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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.

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.

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.

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.

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.

Battery Electric Vehicles (BEVs) have an increasingly large share of the vehicle market. To ensure a safe and long operation of the mostly large underfloor-mounted traction batteries, they must be developed and tested in advance under realistic conditions. Current standards often do not provide sufficiently realistic requirements for environmental and lifetime testing, as these are mostly based on data measured on cars with an Internal Combustion Engine (ICE). Prior to this work, vibration measurements were performed on two battery-powered electric vehicles and a battery-powered commercial mini truck over various road surfaces and other influences. The measurement data are statistically evaluated so that a statement can be made about the influence of various parameters on the vibrations measured at the battery pack housing and the scatter of the influencing parameters. By creating a load profile based on the existing measurement data, current standards can be questioned and new insights gained in the development of a vibration profile for the realistic testing of battery packs for BEVs.

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.

The effective and dependable usage of rechargeable batteries has emerged as a central topic for automobile manufacturers in the wake of the rise of electric vehicle technology. When it comes to rechargeable batteries with high specific energy and specific power, lithium-ion battery technology is the most well-known. The low terminal voltage battery cells in the lithium-ion battery pack are linked in series to provide the necessary voltage for the electric vehicle system. The low charge cell in the string limits the usable capacity of the battery pack, though. Disturbances in the battery pack's charge are due to variations in manufacturing quality and to the unique operating circumstances of each individual cell. These inconsistencies cause a decline in usable capacity, a quickening of cell deterioration, and, most significantly, substantial safety issues including overcharging. The cell balancing controller is a critical component of the battery management system in all electric vehicle and hence performs a crucial function in extending battery life and ensuring the battery's safety. This paper presents a hardware-in-the-loop simulation of a RCD buffer included fly-back converter-based active cell equaliser for lithium-ion batteries in electric vehicles. With the equaliser, all the series-connected cells may be brought to a more even State of Charge. All of the MOSFETs employed in the proposed approach are selected for their low conduction loss. The suggested equaliser is able to produce equalization without the need for a switch driving circuit or sophisticated control method, allowing it to function automatically. The system's overall price is reduced, and the balanced circuit's complexity is considerably reduced. In-depth discussions on circuit configuration, operating principle, modeling, and design consideration are presented. Finally, both Matlab simulation results and Hardware-in-loop based experimental findings are offered to back up the claims that the suggested cell equaliser is both practical and effective.

Electric vehicle systems are a promising future transportation system because they play an important role in reducing atmospheric carbon emission and have become a focal point of research and development in the present era. The emerging fast charging technology has the ability to have refueling experiences comparable to gasoline cars. This article discusses existing electric vehicle charging infrastructure with a particular emphasis on rapid charging technologies, which would be needed to meet current and potential EV refueling requirements. Various dc-dc converter topologies for battery electric and plug-in hybrid vehicles are compared and contrasted in this article in terms of performance, output power, current ripples, voltage ripples, conduction loss, recovery loss, switching frequency loss, reliability, durability, and cost. The architecture, benefits, and drawbacks of AC-DC and DC-DC converter topologies for rapid charging stations are also discussed in this article. Furthermore, this study addresses the crucial problems and difficulties associated with electric vehicle converters for direct current rapid charging. Eventually, technical and relevant contributions are provided for an electric vehicle system development.

Increasing greenhouse gases & air pollution are a global threat. Global forums are aggressively emphasizing on reducing the dependence on non-renewable resources. Battery Electric vehicle are among the initial initiative to reduce dependency on fossil fuels, and this demands more research to understand the energy requirements of a vehicle under different driving conditions. The performance of an Electric Vehicle depends on varying drive conditions and the Power Electronic Controller is primarily responsible for its sustainable operation. In this paper, a novel mathematical model is proposed to analyze the performance of an electric vehicle under different driving conditions. The model is simulated at different driving speeds keeping other longitudinal, lateral, and vertical parameters fixed. Rolling resistance forces, aerodynamic drag force, gradient force, total driving force, driving torque, and power requirements at different speeds have been calculated under standard driving conditions. The rolling resistance increases by 2.16 times with a change in the vehicle speed from 40 kmph to 120 kmph. The aerodynamic drag force increases ten times with a 10-degree gradient. The battery operating temperature is critical in vehicular performance, a hybrid Pneumatic-Liquid Thermal Management System is proposed to maintain battery operating temperature. Performance of the proposed model is simulated and found to be in line with the existing standards. This study concludes that road conditions, tyre pressure, velocity of travel, wind velocity, and temperature significantly influence the performance of an electric vehicle.

