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Studies on solar electric vehicles (EVs) have focused on calculating the power generation in a specific environment without discussing its practical utility. To expand the awareness of the utility of solar EVs, their potential should be evaluated by considering the operation methods of users. This study investigated the photovoltaic (PV) potential of an EV integrated with PV modules while driving on an expressway. Tunnel and shadow areas were identified to determine unpowered areas on the expressway. The PVWatts model was used to evaluate the PV potential by the time of the year. For a single vehicle traveling at 60 km/h on the Donghae expressway section during both the summer and winter solstices, the amount of power generation is within 0.208–0.317 kWh, corresponding to 0.94–1.43% of the electricity consumed for driving. Furthermore, this study assumed that office workers commute on the Donghae expressway. Under the scenario considering the time of operation (traveling to and from work and parking at work) and the shading ratio, the rechargeable amount was more than 10% of the electricity consumption. The results showed that solar roofs are potential charging supplements for EV batteries. This study can provide the efficacy and optimal operation method of solar EVs for commuters. [ABSTRACT FROM AUTHOR]

Despite the advantages and benefits of a battery electric vehicle, its adoption rate remains low. Previous studies have explored factors that influence the adoption of electric vehicles. However, studies investigating whether an electric car outperforms a gasoline car are still limited. Therefore, this study aims to assess the betterness of an electric vehicle compared with a gasoline vehicle in helping customers perform their jobs to be done. A multi-criteria decision-making approach using the analytic hierarchy process is built on two main criteria, namely, customer pains and customer gains, where customer pains are divided into cost pains and non-cost pains, and customer gains are divided into functional gains and emotional gains. Using the most affordable battery electric vehicle in the Indonesian market, interviewees who live in the greater Jakarta and drive to work were invited to perform the pairwise comparison processes. The finding of this study shows that with respect to helping customers perform their jobs to be done, a battery electric vehicle is equally to moderately worse than a gasoline vehicle with a worse score of 0.5946 compared with a better score of 0.4054. This finding comes from interviewees who prioritize customer gains with a priority score of 0.6993 over customer pains with a priority score of 0.3007. Considering that the analytic hierarchy process allows a small number of interviewees, the result obtained should be limited as an early prediction about the betterness of an electric car compared with a gasoline car from a certain group of persons.

Abstract For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world’s highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly.

Battery electric vehicle (BEV) air conditioning systems often use positive temperature coefficient (PTC) heaters to heat the passenger compartment. The heating process consumes a lot of energy in low-temperature environments, which seriously affects the driving range and user experience. This study aims to reduce the low-temperature energy consumption of the air conditioning system and improve energy efficiency through an innovative optimization method. In this study, the energy consumption composition of the air conditioning system was analyzed, and the goal of minimizing the sum of the total power consumption of the PTC heater and the blower was determined, while the efficiency characteristic of the blower was considered at the same time. The relationship between the average temperature of the passenger compartment measurement points and the PTC power and airflow rate was studied by combining experiments and numerical simulations, and the alternative operating conditions that met the temperature requirement were determined. On this basis, the total power consumption of the air conditioning system was analyzed and optimized. The results show that PTC power, airflow rate, and blower efficiency all have an important influence on the total power consumption of the air conditioning system. The optimized scheme could reduce the theoretical total power from 1315.32 W of the original scheme to 1246.83 W, and the actual total power from 1350.05 W of the original scheme to 1326.56 W, with reductions of 5.21% and 1.74%, respectively. The low-temperature energy consumption optimization method for the BEV air conditioning systems proposed in this study is instructive for the selection of blowers and the design of control strategies for air conditioning systems. [ABSTRACT FROM AUTHOR]

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. [ABSTRACT FROM AUTHOR]

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale. • Complexity in charging infrastructure placement for freight transport. • Empirical data from a fleet of freight vehicles in Gothenburg metropolitan area. • Investigation of travel and parking patterns of freight vehicles. • Node-based optimization approach to allocate charging infrastructure. [ABSTRACT FROM AUTHOR]

This paper studies the diffusion of battery electric vehicle (BEV) technology in Japan. The diffusion process in this study refers to both the innovation and adoption of BEV technology. While previous studies on BEV technological diffusion focus on policy selection, especially domestic policy, little is known about the connectedness between international and domestic factors in the BEV technological diffusion process. This paper aims to enrich the discussion of technology diffusion by expanding the argument that technology diffusion is not only a domestic problem but also an international relations problem using a case study of BEV technology diffusion in Japan. The findings indicate that two international factors push Japan to diffuse BEV technology: Japanese commitment to the global environmental regime and global competition in BEV manufacturing. However, the domestic institution factor hinders the BEV technology diffusion. The production structures of automobile industries and political economy relations between the automobile industry and the government further complicate the diffusion process of BEV technology. [ABSTRACT FROM AUTHOR]

