone of EV as it is power supply and essential to run the vehicle. Total price of EV has a good share in the form of battery, thus impacting a lot in the EV industry. Therefore, this paper presents a methodology for the selection of a sustainable battery supplier for BSS using fuzzy multi-criteria decision-making method. Firstly, four main criteria segregated into 12 supplementary criteria are identified considering several requirements for BSS. To cope with the uncertainty in the decision-making process, triangular fuzzy number (TFN) has been implemented. The TFNs give the relative weights among the considered criterion, and to make this framework more customer-centric, quality function deployment (QFD) is implemented, leading to final weights of the criteria. The obtained results give the hierarchy of the criteria, and top five weights after implementing the QFD methodology are as follows: battery price (0.2313), innovation ability (0.2188), safety assurance (0.12135), financial condition (0.1119), and cost of transportation (0.066). Subsequently, integrating fuzzy with complex-proportional assessment of alternatives (COPRAS) leads to rank the alternatives. As a case study, Kolkata city of India is chosen to demonstrate a viable selection. It was found that supplier based on Kolkata Municipal Corporation is the topmost priority, followed by North 24 Parganas, eastern part of Howrah, and South 24 Parganas, with the relative significance value of 0.3305, 0.2575, 0.2540, and 0.1578, respectively. This paper reveals an approach to the stakeholders for accessing the sustainable battery supplier of BSS choosing the most significant one. [ABSTRACT FROM AUTHOR]

Dynamic charging has the potential to overcome the barriers to electric vehicle (EV) adoption by converting road segments into powered lanes (electric roadways or ERs). This paper focuses on examining public acceptance by determining the factors that affect the short-term and long-term intention to purchase an EV and drive on ERs; identifying the clusters of the technology acceptance; and assessing the value of travel time savings (VTTS) for driving on ERs, in general, and for different clusters. A survey was conducted in Los Angeles, California and distributed online, gathering 600 responses. The results showed that respondents' innovativeness, lifestyle and environmental consciousness significantly affect the intention to drive on ERs in the short- and long-run. Public acceptance, in general, seems to be related to charging patterns, safety of commute route, and safety concerns for ERs, among other factors, and depends on the implementation time of the technology. Higher VTTS were found for electrified lanes exclusive for EVs on interstate and arterial roads. People who were classified as positive toward ERs had higher VTTS for ERs followed by those who were neutral and those who were concerned about ERs. This paper can help policymakers and transport operators devise strategies to accelerate EV adoption by appropriately implementing the ER technology. [ABSTRACT FROM AUTHOR]

The power system network is complex and diverse in nature because of the random and irregular existence of renewable sources, various types of varying and constant loads. Under these circumstances, the stabilization of DC link voltage and obtaining reduced power fluctuations are the main control aspect of DC microgrid (MG) system. This paper explores a Kalman filter (KF) based voltage–current control algorithm for the battery unit to control the medium voltage direct current (MVDC) bus voltage accurately with trivial oscillations as compared to the traditional VI algorithm. The stability and monitoring of DC microgrids are greatly influenced by gathering sufficient and precise information. Since installing several sensors on DC MGs is costly and increases DC MG ripple. In this work, an extended Kalman filter (KF) is used to estimate the current of the lead-acid battery. Hence the current represents an important factor in guaranteeing safe operations. Where the Kalman Filter, which improves the estimation efficiency, to reach a good performance in the current prediction. By updating the proposed approach provides smooth and noise-less operation from the measured DC voltage. The control strategy established for electric vehicle (EV) in this paper is to improve the overall charging ability of the EV only if the system's total power is greater than the load level by means of the current loop management technique. The feasibility of the designed MG system with the established battery management mechanism and the Escape Ford EV performance is studied using MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

This paper proposes an interleaved multilevel converter (MLC)-based charger with power rating 2.8 kW to charge on-board battery of electric vehicle (EV). It comprises two stages: (1) an interleaved multilevel ac–dc converter; and (2) an LLC-resonant dc–dc converter. The interleaved approach efficiently maintains the optimum power level. The switching scheme implemented for MLC maintains the source current sinusoidal, supports unity power factor (UPF) and has the capability to balance the capacitor voltage. The MLC maintains UPF and balances capacitor voltage without any auxiliary circuit. In addition, MLC provides lower voltage stress and reduced total harmonic distortion (THD), therefore, making it a better choice for ac–dc conversion stage in battery charging application. The dc–dc converter furnishes regulated and wide output voltage range for on-board charging system. The principle of operation, modulation technique and control strategy for both the converters are analysed. Following theoretical analysis, laboratory experiments were conducted to validate the implemented approach. The results signify that for output voltage range of 24−72 V, UPF is maintained at input, lower stress across switches and capacitor voltage is balanced. The performance of MLC is satisfactory as it attains a peak efficiency of 97% with THD of 2.07%. [ABSTRACT FROM AUTHOR]