Electric vehicles are expected to dominate the vehicle fleet in the near future due to their zero emissions of pollutants, reduced fossil fuel reserves, comfort, and lightness. However, Battery Electric Vehicles (BEVs) suffer from gradual performance degradation caused by irreversible chemical and physical changes inside their batteries. Moreover, predicting the health and remaining useful life of BEVs is difficult due to various internal and external factors. In this paper, we propose an integrated data-driven framework for accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries used in BEVs. As part of this work, we conducted a comprehensive analysis and comparison of publicly available battery datasets, providing an up-to-date list of data sources for the community. Our framework relies on a novel feature extraction strategy that accurately characterizes the battery, leading to improved RUL predictions. Feature types can be divided into three groups: initial state features, current state features, and historic state features. Experimental results indicate that the proposed method performs very well on the NASA benchmark dataset with an average accuracy of 97% on batteries that are not used during the training phase, demonstrating the ability of our framework to operate with batteries being discharged in any conditions.

As governments and the automotive industry push toward electrification, it becomes increasingly critical to address the broad set of factors influence individual car buying decisions. Evidence suggests that operational inconvenience or the perception thereof plays a large role in consumer decisions concerning Battery Electric Vehicles (BEVs). BEV ownership inconvenience and its causal factors have been relatively understudied, rendering efforts to mitigate the issues insufficiently informed. This paper presents an empirical equation, derived using a novel data-based method, which relates operational inconvenience to a small number of housing and local Electric Vehicle Supply Equimpent (EVSE) infrastructure factors. The equation and method provided can be used to conduct quantitative analyses on the inconvenience impacts of current and proposed EVSE infrastructure. Ultimately such a quantitative approach is needed to understand and mitigate large inequities of BEV experience and adoption which might emerge from electrification.

Even though diesel-powered Heavy Goods Vehicles (HGVs) are major contributors of greenhouse gas emission, HGV electrification is still in its infancy owing to various challenges. Costlier and heavier batteries and lack of charging infrastructure along long routes are the major bottlenecks in the realisation of HGV electrification for long-haul freight transportation. In this regard, adopting technologies that can make HGV electrification viable is vital to aid the transportation sector decarbonisation. Benefiting from approaches such as the use of overhead catenary power, and conforming to operate in a platoon formation, it would be possible to considerably reduce the cost of HGV electrification and make it economically feasible. Utilising overhead catenary infrastructure would result in requiring economical charging infrastructure, shorter charging cycles, and smaller batteries, while not compromising on the overall payload of HGVs. HGVs in platoon formation result in energy consumption reduction and increased traffic throughput. This paper investigates the benefits of using overhead catenary-powered electric HGVs for freight transportation and also explores the advantages associated with electric HGV platoon formations on the same. The feasibility analysis has been done with the support of close to real physics-based electric HGV models on realistic operating scenarios adopting an in-service highway drive cycle. From the analysis, electric HGV platooning using overhead catenary as the power source was found to be economical with $\approx 10.4\%$ reduction in electricity cost when compared to without platooning while devising electrification strategies for long-haul freight transportation.

This paper describes the characteristics of worldwide scientific contributions to the field of electric vehicles (EVs) from 1955 to 2021. For this purpose, a search within the Scopus database was conducted using “Electric Vehicle” as the keyword. As a result, 50,195 documents were obtained through analytical and bibliometric techniques and classified into six communities according to the subject studied and the collaborative relationships between the authors. The most relevant publications within each group, i.e., those related to the most publications, were analyzed. The result shows 104,344 authors researching on EVs in 149 different countries with 225,445 relations among them. Furthermore, the most frequent language in which these publications were written as well as the h-index values of their authors were analyzed. This paper also highlights the wide variety of areas involved in EV development. Finally, the paper raises numerous issues to consider in order to broaden knowledge about EVs, their efficiency, and their applications in the near future for the development of sustainable cities and societies.