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source's performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world's highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly. [ABSTRACT FROM AUTHOR]

Electric vehicles rely on energy storage systems i.e. batteries for reliable performance and extended driving range. However, the battery storage system degrades over time due to cyclic and calendar aging, which can reduce its efficiency and capacity. This can lead to high maintenance costs and a decrease in vehicle performance. The battery deterioration due to aging can be formulated as the state of health. In this study, the main emphasis is on the state of health-based power-sharing strategy among battery storage mediums. The proposed approach provides an improvement in the performance of the energy storage technology which can result in low degradation. The power distribution among multiple battery storage mediums can be optimally distributed to maximize efficiency and system capacity. Further, an effort has also been made to employ the supercapacitor with the battery storage system. The supercapacitor has high power density and take care of transient load at a faster rate. To distribute the power among batteries and supercapacitor, a washout filter-based strategy has been selected that separates the average and transient components of the load current, and ensures the better suitability of hybrid energy storage technologies to the connected load. The obtained results verify the efficacy of the studied state of health-based power-sharing strategy.

The limited number of charging stations for electric vehicles (EVs) necessitates periodic charging, resulting in extended queues at charging stations as drivers await their availability. This study contributes to the existing body of literature by providing estimates of consumer preferences for allowable waiting times at charging stations, as well as furthering the understanding of the roles of the explanatory variables influencing these preferences. The study also compares the average and maximum waiting times experienced by EV drivers, with the acceptable waiting time. Responses from the stated preference survey in Japan in 2021 were analyzed using a generalized ordered logit model. The results show that (a) the sex, age, household income, employment status, and vehicle usage frequency significantly influenced the preferences for allowable waiting times, and (b) the allowable waiting time preferences were significantly associated with the charging locations. Our estimation model indicated a positive association of convenience stores, large commercial facilities, and highway locations with short and medium allowable waiting times. The results provide useful insights into the policy implications of the charging infrastructure. [ABSTRACT FROM AUTHOR]

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs' strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters. [ABSTRACT FROM AUTHOR]

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

This paper presents a comprehensive survey of optimization developments in various aspects of electric vehicles (EVs). The survey covers optimization of the battery, including thermal, electrical, and mechanical aspects. The use of advanced techniques such as generative design or origami-inspired topological design enables by additive manufacturing is discussed, along with sensitivity studies of battery performance with alternate materials and incorporating sustainability considerations. Strategies for battery charging/discharging and battery swapping are reviewed, taking into consideration factors such as operation, cost, battery performance, and range anxiety. Future research is suggested to address uncertainties in charging ecosystem design and incorporate both forward and inverse prediction capabilities, leveraging benefits for both the grid and individual vehicles. The optimization techniques for other EV components, such as motors, powertrains, tires, and chassis, are also discussed. Finally, this paper presents a review of the EV management, specifically the optimization of charging station, grid, and fleet management, including research on charging station construction, charging station operation strategies, and power system operation strategies. The need for further research on robustness, reliability, and sustainability is emphasized to justify the use of EVs in the future. [ABSTRACT FROM AUTHOR]

The limited driving range of BEVs is the main challenge in developing zero-emission Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This limitation necessitates an increase in battery energy while balancing the power supply and consumption requirements for the vehicle's motor and auxiliaries, such as the Heating, Ventilation, and Air Conditioning (HVAC) system. This research proposes a solution to achieve more efficient control of HVAC consumption by integrating fuzzy logic techniques with brute-force algorithms to optimize the Energy Management System (EMS) in BEVs. The model was based on actual parameters, implemented using MATLAB-Simulink and ADVISOR software, and configured using a backward-facing design incorporating the technical specifications of a Malaysian electric car, the PROTON IRIZ. An optimal solution was proposed based on the Satisfaction Ratio (SR) and State of Charge (SoC) metrics to achieve the best system optimization. The results demonstrate that the optimized fuzzy EMS improved power consumption by 23.2% to 26.6% compared to a basic fuzzy EMS. The proposed solution significantly improves the driving range of BEVs. [ABSTRACT FROM AUTHOR]

The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan. [ABSTRACT FROM AUTHOR]

