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To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. [ABSTRACT FROM AUTHOR]

Do U.S. households with battery electric vehicles (BEVs) drive less or more than U.S. households with internal combustion engine vehicles (ICEVs)? Answering this question is important to policymakers and transportation planners concerned with reducing vehicle miles traveled and the emissions of greenhouse gases from transportation. So far, this question has not been answered satisfactorily, possibly because of the relatively low number of EVs in the U.S. until recently, but also because of methodological issues. In this paper, we aim to fill this gap by analyzing data from the 2017 National Household Travel Survey (NHTS). We apply propensity score matching (PSM), a quasi-experimental method, to examine the differences in self-reported annual mileage and calculated daily mileage for various trip purposes among households with only BEVs (BEV-only), households with both BEVs and ICEVs (BEV+), and households without BEVs (non-BEV households). Our findings indicate that households with BEVs drive fewer annual miles than non-BEV households, but typically travel no less than they do for daily activities. This apparent discrepancy is likely due to taking fewer longer trips because the public charging infrastructure was still in its infancy in 2017, and its reliability was questionable. As technological progress is helping to overcome current battery limitations, policymakers may consider measures for fostering fast charging technologies while pondering new measures to fund both the charging infrastructure and the road network. [ABSTRACT FROM AUTHOR]

The production and personal use of passenger vehicles contribute significantly to greenhouse gas (GHG) emissions, making personal transport a major contributor. In response to increasing pressure from regulators and consumers to lower emissions, original equipment manufacturers (OEMs) have introduced alternative powertrains, such as battery electric vehicles (BEVs). To assess the economic and environmental feasibility of BEVs, OEMs conduct various life cycle assessment (LCA) approaches as part of their internal management practices. However, the public presentation of their results is often ambiguous and of unverifiable quality. This study conducts a review of sustainability impact reporting using the GREET model, which is based on the total vehicle life cycle. The paper provides a summary of the input data requirements for validating emissions from both the fuel and vehicle cycles as part of the proposed model. The availability of the model components was tested against actual public corporate environmental, social and governance reporting disclosures of the largest global automotive OEMs, indicating a deficit in the public presentation of essential sustainability information. The implications of the research suggest that LCA models could benefit sustainability accounting by incorporating proper tagging during accounting practices and taking advantage of digital accounting and reporting practices such as the extensible business reporting language (XBRL). By further developing the model, sustainability reporting can benefit from its structure and workflow, thus ensuring improved information validity for stakeholders. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. [ABSTRACT FROM AUTHOR]

The transport sector is one of the main contributors to global CO2 emissions and the transport sector in the Kingdom of Morocco is no exception. To combat this, two important agreements aimed at reducing greenhouse gas emissions were created, the Paris Agreement and the Nationally Determined Contributions. The adoption of battery electric vehicles is one way of helping to reduce transport-related emissions. However, there are several barriers to the adoption of battery electric vehicles in Morocco. The objective of this paper is to identify these barriers and to propose solutions to overcome them based on a survey of 209 responses that were analyzed using the Smart-PLS 4 approach. The study found that the financial attributes, maintenance, design, social reinforcement, and lack of incentives related to battery electric vehicles are the most significant factors that could cause the hindrance of the adoption of battery electric vehicles. Therefore, more affordable and attractive battery electric vehicles should be made more accessible. Moreover, increased technical training facilities should be mobilized to boost further efforts and increase experience in the field of BEV maintenance in Morocco. By implementing these solutions, Morocco can increase the uptake of battery electric vehicles and reduce its greenhouse gas emissions. This will help Morocco reach its Nationally Determined Contributions and protect the environment as well as the health of its population. [ABSTRACT FROM AUTHOR]

Potential differences among developed and emerging countries regarding the battery electric vehicles (BEVs) adoption are highlighted in the literature. However, most studiesfocus onfactors influencing BEV penetration in mature markets. In this paper, we explore the spatial adoption of electric vehicles and its determinants using data from Hungary, an emerging country. We show that spatial heterogeneities in the adoption rates of BEVs are important. Without taking these heterogeneities into account, only thefast chargers exhibit significant positive effects on BEV registration rate. We show that other local conditions, such as income and urbanization are also important factors explaining the new BEV registration rate. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve the problem for battery electric vehicles of low-precision and time-consuming inspection. A novel method of driving cycle development for battery electric vehicles' operational safety is proposed in this paper. First, three inspection items are proposed based on relevant testing standards. The inspection calculation method of operational safety is developed based on the acceleration changing rate. Then the multi-cycle inspection method with the stable pedal mode is developed, and the Gauss filtering algorithm is applied for data preprocessing. A rapid inspection driving cycle construction method based on support vector machine is proposed, and a driving cycle is built with a total time of 204 s by fusing and splicing kinematic fragments. Finally, the proposed inspection calculation method is used to validate the operational safety inspection items by tracking the established rapid inspection driving cycle based on the test bench. The results shown are those that qualified the range of acceleration changing rate for driving stability [−0.35, −0.04]. The range for gliding smoothness is [0.05, 0.09]. The range for braking coordination is [−0.04, 0.095]. The maximum RMSE between the constructed rapid inspection segments is 9%, and the maximum RMSE between the tested driving segments is 6%. Test results meet design requirements. The thresholds for operational safety inspection items are evaluated based on the test results. We set less than 0.5 as the safety threshold for driving stability. During the experiment, gliding was less than 0.1 as the safety threshold for gliding comfort, and during braking it was less than 0.1 as the safety threshold for vehicle braking coordination. [ABSTRACT FROM AUTHOR]