As the first and second largest greenhouse gases (GHG) emissions industries, the sustainable development of construction and transportation industry determines the future of human society. The current carbon emission calculation standard does not cater to electrical transportation, as well as the characteristics of prefabricated components comparing with raw materials. This study focused on the transportation process of prefabricated components, exploring the characteristics of battery electric vehicles (BEV) in carbon emissions. Through the real-world experimental research, the true carbon emissions performance of components’ transportation was obtained under different external parameters. After preliminary screening of variables through correlation analysis, several variables were determined as compensation coefficient for multiple linear regression. The quantitative relationship between the three parameters of Load Ratio, Average Speed and Atmospheric Temperature on carbon emissions factor was revealed, and the R2 value of the fitting formula reached 0.7079(AC off). Results shows that BEV can reduce GHG emissions by 36.18%–54.69%. As well as promote the low-carbon innovation of the fabricated construction industry.

Electric range is an important indicator of battery electric vehicles. China has been testing the range of battery electric vehicles in accordance with national standard GB/T 18386-2017 in the past until the new version of GB/T 18386 standard released in 2021. This paper compares the differences between the new and old versions of GB/T 18386, and analyzes the influence of electric range under NEDC and CATC test procedure. The results show that adopting to CATC, the range increase slightly, with an average increase of 2.20%. The simplification of the test procedure results in a significant reduction in the net test duration, with the reduction of 67.78% for the tested vehicle, while the test results are basically unaffected, only with an average increase of 0.60%.

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.

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.

The sharp inclination in the emissions from conventional vehicles contribute to a significant increase in environmental issues, besides the energy crises and low conversion efficiency leads to the evolution of electric vehicles (EV). Hybrid electric vehicles (HEV) have efficient fuel economy and reduce the overall running cost, but the ultimate goal is to shift completely to the pure electric vehicle. Despite this, the main obstruction of HEV is energy storage capability. An EV requires high specific power (W/kg) and high specific energy (W·h/kg) to increase the distance travelled and reduce the time required for charging. The main focus of this paper is on the energy sources as these are the main components in EVs towards making them eco-friendly and cost-effective. Various topologies of EV technology such as HEVs, plug-in HEVs, and many more have been discussed. These topologies of EVs are based on the diverse combination of batteries, fuel cells, super-capacitor, flywheels, regenerative braking systems, which are used as energy sources and energy storage devices.

In 2020, the Government of Japan declared “2050 carbon neutral” and launched a long-term strategy to create a “virtuous cycle of economy and environment”.11 See https://www.meti.go.jp/english/policy/energy_environment/global_warming/ggs2050/index.html. Japanese firms possess many technologies that contribute to decarbonization, which is important to expand investment for Green Technology (environmental technology) development. As automobiles are major contributors to greenhouse gas emissions [1], the technological shift towards vehicle powertrain systems is an attempt to lower problems like emissions of carbon dioxide, nitrogen oxides [2]. On the other hand, patent data are the most reliable business performance for applied research and development activities when investigating the knowledge domains or the technology evolution (Wand, 1997). Our paper describes a Japanese patents dataset of the vehicle powertrain systems for hybrid electric vehicle (HEV), battery electric vehicle (BEV) and fuel cell electric vehicles (FCEV). In this paper we create a method of bombinating international patent classification (IPC) and keywords to define “green” patents in vehicle powertrains field, using patent data which were applied to Japan Patent Office recorded on EPO's PATSTAT database during 2010∼2019 year. When analyze patents, it is necessary to consider the social situation of each country including language background, we collect patents description documents (abstracts and titles) not only written in English but also in Japanese. Finally, we build a database includes 6025 green patents’ description documents and 266 patents’ holding firms. With which we then identify 3756 HEV patents, 1716 BEV patents, and 553 FCEV patents. Data about patent holding firms is also appended. The full dataset may be useful to researchers who would like to do further search like natural language processing and machine learning on patent description documents, statistical data analysis for empirical economics.