Graphene aerogel are frequently employed as electrode materials for power batteries due to their high specific surface area and excellent properties. This paper presents a method for preparing graphene aerogel by radiolytic reduction in a water and isopropanol system. In this study, the authors used radiolytic reduction technology to reduce graphene oxide to graphene aerogel and investigated the effects of various factors in the water and isopropanol system on its preparation process and properties. The experimental results showed that the water and isopropanol system could effectively prepare graphene aerogel with a high specific surface area, and the isopropanol concentration and irradiation time in the isopropanol system had important effects on its properties. This preparation method has the advantages of simplicity, rapidity, and low cost, providing a new idea for the large-scale preparation of graphene aerogel. [ABSTRACT FROM AUTHOR]

China has been able to deploy electric vehicles at an unprecedented speed and scale. This paper explores the underlying policies during six years of sector formation, and it identifies a change in policy-regime after three years. The analysis of evolution of the policy-regimes indicates that creating, transferring, and sharing knowledge among the principal actors was key to catching up. The changing policy-regime enabled a minimum threshold of technology development required for deployment. Nevertheless, it was insufficient for pushing the sector to the global technology frontier. Key to the sector's relative success was the government's responsiveness to sectoral development and its ability to address production, demand, and knowledge issues simultaneously. In addition, it created synergies between policies designed to address environmental concerns and foster economic development concurrently. However, fast deployment required heavy subsidies and important policy initiatives came with potential pitfalls that may hamper international competitiveness of the sector. [ABSTRACT FROM AUTHOR]

The increasing uptake of electric vehicles (EVs) has raised concerns about the impact a large fleet could have on electricity markets and distribution grids alike. Charging strategies have emerged as a means to provide flexibility, especially to electricity distribution grids, by controlling the EV charging process. This paper presents a typification of charging strategies and introduces a conceptual framework for appraising their flexibility in distribution grids. This is underpinned by data collected through an aggregative systematic literature review. The framework is derived from an exploratory qualitative content analysis of the sampled data and encompasses four flexibility dimensions: time, duration, quantity, and location of charging. Structural elements of a charging strategy are also explored, complementing the framework. Finally, the paper also presents a quantitative data analysis assessing the level of flexibility provided by each charging strategy. Results show that flexibility dimensions are not equally exploited, direct control strategies do not strictly outperform other control alternatives and innovative charging structures are yet to thrive for flexibility supply to increase. These findings contribute to better-informed, evidence-based policy interventions. [ABSTRACT FROM AUTHOR]

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models. [ABSTRACT FROM AUTHOR]

The effects of Electromagnetic Interference (EMI) in Inductive Power Transfer (IPT) for electric vehicle charging circuits are elaborated with Common Mode (CM) emission. Electromagnetic Compatibility (EMC) has to be sustained in the circuits such that it is prone to the effects of interferences, noises and disturbances. These issues can be addressed at the initial product development stage, with accurate identification of the parameters that influence the CM emission in the circuit. The analysis of EMI focuses on three features, namely Source of EMI, Coupling path and Receptor. In the IPT model, the primary source of the EMI is the power converter used for high-frequency supply to the inductive coils. The power converter switches and the heat sink with its Printed Circuit Board (PCB) design are predominant in power electronic circuits. This paper facilitates the reduction of EMI without including complex filters. The system is tested for the violated standard SAEJ2954. In addition, the article corroborates precognitive modeling by implementing compensation networks used in IPT applications and their effectiveness in reducing the leakage current caused by the converters and the coils. The paper insights the air gap variation of the coil with the power transfer efficiency and its influence on electromagnetic interferences. The Fast Fourier Transform (FFT) analysis used in PSIM shows leakage current and harmonic reduction with the compensation network and Pulse Width Modulation (PWM) technique. The fabrication model for the front end power supply to the IPT is tested. The electrical noise emission is estimated using predictive modeling. [ABSTRACT FROM AUTHOR]