The dual active bridge DC-DC converter is a promising power converter used in several applications. Much research has been focusing on the study of such a converter from different angles. In this paper, an optimal design of the DAB converter is proposed to provide minimal reactive power in addition to reduced weight and size for the converter magnetic components in order to assure the DC-DC conversion stage of battery electric vehicles' powertrains. Two modes of operation are considered in order to fulfill such a requirement and minimize reactive power of the converter (circulating current/conduction losses): an optimal extended phase shift (EPS) modulation along with an optimal triangular phase shift (TrgPS) modulation. The operation of the DAB converter under the two modes is being driven using a model predictive controller. Simulation results using MATLAB/Simulink presented in the paper show that the operation of the DAB converter for such an application is optimal when operating under optimal TrgPS modulation. In addition to the aforementioned features, it also solves many other concerns, such as transient load fluctuation and input voltage disturbance effects, and provides ease of control. [ABSTRACT FROM AUTHOR]

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components. [ABSTRACT FROM AUTHOR]

This article overviews Poland's current electric vehicle infrastructure development. It discusses market segmentation and the analysis of charging standards, connectors, and types of charging. The paper focuses on Poland's charging infrastructure, including costs and charging times for popular electric vehicle models in 2022. It highlights the challenges faced by charging operators and the barriers to infrastructure development. The article also presents the outlook for the electric vehicle market in Poland until 2025 and 2030. Furthermore, it examines private charger development, particularly in prosumer households with renewable energy sources. The implementation of smart charging and the potential for vehicle-to-grid technology in Poland are addressed. Lastly, a comparative analysis of incentives for electric vehicle users in Poland and Norway is discussed in the context of achieving 100% zero-emission vehicle sales by 31 December 2035, in Poland. [ABSTRACT FROM AUTHOR]

The optimization of power parameters is the key to the design of pure electric vehicles. Reasonable matching of the relationship between various parameters can effectively reduce energy consumption and achieve energy sustainability. In this paper, several vehicle performance indexes such as maximum vehicle speed, acceleration time and power consumption per 100 km were used as optimization target vectors, and transmission ratio was used as optimization variable to establish the optimization problem of parameter matching. Then, the feasible domain of the transmission ratio was obtained by taking the lowest performance index of the vehicle as the constraint condition. In the feasible domain, the multi-objective genetic algorithm is used to solve the optimization problem. The Pareto optimal solution set is obtained for fixed ratio transmission and two-gear transmission, which is used as an alternative solution set. The final parameter-matching scheme is determined by comparing the alternative scheme set of different motors comprehensively. The results show that the competition relationship between multiple optimizable indexes can be described effectively by solving the Pareto front. Specifically, the Pareto optimal solution set for the motor A + fixed transmission scheme is 1.33~1.85; the Pareto optimal solution set for the motor A + 2 transmission scheme is [1.72, 0.98]~[2.99, 1.57], and the Pareto optimal solution set for the motor B + 2 transmission scheme is [2.99, 1.40]~[2.99, 1.57]. The motor A + fixed transmission scheme does not require A clutch and does not require designing a shift algorithm. Therefore, after comprehensive consideration, the motor A + fixed transmission ratio transmission scheme is set as the final scheme. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to provide a systematic overview with guidelines how to use partial least squares (PLS) path modeling in longitudinal studies. Practical examples from a study of the acceptance of battery electric vehicles (BEVs) in corporate fleets are used for demonstration purposes.Design/methodology/approach In this study, data at three points in time were collected: before the initial use of a BEV, after three and after six months of extensive usage of BEVs.Findings Three different models are identified depending on the research objective and on the data basis. Multigroup analyses are suggested to test the difference between the path coefficients of latent variables at different points in time. Limitations for the use of repeated cross-sectional data have to be observed.Originality/value Academics and practitioners will benefit from this paper by receiving an overview of the different PLS path models in longitudinal studies. A decision-tree enables them to make a choice regarding the most appropriate model and suggests a sequence of complementary analyses. So far, there is a lack of a tutorial type paper delivering such guidance. [ABSTRACT FROM AUTHOR]

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles.