With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes. [ABSTRACT FROM AUTHOR]

To predict the market dynamics of various zero-emission vehicle (ZEV) technologies, this study introduces a dynamic discrete vehicle choice model (VCM) that investigates the probabilities associated with 14 decision factors, applying these to the purchase of ZEVs from 2020 to 2040. Market share and penetration results are presented under eight scenarios, that vary by vehicle costs infrastructure development and incentive strategies. The findings suggest that in the early years, incentives alone may not generate significant market penetration of ZEVs before the infrastructure meets the basic convenience for daily use, especially for fuel cell vehicles (FCVs). However, in later years, incentives play a more important role in the market penetration of ZEVs under well-defined infrastructure networks. By 2040, battery electric vehicles (BEVs) are projected to dominate the market in California. Plug-in hybrid electric vehicles (PHEVs) and FCVs may experience a decline in market share due to improved charging convenience, which benefits the market penetration of BEVs. However, fuel cell plug-in hybrid electric vehicles (FC-PHEVs) could still be beneficial if accessible models are available, considering the limited availability of hydrogen refueling stations. The goal set by the California Air Resources Board (CARB) is achievable, but it requires a sustained combination of measures; no single effort can achieve it. These measures include technological improvements to reduce the cost of ZEVs, a wider range of models available for consumers to choose from based on their desired performance, the establishment of infrastructure (battery chargers and hydrogen dispensers), and attractive incentives aimed at promoting ZEV adoption. The proposed methodology can be adapted for other regions in the United States and globally by carefully examining the inputs for each decision factor at the desired scale.

Battery electric vehicles (BEVs) are surging worldwide due to technology improvements in lithium-based batteries and rising petroleum prices. India’s EV30@30 campaign aggressively penetrate the Electric vehicle and target share by 30% in 2030. Sarcastically, from the Indian context, the availability of Li-source is limited and subsequently, there has been an impetus to find out a promising electric vehicle (EV). Fuel cell-based EVs are one such potential option. The present review critically assesses the scope and perspective of battery electric vehicles (BEVs)with fuel cell electric vehicles (FCEVs) in line with the Indian context. The assessment validates that fuel cell-based EVs can be a likely option for the Indian sub-continent. As a whole, FCEV technology will advance, and over time, smaller and mid-segment commercial vehicles will eventually be more affordable in addition to being sustainable, especially when using green hydrogen as a fuel. The total cost of ownership (TOC) of FCEV is better than BEV for the period of twelve years and also supports the future of FCEV in the Indian context (Sontakke et al. 2024).

A battery management system needs a robust algorithm for online state-of-charge estimation of batteries in different dynamic systems. Due to the ease of implementation, model-based state-of-charge estimation using the extended Kalman filter is popularly used in battery management systems for online state-of-charge estimation. However, the accuracy of the extended Kalman filter depends on the appropriate initialization of noise covariance. In this paper, a robust modified adaptive extended Kalman filter (RMAEKF) is proposed that enhances the state-of-charge estimation accuracy by incorporating recursive adaptive correction rules for process and measurement noise covariance matrices. The adaptive rule considers the predicted terminal voltage error and the state prediction error in each time step to provide the necessary correction of measurement noise covariance and process noise covariance respectively. Further, to validate the state-of-charge estimation accuracy of the proposed RMAEKF, its performance indices for LA92, US06, and mixed drive cycles are obtained at different operating temperatures and compared with the performance indices of the extended Kalman filter and forgetting factor-based adaptive extended Kalman filter. Moreover, the robustness of the proposed RMAEKF is examined with different initial values of state-of-charge, noise covariance matrices, offset current and bias voltage. In addition to that, an experiment using the OPAL-RT real-time simulator is also performed to validate the proposed methodology for online SOC estimation. Concurrently, mean execution time and computational complexity analyses of the proposed RMAEKF are performed to check its applicability in real-time battery management system applications. From the result analysis, it is observed that the proposed RMAEKF shows better robustness and higher state-of-charge estimation accuracy than other compared algorithms under dynamic operating conditions.