State of Charge (SoC) of Lithium-ion battery is a key parameter in battery management systems for electric vehicles. This paper uses the fundamental theory of the smooth variable structure filter (SVSF) and proposes a SoC estimation algorithm for a Manganese Cobalt (NMC) cell with a nominal capacity of 20 Ah. Several tests are conducted considering different types of noise and parameters variation. A nonrandom Gaussian noise is first added to the battery voltage. The maximum root mean square error (RMSE) of the estimated SoC is about 2.8 % for a standard deviation of the noise set to 2.6 e − 3 P.U. The same noise is applied to the battery current and the maximum RMSE of the SoC is obtained as 1.36%. Moreover, an EMI noise is added to the battery voltage and the obtained RMSE of the SoC is about 1.73% for a peak amplitude of the noise set to 0.07 P.U. The convergence of the algorithm is also confirmed under battery parameters variation due to the temperature change. However, its accuracy degrades considerably. Finally, a comparative study is carried out with the extended Kalman filter and shows the superiority of SVSF in terms of accuracy and robustness against measurement noise. [ABSTRACT FROM AUTHOR]

Electric Vehicle (EV) standardization is of paramount importance in the rapidly evolving automotive landscape, playing a pivotal role in ensuring interoperability, safety, and global harmonization. This review delves deep into the multifaceted challenges and pillars of standardizing EVs, the FAME I, FAME II schemes initiated by the Indian government. With the advent of EVs, the convergence of technologies from these traditionally distinct sectors has brought forth unique challenges, from communication protocols to global harmonization and safety concerns. The paper sheds light on the prevailing international standards set by bodies such as IEC, IEEE, UL, BIS, CHAdeMO, Standardization Administration of China (SAC), SAE and ISO. Moreover, the status of Indian standards and worldwide standards are discussed. The voltage levels and maximum charging capacity of the different chargers are discussed. Furthermore, it elucidates the advantages of EV and PV charging stations and impact of charging stations on grid. [ABSTRACT FROM AUTHOR]

With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models. [ABSTRACT FROM AUTHOR]

An electric vehicle battery system often consists of multiple battery cells that are interconnected in series and parallel. If a micro-short circuit failure occurs in one of the battery cells, it may cause a serious internal short circuit or even thermal runaway. Consequently, it is extremely important to detect micro-short circuit faults in batteries. This paper presents a novel approach for diagnosing faults in lithium-ion batteries based on the similarity ranking fluctuation rate of voltage curve, and verify the feasibility of the method through a series of micro-short circuit experiments containing an external parallel variable resistance device. This approach does not require complete charge-discharge curves. Firstly, the charging voltage data is preprocessed using the Variational Mode Decomposition algorithm, after which the Dynamic Time Warping technique is employed to calculate the similarity between the charging voltage of each battery and a reference battery. Then a ranking fluctuation rate of voltage curve similarity is established, and the Grubbs criterion is utilized to identify batteries exhibiting abnormal fluctuation rate within the ranking. Finally, the effectiveness of the proposed method is validated by analyzing actual battery signals collected from faulty vehicles. The results demonstrate that the method accurately identifies batteries with micro-short circuit faults. [ABSTRACT FROM AUTHOR]

AbstractAs the need for changes in transportation grows, the transition to sustainable mobility is being envisioned in varying ways. Shifting from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs), along with changing mobility habits, will both be necessary. What has been neglected from the study is the role of sport in aiding this transition. Motorsport has long served dual roles of serving as a technological testbed and center for culture formation around street cars. Coupling one of the largest sporting platforms in the world with the missions of climate change awareness and technological advancement, two racing leagues are catalyzing the move to electric mobility. Formula E and Extreme E have released documentary films showcasing their future goals. To understand the role of electric racing in shaping the transition to sustainable mobility, this paper uses a grounded theory approach to identify relevant themes of the EV transition, while a narrative analysis is used to foreground the overall storytelling aspect of the ecocinematic films. The racing EV emerges as a site of collective collaboration: meeting the needs of the future within the framing of traditional motorsport, and merging traditionally masculinist narratives of automotive technology with prototypically feminized environmentalism. [ABSTRACT FROM AUTHOR]