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source. [ABSTRACT FROM AUTHOR]

Voltage violation tends to be one of the dominant constraints or critical factors that limit the penetration of small wind turbines (SWTs) and battery electric vehicles (BEVs) in distribution networks. To quantify the voltage violation, a statistical time series based approach is developed in this paper. The daily time varying characteristics of electric loads, wind speeds and BEV charging loads have been taken into consideration in a statistical manner. The method has been compared with the conventional time series approach. It is proven that the proposed methodology, while providing a global picture on voltage violations, can potentially be used for fast evaluation of the probability of a voltage violation occurring in a network. The methodology has been applied to an example distribution network. Results show that the proposed methodology can reduce the computational requirement by over 98%, while the difference between the results obtained using the proposed method and applying the conventional time series approach is within 12.5%. [ABSTRACT FROM PUBLISHER]

New advanced power conversion systems play an essential role in the extension of range and life of batteries. This paper proposes a new modular multilevel converter with embedded electrochemical cells that achieves very low cell unbalancing without traditional balancing circuits and a negligible harmonic content of the output currents. In this new topology, the cells are connected in series by means of half-bridge converters, allowing high flexibility for the discharge and recharge of the battery. The converter features a cell balancing control that operates on each individual arm of the converter to equalize the state of charge of the cells. The paper shows that the proposed control does not affect the symmetry of the three-phase voltage output, even for significantly unbalanced cells. The viability of the proposed converter for battery electric vehicles and the effectiveness of the cell balancing control are confirmed by numerical simulations and experiments on a kilowatt-size prototype. [ABSTRACT FROM AUTHOR]

In the context of a global shift towards renewable energies and climate change mitigation, the market for electric vehicles has experienced a remarkable upswing, with battery electric vehicles (BEVs) leading this transformative wave. The appeal of BEVs lies in their ability to significantly curtail CO2 emissions by supplanting the traditional internal combustion engine (ICE) with an electric motor. This pivotal change in vehicular technology extends its influence to various subsystems, including automotive lighting. Headlights are particularly sensitive to the thermal environment they operate in, which can profoundly affect their functionality and durability. The removal of an ICE in BEVs typically results in a reduction in heat exposure to headlight components, prompting a potential reevaluation of their design. This article presents a comprehensive analysis of temperature distributions within headlight units, comparing BEVs and ICE vehicles. The study encompasses a robust dataset of nearly 30,000 vehicles from around the globe, taking into account the impact of ambient temperature on headlight operation. The investigation delineates the distinct thermal behaviors of the two vehicle categories and offers strategic recommendations for conceptual modifications of headlights in BEVs. These adjustments are aimed at enhancing headlight efficacy, prolonging lifespan, and furthering the sustainability objectives of electric mobility. [ABSTRACT FROM AUTHOR]

Environmental problems such as air pollution and greenhouse gas emissions are especially challenging in urban areas. Electric mobility in different forms may be a solution. While in recent years a major focus was put on private electric vehicles, e-mobility in public transport is already a very well-established and mature technology with a long history. The core objective of this paper is to analyze the economics of e-mobility in the Austrian capital of Vienna and the corresponding impact on the environment. In this paper, the historical developments, policy framework and scenarios for the future development of mobility in Vienna up to 2030 are presented. A major result shows that in an ambitious scenario for the deployment of battery electric vehicles, the total energy demand in road transport can be reduced by about 60% in 2030 compared to 2018. The major conclusion is that the policies, especially subsidies and emission-free zones will have the largest impact on the future development of private and public e-mobility in Vienna. Regarding the environmental performance, the most important is to ensure that a very high share of electricity used for electric mobility is generated from renewable energy sources. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are considered a potential solution to tackle the main environmental issues of road transport. However, their deployment is limited, notably for long-distance trips where BEVs suffer from short ranges, slow charges, and lack of infrastructure. Considering these technological, sociological, and environmental stakes of BEV deployment, this paper aims to highlight the interests of multi-perspective analysis for electromobility. It introduces a methodology to analyse and compare performances of both conventional vehicles and BEVs, considering diverse possible usages. Fleet simulations on the highway linking Paris to Lyon (France) were conducted to provide recommendations for designers and insights for electromobility stakeholders such as drivers, carmakers, or infrastructure planners. Performance criteria such as average travel time, the number of charging points to install, and environmental impacts have been used to compare vehicles and evaluate the influence of users' behaviours and electricity mix. This study quantifies the travel time differences between conventional vehicles, average BEVs, and high-end BEVs and the effect of the driving speed choice and battery management on the performance criteria. Finally, electricity consumption is identified as a significant source of the BEV highway traffic environmental impacts, although they depend on the regional electricity mix; the battery life cycle is also an important source of impacts. [ABSTRACT FROM AUTHOR]