Considering the significant contribution of the transport sector to carbon emissions, the importance of battery electric vehicles (BEVs) as environmentally friendly vehicles is self-evident. Due to the rapid expansion of the BEV market in recent years, a comprehensive evaluation of BEV options from the consumer perspective has become an important issue. This paper proposes a data-driven decision aids for purchasing BEVs based on a multiple criteria decision-making methodology (i.e., PROMETHEE-II). A hierarchical evaluation criteria system of BEVs is constructed and correlation analysis between indicators is performed to eliminate duplicate indicators. Then, a comprehensive weighting method by integrating large-scale group decision making method and the Entropy-based method is proposed to identify the weights of criteria. Based on which, the ranking of candidate BEVs can be obtained based on the PROMETHEE-II with a hierarchical evaluative criteria, which can help consumers make BEV purchase choices. Furthermore, the robustness and reliability of the results are tested by applying the sensitivity analysis and contrastive analysis.

The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan.

The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policymakers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.

This study proposes a novel mathematical model for the Multi-Day Tourist Trip Design Problem with Stochastic Travel Time and Partial Charging for Battery Electric Vehicle (MD-TTDP-STT-PCBEV). To the best of our knowledge, no prior study has fully incorporated the use of BEVs into TTDP models. Given the limited driving range of BEVs, the model requires decisions regarding the locations and policy for recharging the vehicle’s battery. The problem also incorporates real-world uncertainty by considering travel time as a random variable subjected to normal distribution. The model is formulated using chance-constraint programming, aiming to find optimal tourist routes for BEVs that maximize tourist satisfaction. Numerical experiments were conducted to compare solutions between stochastic and deterministic environments. Computational experiments using the LINGO optimization solver demonstrated that the total rating scores obtained from the stochastic model with chance-constraint programming were generally lower than those from the deterministic model due to travel time uncertainties. These results highlight the importance of incorporating real-world uncertainty and variability to achieve more accurate and reliable planning.

Global warming remains a prominent topic of discussion in numerous countries, with transportation being a key contributor to significant greenhouse gas emissions. To address this issue, governments and the automotive industry in advanced nations are actively promoting a shift from traditional gasoline cars to a variety of electric vehicles. This study adopts the Technology Acceptance Model and perceived value model as its theoretical framework, focusing on environmental awareness and customer experience to analyze the decisive factors influencing consumers' decisions to purchase battery electric vehicles. By employing a questionnaire survey design, a total of 322 valid responses were collected, and the findings indicate that environmental awareness and customer experience significantly impact perceived quality. Moreover, the study reveals that purchase intention is positively influenced by an enhanced user attitude towards battery electric vehicles. Consumers view these vehicles as valuable, influencing their willingness to purchase and shaping their post-use perceptions, whether positive or negative. To encourage greater adoption, automakers can focus on promoting environmental awareness and organizing more customer-centric experiential activities. [ABSTRACT FROM AUTHOR]

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

The recent trend of transitioning from cars with internal combustion engines to green cars has accelerated amidst growing concerns of a climate crisis and the COVID-19 pandemic. South Korea is a particularly good test bed for exploring green mobility alternatives and setting priorities for green car policies, owing to its fast-growing green car market and its considerable share in the worldwide purchases of fuel cell electric vehicles. Accordingly, this study explores the Korean conceptualization of green cars and the prioritization of different aspects of future green car policy using qualitative Q methodology and additional interviews. A total of 31 transport policy experts participated in our survey, resulting in three distinct perspectives: a hybrid vehicle supporter with comparative pragmatism, an electric vehicle supporter from multiple aspects, and a radical ecologist searching for new alternatives. Based on these expert insights, we determine the most appropriate green car type and provide important recommendations for future green car policy. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs. [ABSTRACT FROM AUTHOR]

This data set contains the questionnaires and respective responses from two joint surveys dealing with the actual and anticipated charging behaviors of battery electric vehicle (BEV) drivers and professional truck drivers. During a period of 2 months in early 2023, a total of 348 responses were collected using an online survey built on the LimeSurvey framework. Participants were recruited using a database of professional truck drivers available to the authors and an open recruitment process based on QR codes attached to charging stations in the Munich area. In the fast-paced electromobility market, this data set constitutes one of the few sources of insights into the charging behaviors of current and future users of electrified vehicles. It may be used to calibrate behavioral models or derive design decisions for future charging infrastructures.

Studies on solar electric vehicles (EVs) have focused on calculating the power generation in a specific environment without discussing its practical utility. To expand the awareness of the utility of solar EVs, their potential should be evaluated by considering the operation methods of users. This study investigated the photovoltaic (PV) potential of an EV integrated with PV modules while driving on an expressway. Tunnel and shadow areas were identified to determine unpowered areas on the expressway. The PVWatts model was used to evaluate the PV potential by the time of the year. For a single vehicle traveling at 60 km/h on the Donghae expressway section during both the summer and winter solstices, the amount of power generation is within 0.208–0.317 kWh, corresponding to 0.94–1.43% of the electricity consumed for driving. Furthermore, this study assumed that office workers commute on the Donghae expressway. Under the scenario considering the time of operation (traveling to and from work and parking at work) and the shading ratio, the rechargeable amount was more than 10% of the electricity consumption. The results showed that solar roofs are potential charging supplements for EV batteries. This study can provide the efficacy and optimal operation method of solar EVs for commuters.