We supplied 25 two-car households with a short-range battery electric vehicle (BEV) to study their adaptation to a BEV replacing one of their conventional cars. The data includes GPS-measured driving of the households’ two original cars for 2–3 months, and for the BEV and the remaining conventional car for 3–4 months. We performed interviews with the households before and after the BEV trial period. We can thus compare the change in measured driving patterns and the users’ experienced adaptation in relation to their measured driving adaptation. We find large heterogeneity in measured adaptation, with some users driving the BEV more than the replaced car and some less. Most users state a preference for using the BEV, but this is not always detectable in the GPS data. Similarly, expected issues with the range limitation from the GPS data do not predict satisfaction with the BEV from the qualitative data.

This paper investigates the charging needs and charger utilisation of a system of battery electric trucks and their public fast chargers, along the highway between the Swedish cities of Helsingborg and Stockholm, in a possible future scenario in which all trucks are battery electric. The system is investigated via an agent-based model which simulates a typical day with current levels of truck traffic on the road. The traffic flow is based on hourly truck flow data during a day and the annual flow of trucks. The findings indicate major potential for a well-functioning system of public chargers for trucks, with high utilisation, few queuing problems at charging stations, cheap public fast charging and a robust response to queues caused by peaks or increases in the traffic flow. The number of 900 kW chargers along the road needed to achieve this has been estimated at 140, and the utilisation factor for the chargers is predicted to be 30%.

The increasing market share of electric vehicles and the politically intended phase-out of the internal combustion engine require reliable and realistic predictions for future consumption and greenhouse gas emissions as a function of technological solutions. This also includes the consumption- and emission-intensive transport of goods. We consider both passenger vehicles and commercial vehicle traffic in our study and have investigated whether there are drive alternatives to the battery electric vehicle that enable uninterrupted trips with a long range, especially for regional delivery services and internationally active freight forwarders. To this end, we have analysed three system architectures and their expected technological progress until 2050: battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and internal combustion engine vehicles (ICEV) running on compressed natural gas (CNG). The latter case serves as a best-practice reference from a combustion technology perspective. The analysis is based on a validated and proven physical model and predicts that the BEV2050 will consume 3.5 times less energy and emit 15 times fewer greenhouse gases than the ICEV-CNG2020, whereas the FCEV2050 will consume 2.5 times less energy and emit 6.5 times fewer greenhouse gases than the ICEV-CNG2020 on the road (hilly terrain, transition season, and WLTP triple-mixed drive cycle). The advantages of the BEV result from the shorter drive train with lower total losses. Our results thus confirm the expected role of the BEV as the dominant drive technology in the future, and light vehicles with low-to-medium-range requirements will especially benefit from it. On the other hand, since the greenhouse gas emissions of the FCEV2050 are lower by a factor of 6.5 than those of the ICEV-CNG2020, it is reasonable to conclude that the FCEV can play a significant role in transport until 2050 when long distances have to be covered. Our model-based approach also allows us to determine the energy fractions of the acting physical forces and thus calculate the consumption shares: electric drive recuperation increases BEV and FCEV range by about 15% in 2020 and will increase it by about 20% in 2050, depending on drive technology and vehicle type. Air and rolling resistance contribute 20% each to the total consumption. The consumption of the accessories of modern vehicles with a share of about 10% of the total consumption cannot be neglected.

In this paper, battery system architectures are methodologically derived in order to find the key type differences. In a first step, the system levels are identified and distinguished. In order to be able to completely cover the solution space of battery system architectures, a distinction is also made between mono- and multifunctional materials. Based on the system levels, a framework for possible architectures is derived. Four system architecture generations with a total of eight different types are identified and analyzed in the dimensions “Nomenclature”, “Approach”, “Omitted Components” and “Industry Examples”. In this way, upcoming system architectures, such as cell-to-pack and cell-to-chassis, can be clearly differentiated. Finally, fundamental product characteristics for the four system generations are derived and compared.