In the light of global warming, this paper develops a framework to compare energy and transportation technologies in terms of cost-efficient GHG emission reduction. We conduct a simultaneous assessment of economic and environmental performances through life cycle costing and life cycle assessment. To calculate the GHG mitigation cost, we create reference systems within the base scenario. Further, we extend the concept of the mitigation cost, allowing (i) comparision of technologies given a limited investment resource, and (ii) evaluation of the direct impact of policy measures by means of the subsidized mitigation cost. The framework is illustrated with a case of solar photovoltaics (PV), grid powered battery electric vehicles (BEVs), and solar powered BEVs for a Belgian small and medium sized enterprise. The study's conclusions are that the mitigation cost of solar PV is high, even though this is a mature technology. The emerging mass produced BEVs on the other hand are found to have a large potential for cost-efficient GHG mitigation as indicated by their low cost of mitigation. Finally, based on the subsidized mitigation cost, we conclude that the current financial stimuli for all three investigated technologies are excessive when compared to the CO market value under the EU Emission Trading Scheme. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are gaining market shares due to their ability to employ clean energy, their smooth operation and reduced noise, pollutant emissions and maintenance. Batteries are one of the key technologies in BEV since they strongly affect the vehicle cost and driving range, two of the major concerns of BEV costumers. While the energy storing capabilities of BEVs usually exceed commuting requirements, batteries can be also utilized for home energy management system using bi-directional charging technology. This paper introduces an efficient energy management system for a smart home with BEVs and a bidirectional charger by addressing the corresponding optimal control problem of deciding the battery charging and discharging strategy to minimize energy expenditure and cost. To this aim, the electricity price and expenditure of upcoming weeks is forecasted using data from the present week, and a model of the complete system is used to find the optimal solutions by means of backward induction in a receding horizon approach. The proposed strategy relies on currently available information about the home and vehicle energy expenditure and energy prices in the recent past. The results of the study show a reduction of the electricity cost above 20% in the considered use-case. • Combined use of BEV and smart home for global energy consumption optimization. • Optimization of energy consumption considering hourly cost and power demand. • Backward induction technique embedded in MPC controller for energy optimization. • Reduction in electricity consumption costs by over 20% in a real application. [ABSTRACT FROM AUTHOR]

Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars.

Data on state-of-the-art battery electric vehicles are crucial to academia; however, these data are not published due to non-disclosure policies in the industry. As a result, simulation models and their analyses are based on assumptions or insider information. To fill this information gap, we present a comprehensive analysis of the electric powertrain of a Tesla Model 3 Standard Range Plus (SR+) from 2020 with lithium iron phosphate (LFP) cells, focusing on the overall range. On the vehicle level, we observe the resulting range in multiple test scenarios, tracing the energy path from source to sink by conducting different test series on the vehicle dynamometer and through alternating current (AC) and direct current (DC) charging measurements. In addition to absolute electric range tests in different operating scenarios and electric and thermal operation strategies on the vehicle level, we analyze the energy density and the power unit's efficiency on the component level. These tests are performed through procedures on the chassis dynamometer as well as efficiency analysis and electric characterization tests in charge/discharge scenarios. This study includes over 1 GB of attached measurement data on the battery pack and vehicle level from the lab to the real-world environment available as open-source data. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle's vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are being considered as a replacement technology for diesel vehicles in underground mining of mineral resources. However, many issues related to BEVs in industrial applications still require more detailed research. One significant topic is the heat load—BEVs' heat emission into the working environment during regular operation. This paper provides an overview of the thermal behavior of NMC and LFP Li-ion cells tested under various conditions, which is an important step in determining the overall heat load of BEVs. The surface temperature of three NMC and one LFP 18650 Li-ion cells was monitored during charging and discharging at four different rates, at constant ambient temperatures of 30 °C and 40 °C. The main results pertain to the cell's thermal behavior during charging and discharging, highlighting the distinctive temperature profiles of NMC and LFP cells.