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short.

This paper investigates the potential of bidirectional charging using modular multilevel inverter-based reconfigurable battery systems via grid-parallel control. The system offers several advantages such as modularity, scalability, and fault-tolerance over conventional battery electric vehicle systems. It is designed for seamless integration with the grid, allowing bidirectional power flow and efficient energy storage. Within this study, the battery system is first simulated in Matlab/Simulink and later implemented into a hardware setup. Eventually, the simulation results and the measurements have been compared and evaluated. Thereby, startup sequences and constant current scenarios were investigated. It has been shown that the system is fully capable to charge and discharge the batteries in the grid-parallel connection, thus enabling bidirectional charging with close to full drive system power. The current total harmonic distortion complies with grid regulations and can potentially improve the grid quality. The proposed system offers significant potential for grid-integrated energy storage systems, addressing the challenges associated with renewable energy integration, grid stability, and energy management. In comparison to other publications on this topic, the proposed approach does not need additional dedicated power electronic hardware and has more degrees of freedom for current control.

The growing number of battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV) brings the need of more fast charging stations across cities and highway stops. This charging stations toned to be connected to the electrical grid via existent facilities, causing constraints such as power availability. This study brings an approach for the planning and operation of such energy hubs by coping with this challenge by deploying a Battery-based Energy Storage System (BESS). With the BESS integration, it is expected to minimize utilization and overall energy costs, preventing infrastructure upgrades, and enhancing the integration of renewable energy resources. This approach sizes a stationary energy storage system with lithium-ion technology batteries through a co-optimization of the planning and operation stages, integrated in an electrical installation that will implement fast charging stations. This sizing is a result of an optimization based on the interior point algorithm, where the objective is to minimize the costs of maintenance, operation, and installation of a BESS, while properly modelling the different resources such as the BESS, the charging station and EV charging and PV generation.

Exergy is a thermodynamic concept that ponders the quality of energy. It evaluates the irreversibilities of a machine, demonstrating its capacity to perform work associated with energy conversion. This article focuses on directing public policies and vehicle development toward their most proper usage worldwide. In the urban mobility scenario, there is an obvious demand to decrease greenhouse gas (GHG) emissions. In addition, the internal combustion engine (ICE) experiences considerable energy losses through heat exchange through the radiator and exhaust flow gases, which are not considerable in battery electric vehicles (BEVs) since there are no exhaust gases subsequent to combustion, nor combustion itself. This work presents longitudinal dynamics simulations of passenger vehicles to understand the magnitude of exergy destruction in ICEVs and BEVs, considering the Brazilian and European Union electric energy mix. Overall, the method can be applied to any other country. The simulation and model parameters were configured to match production road vehicles commercialized in the Brazilian market based on different versions of the same model. Two vehicle dynamic duty cycles were used, one relating to urban usage and another to highway usage, resulting in an overall exergy efficiency of around 50–51% for BEVs considering the exergy destruction in power plants. In contrast, ICE has an average efficiency of 20% in the urban cycle and around 30% in the highway cycle. By comparing the overall equivalent CO2 emissions, it is possible to conclude that EVs in the European energy matrix produce more GHG than ICE vehicles running on ethanol in Brazil. Nevertheless, there are increasing uses of coal, natural gas, and oil thermal electric power plants, raising the question of how the transition may occur with a general increase in electrification since there is an increasing electric expenditure in all sectors of society, and the renewable energy plants may not meet all of the demand. [ABSTRACT FROM AUTHOR]

The transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging the light-duty sector due to technical and operational challenges and the choices made by medium- and heavy-duty fleet operators. Research investigating the procurement considerations of fleets has relied heavily on interviews and surveys, but many of these studies suffer low rates of participation and are difficult to generalize. To model fleet operators' decision-making priorities, we apply a robust text analysis approach based on latent Dirichlet allocation to a broad corpus of fleet adoption studies. We find operational compatibility to be the most salient factor, followed by long-term economics, technological familiarity, and perceived reliability. [ABSTRACT FROM AUTHOR]