This paper investigates the economic consequences of a haulage company replacing its line-haul diesel trucks with battery-electric ones. It also examines how large truck batteries should be, whether the haulage companies should use public fast chargers to complement their own, and whether public fast chargers have the potential to be profitable. The potential extra cost of losing payload capacity is estimated and there is an investigation of whether a charge-point operator should meet the peak demand for charging. The case under analysis is designed to represent a typical line-haul service between terminals in a major logistics system, with the finding that, in this case, a transition to battery-electric trucks seems cost effective for the company. Moreover, it is advisable for the company to use public fast chargers and these will likely become profitable given that the utilisation factor of the investigated public fast chargers may realistically exceed 20%.

Road transport is one of the most energy-consuming and greenhouse gas (GHG) emitting sectors. Progressive decarbonisation of electricity generation could support the ambitious target of road vehicle climate neutrality in two different ways: direct electrification with onboard electrochemical storage or a change of energy vector with e-fuels. The most promising, state-of-the-art electrochemical storages for road transport have been analysed considering current and future technologies (the most promising ones) whose use is assumed to occur within the next 10–15 years. Different e-fuels (e-hydrogen, e-methanol, e-diesel, e-ammonia, E-DME, and e-methane) and their production pathways have been reviewed and compared in terms of energy density, synthesis efficiency, and technology readiness level. A final energetic comparison between electrochemical storages and e-fuels has been carried out considering different powertrain architectures, highlighting the huge difference in efficiency for these competing solutions. E-fuels require 3–5 times more input energy and cause 3–5 times higher equivalent vehicle CO2 emissions if the electricity is not entirely decarbonised.

Extreme ambient temperatures cause electric vehicles’ batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control.

This article deals with determining statistically significant factors affecting the sale of battery electric vehicles in different European countries. Typical representative countries were selected on the basis of cluster analysis. The input data for multiple regression models and vector autoregressive models include data for the last decade and thus essentially cover the complete history of the electric car market. Attention is paid not only to the European leader but also to countries with a lower share of electric cars. The results of this study show the existence of a common factor in the countries with different development trends in the battery electric vehicle market. However, differences among individual countries are generally so significant that the identified factors vary from country to country.

In the last decade, the automotive industry has undergone a paradigm shift towards electrification. Electric vehicles have become increasingly popular, but so far, they have almost solely utilized single-ratio gearboxes. The use of multiple gear ratios has several potential benefits, including enabling the electric traction machine and inverter to operate in a more efficient region, increasing vehicle acceleration, gradeability, and top speed, and reducing overall traction system mass and volume. Performance vehicles, light to heavy-duty trucks, and buses may especially benefit from multi-speed gearboxes due to their high torque and power requirements. This paper covers the fundamentals of applying multi-speed gearboxes to EVs, the latest designs, and future trends. The efforts of both academia and industry in this field are covered. A range of topics are discussed, including gearbox topologies, gear ratio selection, gearbox losses, noise vibration and harshness, gearbox control, shift scheduling, and regenerative braking. Prior studies are presented showing that depending on the drive cycle, vehicle type, and gearbox configuration, drivetrain energy consumption may be reduced slightly or increased anywhere from a few percent to thirty percent when utilizing a multi-speed configuration. While multi-speed EV traction systems do show considerable promise, more investigation is needed to conclusively determine in what cases they can outperform highly optimized single-speed systems.

Today, the Internal Combustion Engine (ICE) is gradually being replaced by electric motors, which results in higher efficiency and low emission of greenhouse gases. The electric vehicle either works wholly or partially on electrical energy generated from batteries and ultra-capacitors. The battery or ultra-capacitor is either charged from the AC supply connected to a grid line in a plug-in electric vehicle or from ICE in a hybrid electric vehicle. Alternatively, the battery charges from the traction motor by regenerative braking. In the reverse direction, the energy from the battery or ultra-capacitor is injected into the AC grid line in the plug-in electric vehicle. Power electronic converters play a vital role in the conversion process from grid line to traction motor and in the reverse direction. In this paper, the role of power electronics converters in an electric vehicle is elaborated. The bidirectional DC-DC converter plays a vital role in the power conversion process of electric vehicles. The existing bidirectional DC-DC converter topologies are discussed with a comprehensive review, comparison, and application. Additionally, the advancement in power electronics converters to improve the efficiency and reliability of the vehicular system is elaborated.

The paper aims to present an analysis of the component sizes of commercially available vehicles with electrified powertrains. The paper provides insight into how the powertrain components (an internal combustion engine, an electric motor and a battery) of mass production electrified vehicles are sized. The data of wide range of mass production electrified vehicles are collected and analyzed. Firstly, the main requirements to performance of a vehicle are described. The power values to meet the main performance requirements are calculated and compared to the real vehicle data. Based on the calculated values of the power requirements the minimum sizes of the powertrain components are derived. The paper highlights how the sizing methodologies, described in the research literature, are implemented in sizing the powertrain of the commercially available electrified vehicles.

In the last decade, a number of research works in electrified vehicles have been devoted to the analysis of the electric consumption of battery electric vehicles and the evaluation of the main influencing factors. The literature analysis reveals that the electric motor size, efficiency, and driving condition substantially affect the electric energy stored in the vehicle battery. This paper studies the degree of sensitivity of energy consumption to electric motor size and to its efficiency map characteristics. In order to accomplish this task, three electric motors whose parameters are re-scaled to fit the maximum power torque and speed with different efficiency maps are simulated by installing them on two commercially available battery electric vehicles. This allows for isolating the influence of the efficiency map on electricity consumption. The original characteristics of the motors are then used to evaluate the influence on the electricity consumption of both the size and the efficiency characteristics. The results of the simulation revealed that the influences of the efficiency map and the electric motor size can be around 8–10% and 2–11%, respectively. When both factors are taken into account, the overall difference in electricity consumption can be around 10–21%.

One possible step for reducing humans’ use of fossil fuel due to transport tasks is to replace diesel trucks with battery electric ones. This paper introduces the energy distribution diagram, which makes it easy to visualise the trucks’ daily energy consumption over their full service life. The energy distribution is used to investigate which driving patterns are suitable for cost-effective battery electric trucks when compared to commercial diesel trucks. It is shown that the battery capacity that results in the lowest cost per kilowatt-hour propulsion energy depends on the driving pattern, and an algorithm for selecting the most cost-effective capacity is presented. In many instances, it was found that battery electric trucks competed favourably with diesel trucks, especially when the trucks had low variations in daily energy consumption. It is beneficial to determine the circumstances under which they may be cheaper, as this will facilitate the transition to battery electric trucks in segments with a reduced overall cost of ownership.

Abstract The urban transport electrification is going upward in many countries. However, this electrification faces many hurdles, among them, the limited range and cost that present-day electric vehicles (EV) have. This paper analyzes which Battery Electric Vehicles (BEV) could meet the current car travel needs in different uses. The results from a survey about distance travelled in daily trips in La Havana, allow the assessment of maximum average daily distance travel (MADT) by the private owners, cars in public service, taxis and leasing. The Lognormal distribution adjusts to MADT´s histogram in each group, and yields estimates characterizing its behavior. The use of a database of BEV with more than 60 electric vehicles allows to select an appropriate model for each use. The BEV´s range is normally expressed using several driving cycles, being EPA (Environmental Protection Agency) the most adequate, but the range by EPA is not always available. A regression analysis was made to obtain the EPA range as a function of range by others driving cycles. Finally, a Total Cost of Operation (TCOp) is calculated considering the purchase cost, energy cost, maintenance, etc. The outcomes are that for the private use, only small BEV with a strong subsidy or battery leasing can compete with an Internal Combustion Engine Vehicle (ICEV). In the public, taxis and leasing sector, the BEV are competitive in several years. The daily mileage and electricity cost are decisive. The use of renewable energy to charge batteries can reduce this period to some extent.

The publication presents a proposal of methodology for the evaluation of electric vehicle energy storage, based on examples of three types of batteries. Energy stores are evaluated in different categories such as cost, reliability, total range, energy density, battery life, weight, dependency on ambient temperature, and requirements of battery conditioning system. The performance of the battery systems were analyzed on exemplary 4 × 4 vehicle with 4 independent drives systems composed of inverters and synchronous in-wheel motors. The studies showed that the best results were obtained for energy storage built on LFP prismatic batteries, and the lowest ranking was given to energy storage built on cylindrical NMC batteries. The studies present the method of aggregation of optimization criteria as a valuable methodology for assessing design requirements and the risk of traction batteries in electric vehicles.

A hub motor is an effective drive system for Battery Electric Vehicles (BEVs). However, due to limitations on packaging and cost, there are few applications in which hub motors are taken as the only actuators for a brake vehicle. Most applications involve a Regenerative Braking System (RBS) combined with a Hydraulic Braking System (HBS). In this paper, a top hierarchy Advanced Emergency Braking System (AEBS) controller is designed in Matlab/Simulink and State-flow, including functionalities of basic emergency braking, brake force distribution between front and rear wheels, anti-lock braking and coordination between RBS and HBS based on Model Predictive Control (MPC); a Seven Degrees of Freedom (DOF) BEV chassis model is constructed and rear-end crash test scenarios are created in Carsim with a high and low road adhesion coefficient. A series of comparison tests show that not only are the stopping distances between the ego vehicle and target vehicle shorter, but also the braking torques, longitudinal slip ratio and rotation speed of each wheel are well controlled without wheel locking. To sum up, in addition to meeting the AEBS requirements of avoiding a rear-end collision, the control strategy developed in this paper also levels up braking performance and enhances vehicle stability on both high-mu and low-mu roads for BEVs driven by a hub motor independently.

Electric road systems (ERS)—infrastructure that allows for charging while driving—are currently considered in Sweden for electrifying long-haul trucking. The technology can also charge battery electric passenger vehicles (BEVs). This study utilizes real-world car movement data in Sweden and detailed spatial analysis to explore to what extent ERS could displace stationary charging if it is available for BEVs and the expected benefits. We find that ERS utilization and the minimum battery ranges depend more on visited locations and home locations and less on the annual travel distances of car users. The median battery ranges required by rural residents are 15–18% greater than for urban residents. Our scenarios suggest that a mix of ERS and home-charging would achieve the most significant benefits. ERS with home charging reduces the required battery range by 62–71% in the main scenarios, and the net savings from smaller BEV batteries exceed the cost of ERS. Eliminating all stationary charging is feasible for many but not all vehicles. Utilizing ERS could also significantly reduce peak BEV charging by distributing charging throughout the day. We also find that there is a considerable difference between the maximum possible and minimum needed charging on ERS, which can significantly influence ERS revenues. Future studies can expand to include other modes (e.g., trucks) to provide more holistic assessments of economic benefits and charging needs.

The transportation sector produces a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large-scale adoption of battery electric vehicles and has led to several policies and laws aimed at incentivizing their sales. While electric vehicles comprise a small percentage of the overall fleets of vehicles, the expected production of electric vehicles is soon expected to be in the millions. This will create challenges in providing an adequate charging infrastructure, as well as the ensuing management of the overall electricity demand at the grid level. In this work, a novel smart-charging protocol for battery electric vehicle charging at workplace parking structures is proposed. The Octopus Charger-based Mixed Integer Linear Programming protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging periods for their assigned vehicles. The proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of the proposed protocol. The proposed protocol resulted in improved peak load reductions for all simulated smart-charging scenarios when compared with uncontrolled charging. Critically, the assignment algorithm resulted in a number of required chargers close to the theoretical minimum.

Greenhouse gas emissions, produced by various sectors, including transportation, are significantly impairing the environment and drive climate change. Battery electric vehicles are increasingly seen as a way to alleviate these problems, but they must be charged with electricity produced through environmentally friendly methods. This paper investigates a possible relationship between battery electric vehicles and solar photovoltaic panels using ENABLE.EU household survey data from ten European countries in autumn 2017–spring 2018. Based on the estimates from a recursive bivariate probit model, it is found that the probability that a household owns a battery electric vehicle increases significantly if said household owns solar photovoltaic panels. This suggests that a policy encouraging the home charging of battery electric vehicles using solar photovoltaic panels that includes an energy storage facility could speed up the transition to the use of these vehicles.