Automation and electrification in road transport are trends that will influence several economic sectors of the European economy. The automotive maintenance and repair (M&R) sector will experience the effects of such transitions in the long term. This paper assesses the research in the road transport to derive the factors that may influence the M&R demand based on Battery Electric Vehicles (BEVs) and Autonomous Vehicles (AVs) uptake. Starting from current scientific research and grounded on interviews with experts, the paper reviews major drivers influencing M&R demand and provides indications on possible future effects. While for BEVs, previous work has been conducted to estimate the M&R cost variations, the research addressing the impacts of AVs deployment on the M&R sector is at its incipient stage, hence the views of experts were paramount to shed light on this topic. We identified a scientific consensus that BEVs have less M&R requirements compared with Conventional Vehicles (CVs). For AVs, our analysis and expert views identify some important factors influencing M&R requirements: hardware components, software that enables autonomy, the rise in vehicle kilometres travelled leading to higher wear and tear of replaceable parts, the need for adequate cleaning services, especially for fleets and shared vehicles. Further work should look at the impact of regulations and the non-insurable risks linked to M&R requirements.

Throughout decades, conflicts related to the access and usage of various energy sources have caused political tensions between nations and confederations of states. Thus, partially to decrease the dependence on fossil fuels, a thorough transition towards renewable energies has been promoted by several regional and national governments as well as by multinational institutions such as the European Union. In this context, the automotive industry has particularly been held responsible for the production of negative externalities, such as global greenhouse gas emissions (GHG emissions), noise and air pollution. To a notable extent, these externalities were caused by vehicles running on fossil fuels such as petroleum products, including gasoline, diesel fuel and fuel oil. Accordingly, it is often argued that replacing vehicles run by internal combustion engines (ICEs) with so-called alternative fuel vehicles (AFVs), particularly with plug-in electric vehicles (PEVs), is crucial to increase the sustainability of the transport sector. Moreover, several EU-member states aim to reduce the vehicle-related petrol and diesel demand to decrease their dependence on foreign energy sources. However, one must consider that there are important economic costs related to such a transition process. This paper evaluates the short-term and long-term effects of fiscal policies on the European automotive market in the period from 2010 to 2018, focusing on the impact of mentioned public incentives for AFVs. This public interventionism will be critically evaluated to examine the effectiveness of government incentives in promoting AFVs, particularly for plug-in electric vehicles (PEVs). The author argues that the rather positive sales evolution of AFVs was not caused by corresponding actual customer demand but mainly by governmental policies in an increasingly interventionist market. He acknowledges that the growing variety of available PEV models, the increasing driving range of electric vehicles, as well as their decreasing production costs due to economies of scale, have helped PEVs to become more competitive. However, the concern should be raised that mentioned public interventionism is unsustainable from a macroeconomic perspective, possibly leading to significant market distortion and a new artificial market bubble. The narrowed focus on battery electric vehicles prevents the market from further elaborating on other potentially more sustainable technologies. Moreover, from a geostrategic perspective, the transition of the European automotive industry towards electrification is likely to reduce the EU's dependency on imported fossil fuels but enables several non-European automotive brands to conquer a significant market with their new competitive plug-in electric vehicle technologies. [ABSTRACT FROM AUTHOR]

Although battery electric vehicles (BEVs) are locally emission-free and assist automakers in reducing their carbon footprint, two major disadvantages are their shorter range and higher production costs compared to combustion engines. These drawbacks are primarily due to the battery, which is generally the heaviest and most expensive component of a BEV. Lightweight measures (strategies to decrease vehicle mass, e.g., by changing materials or downsizing components) lower energy consumption and reduce the amount of battery energy required (and in turn battery costs). Careful selection of lightweight measures can result in their costs being balanced out by a commensurate reduction in battery costs. This leads to a higher efficiency vehicle, but without affecting its production and development costs. In this paper, we estimate the lightweight potential of BEVs, i.e., the cost limit below which a lightweight measure is fully compensated by the cost savings it generates. We implement a parametric energy consumption and mass model and apply it to a set of BEVs. Subsequently, we apply the model to quantify the lightweight potential range (in €/kg) of BEVs. The findings of this paper can be used as a reference for the development of cheaper, lighter, and more energy-efficient BEVs. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of modular multilevel converters with integrated battery cells when used as traction drives for battery electric vehicles. In this topology, individual battery cells are connected to the dc link of the converter submodules, allowing the highest flexibility for the discharge and recharge. The traditional battery management system of battery electric vehicles is replaced by the control of the converter, which individually balances all the cells. The performance of the converter as a traction drive is assessed in terms of torque–speed characteristic and power loss for the full frequency range, including field weakening. Conduction and switching losses for the modular multilevel converter are calculated using a simplified model, based on the datasheet of power devices. The performance of the modular multilevel converter is then compared with a traditional two-level converter. The loss model of the modular multilevel converter is finally validated by experimental tests on a small-scale prototype of traction drive. [ABSTRACT FROM PUBLISHER]

The automotive industry today faces three major transitions: the emergence of autonomous driving, electric powertrain replacing the internal combustion engine, and changes in possession of automobiles, e.g., increased usage of car sharing. As all three transitions are fostered by technologies that drive digital transformation of automobiles, the Technology Acceptance Model (TAM) by Davis represents the underlying research model of this paper. Hypotheses are developed and tested for a sample of 1177 participants using Partial Least Squares Structural Equation Modeling (PLS-SEM). Group differences are investigated for three markets: Europe, North America, and China. The paper confirms the underlying assumptions of the Technology Acceptance Model in the context of automobiles. Further, it illustrates influential societal norms and individual experiences for technology acceptance. In addition, compound effects for technology acceptance are found, e.g., the perceived enjoyment of electric driving affects the acceptance of autonomous driving and car possession behavior. The novel approach to integrate three different technologies within the Technology Acceptance Model requires unifying items to a level which makes them comparable, limiting the results for each individual technology. For practice, automotive manufacturers obtain advice on how to foster technology acceptance. For society, the paper uncovers the role of societal norms for technology acceptance in the context of automobiles. Policy makers can obtain insights on how to successfully increase technology acceptance, e.g., for environmental purposes. Conclusively, the paper applies the Technology Acceptance Model for three developments in the context of automobiles, thereby extending current research using the Technology Acceptance Model. [ABSTRACT FROM AUTHOR]

Transport-related activities represented 34% of the total carbon emissions in the UK in 2022 and heavy-duty vehicles (HGVs) accounted for one-fifth of the road transport greenhouse gas (GHG) emissions. Currently, battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (FCEVs) are considered as suitable replacements for diesel fleets. However, these technologies continue to face techno-economic barriers, creating uncertainty for fleet operators wanting to transition away from diesel-powered internal combustion engine vehicles (ICEVs). This paper assesses the performance and cost competitiveness of BEV and FCEV powertrain solutions in the hard-to-abate HGV sector. The study evaluates the impact of battery degradation and a carbon tax on the cost of owning the vehicles. An integrated total cost of ownership (TCO) model, which includes these factors for the first time, is developed to study a large retailer's HGV fleet operating in the UK. The modelling framework compares the capital expenditures (CAPEX) and operating expenses (OPEX) of alternative technologies against ICEVs. The TCO of BEVs and FCEVs are 11% to 33% and 37% to 78% higher than ICEVs; respectively. Despite these differences, by adopting a longer lifetime for the vehicle it can effectively narrow the cost gap. Alternatively, cost parity with ICEVs could be achieved if BEV battery cost reduces by 56% or if FCEV fuel cell cost reduces by 60%. Besides, the pivot point for hydrogen price is determined at £2.5 per kg. The findings suggest that BEV is closer to market as its TCO value is becoming competitive, whereas FCEV provides a more viable solution than BEV for long-haul applications due to shorter refuelling time and lower load capacity penalties. Furthermore, degradation of performance in lithium-ion batteries is found to have a minor impact on TCO if battery replacement is not required. However, critical component replacement and warranty can influence commercial viability. Given the high costs, we propose financial incentives and vehicle tax reforms to reduce costs of critical components that will encourage the roll-out of zero emission HGVs. • TCO model employs real-world fleet data to compare HGV powertrain technologies. • Maximising vehicle lifetime is key to reduce the TCO gap and get closer to parity. • BEVs have a TCO 11%–33% higher than ICEVs. • FCEVs have a TCO 37%–78% higher than ICEVs. • Degradation in lithium-ion batteries is found to have a minor impact on TCO value. [ABSTRACT FROM AUTHOR]

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet. [ABSTRACT FROM AUTHOR]

Different measures are considered by the authorities to tackle the negative impacts of city logistics. Among them, battery electric vehicles are seen as a promising solution, but high purchase cost represents an important barrier to their adoption. However, these vehicles benefit also from low operational costs. This different cost structure between electric and conventional vehicles makes therefore a comprehensive cost analysis necessary for fleet managers who want to assess the real competitiveness of the vehicles. Hence, we developed a total cost of ownership model to assess the competitiveness of quadricycles and light commercial vehicles for freight transport companies. This paper presents the results for 7 battery electric vehicles, 5 diesel vehicles and 3 petrol vehicles. The results of a sensitivity analysis explored also the possible measures that can support their competitiveness. The model shows that battery electric vehicles have a better competitive position than petrol vehicles but they do not compete yet with diesel vehicles. A sensitivity analysis shows however that their total cost of ownership can become lower than diesel vehicles depending on their utilization, future market conditions or government support. Still, electric quadricycles appear to be currently an affordable solution for transport operators to adopt electric vehicles since their total costs of ownership is lower than diesel light commercial vehicles. [ABSTRACT FROM AUTHOR]

In order to study the influence of high and low temperature environments on the energy consumption of battery electric vehicles, this paper takes Tianjin as an example to collect the actual road data of 8 battery electric passenger cars for eight months. It determines the correlation between each characteristic parameter and energy consumption based on the maximum mutual information coefficient of short stroke segments, of which the average driving speed correlation is the highest, which is 0.785. Given the two factors that have a greater impact on energy consumption in high and low temperature environments: driving resistance and air conditioning on motion and idle energy consumption analysis, the study shows that in terms of driving resistance, high temperature and low temperature energy consumption are reduced and increased by 5.4% and 13.3% respectively relative to normal temperature; The energy consumption of high temperature and low temperature air conditioner switching on increased by 17.12% and 47.48% respectively compared with the air conditioner off.

With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers' route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers' battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers' en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal. [ABSTRACT FROM AUTHOR]

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution. [ABSTRACT FROM AUTHOR]

This paper examines the life-cycle greenhouse gas (GHG) emissions reduction and associated marginal costs (MC) in Japan when the government enhances the transition to battery electric passenger vehicles (BEVs) by implementing a rapid investment regime for charging stations and fossil-fueled vehicle bans. Important factors affecting BEV emissions are battery performance, battery production technology, electricity generation mix and battery minimum state of charge (SOC min). We find that in addition to a rapid investment regime, a fossil-fueled vehicles ban policy is necessary to significantly increase the uptake of BEVs and achieves the deepest reduction in life cycle GHG emissions. Under this policy, with carbon neutral electricity by 2050 and a low SOC min charging regime, life-cycle GHG emissions are reduced by 48% and the MC by 53% in 2060. A carbon neutral light transport fleet in 2050 will require carbon capture and storage to offset 24 million tonnes-CO 2 equivalent of emissions even if electricity is carbon neutral by 2050 and users adhere to a low SOC min charging regime. [Display omitted] • Enhanced infrastructure development has a little impact on the uptake of BEVs. • The more stringent vehicle ban reduces life cycle GHG emissions the most. • Battery charging regime is crucial to GHG emissions reduction and associated cost. • Improving battery and vehicle production is crucial to GHG emissions reduction. • Carbon neutral light vehicle transport by 2050 requires carbon capture and storage. [ABSTRACT FROM AUTHOR]

Although battery electric vehicles (BEVs) are locally emission-free and assist automakers in reducing their carbon footprint, two major disadvantages are their shorter range and higher production costs compared to combustion engines. These drawbacks are primarily due to the battery, which is generally the heaviest and most expensive component of a BEV. Lightweight measures (strategies to decrease vehicle mass, e.g., by changing materials or downsizing components) lower energy consumption and reduce the amount of battery energy required (and in turn battery costs). Careful selection of lightweight measures can result in their costs being balanced out by a commensurate reduction in battery costs. This leads to a higher efficiency vehicle, but without affecting its production and development costs. In this paper, we estimate the lightweight potential of BEVs, i.e., the cost limit below which a lightweight measure is fully compensated by the cost savings it generates. We implement a parametric energy consumption and mass model and apply it to a set of BEVs. Subsequently, we apply the model to quantify the lightweight potential range (in €/kg) of BEVs. The findings of this paper can be used as a reference for the development of cheaper, lighter, and more energy-efficient BEVs.

Battery Electric Vehicles (BEVs) are considered to have higher energy efficiency and advantages to better control CO2 emissions compared to Internal Combustion Engine Vehicles (ICEVs). However, in the context that a large amount of thermal power is still used in developing countries, the CO2 emission reduction effectiveness of BEVs can be weakened or even counterproductive. To reveal the impact of the electricity generation mix on carbon emissions from vehicles, this paper compares the life cycle carbon emissions of BEVs with ICEVs considering the regional disparity of electricity generation mix in China. According to Life Cycle Assessment (LCA) analysis and regional electricity carbon intensity, this study demonstrates that BEVs in the region with high penetration of thermal power produce more CO2 emissions, while BEVs in the region with higher penetration of renewable energy have better environmental performance in carbon emission reduction. For instance, in the region with over 50% penetration of renewable energy, a BEV can reduce more CO2 (18.32 t) compared to an ICEV. Therefore, the regions with high carbon emissions from vehicles need to increase the proportion of renewable generation as a priority rather than promoting BEVs. [ABSTRACT FROM AUTHOR]

Environmental problems such as air pollution and greenhouse gas emissions are especially challenging in urban areas. Electric mobility in different forms may be a solution. While in recent years a major focus was put on private electric vehicles, e-mobility in public transport is already a very well-established and mature technology with a long history. The core objective of this paper is to analyze the economics of e-mobility in the Austrian capital of Vienna and the corresponding impact on the environment. In this paper, the historical developments, policy framework and scenarios for the future development of mobility in Vienna up to 2030 are presented. A major result shows that in an ambitious scenario for the deployment of battery electric vehicles, the total energy demand in road transport can be reduced by about 60% in 2030 compared to 2018. The major conclusion is that the policies, especially subsidies and emission-free zones will have the largest impact on the future development of private and public e-mobility in Vienna. Regarding the environmental performance, the most important is to ensure that a very high share of electricity used for electric mobility is generated from renewable energy sources.

This article reviews the design and evaluation of different DC-DC converter topologies for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The design and evaluation of these converter topologies are presented, analyzed and compared in terms of output power, component count, switching frequency, electromagnetic interference (EMI), losses, effectiveness, reliability and cost. This paper also evaluates the architecture, merits and demerits of converter topologies (AC-DC and DC-DC) for Fast Charging Stations (FCHARs). On the basis of this analysis, it has found that the Multidevice Interleaved DC-DC Bidirectional Converter (MDIBC) is the most suitable topology for high-power BEVs and PHEVs (> 10kW), thanks to its low input current ripples, low output voltage ripples, low electromagnetic interference, bidirectionality, high efficiency and high reliability. In contrast, for low-power electric vehicles (<10 kW), it is tough to recommend a single candidate that is the best in all possible aspects. However, the Sinusoidal Amplitude Converter, the Z-Source DC-DC converter and the boost DC-DC converter with resonant circuit are more suitable for low-power BEVs and PHEVs because of their soft switching, noise-free operation, low switching loss and high efficiency. Finally, this paper explores the opportunity of using wide band gap semiconductors (WBGSs) in DC-DC converters for BEVs, PHEVs and converters for FCHARs. Specifically, the future roadmap of research for WBGSs, modeling of emerging topologies and design techniques of the control system for BEV and PHEV powertrains are also presented in detail, which will certainly help researchers and solution engineers of automotive industries to select the suitable converter topology to achieve the growth of projected power density. [ABSTRACT FROM AUTHOR]

Compared with traditional fuel vehicles, battery electric vehicles (BEVs) as a sustainable transportation form can reduce carbon dioxide emissions and save energy, so its market share has great potential. However, there are some problems, such as: Their limited range, long recharging time, and scarce charging facilities, hindering improvement in the market potential of BEVs. Therefore, perfect and efficient charging facility deployment for BEVs is very important. For this reason, the optimal locations for charging stations for BEVs are investigated in this paper. Instead of flow-based formulation, this paper is based on agents under strictly imposed link capacity constraints, where all agents can select their routes and decide on the battery recharging plan without running out of charge. In our study, not only the locations of charging stations, but also the size of charging stations with the different number of chargers, would be taken into consideration. Then, this problem is formulated as a location problem for BEV charging stations of multiple sizes based on agents under link capacity constraints. This problem is referred to as the agent-refueling, multiple-size location problem with capacitated network (ARMSLP-CN). We formulate the ARMSLP-CN as a 0–1 mixed-integer linear program (MILP) with the aim to minimize the total trip time for all agents, including four parts, namely, the travel time, queue time, fixed time for recharging, and variable recharging time depending on the type of charger and the amount of power recharged, in which commercial solvers can solve the linearized model directly. To demonstrate this model, two different numerical instances are designed, and sensitivity analyses are also presented. [ABSTRACT FROM AUTHOR]

In the current context of the ban on fossil fuel vehicles (diesel and petrol) adopted by several European cities, the question arises of the development of the infrastructure for the distribution of alternative energies, namely hydrogen (for fuel cell electric vehicles) and electricity (for battery electric vehicles). First, we compare the main advantages/constraints of the two alternative propulsion modes for the user. The main advantages of hydrogen vehicles are autonomy and fast recharging. The main advantages of battery-powered vehicles are the lower price and the wide availability of the electricity grid. We then review the existing studies on the deployment of new hydrogen distribution networks and compare the deployment costs of hydrogen and electricity distribution networks. Finally, we conclude with some personal conclusions on the benefits of developing both modes and ideas for future studies on the subject. [ABSTRACT FROM AUTHOR]

With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers’ battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers’ en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal.