Decarbonizing transportation through electric vehicles: A life cycle perspective across China, Europe, and the USA.

ABSTRACT Future mobility options, such as electric vehicles (EVs), have been growing in popularity in recent years. EVs may improve urban climates and provide affordable and flexible mobility throughout their lifespan. They benefit society by offering zero tailpipe emissions, superior comfort, low lifespan costs and enhanced connectivity. In this study, after identifying various aspects influencing vehicle purchases, we determined the total cost of ownership (TCO) and studied the average TCO for each vehicle type: EVs, internal combustion engine vehicles (ICEVs) and hybrid electric vehicles (HEVs). Next, we employed a combination of two multi-criteria decision-making methods to rank EVs, ICEVs and HEVs across various pricing groups. The Best-Worst Method (BWM) was used to calculate the weights of each criterion, while the Technique for Order Preference by Similarity to Ideal Solution was employed to compute the ranking of the alternatives based on the BWM results. The ranking indicates that buyers should prioritize purchasing EVs, followed by HEVs, and then ICEVs, to enhance flexibility in their mobility.

Li-ion battery safety in electric buses is critically affected by rollover accidents, which can cause severe deformation of the bus superstructure. Such deformation may cause internal short circuits and trigger thermal runaway. In this context, this work investigates how different propulsion modes affect the safety of a bus superstructure under a rollover scenario in accordance with UNECE Regulation No. R66.02. This work studied an internal combustion engine (ICE) and electric propulsion systems, using the ICE chassis as the base for the electric bus. The study defines worst-case scenarios for both vehicle types, determines the center of gravity (CoG), and performs numerical simulations using the finite element method. The ICE and electric buses have CoGs of 5397.5 × 13.0 × 1617.4 and 4474.1 × 20.9 × 1682.2 mm, respectively. The ICE vehicle had a total weight of 15,836 kg and a reference energy of 5.0580 × 10 5 kg·m 2 /s 2 , while the electric vehicle had a total weight of 18,118 kg and a reference energy of 5.1145 × 10 5 kg·m 2 /s. For the rollover simulations, the unstable positions before tilting were set to 33.7° and 35° for the ICE and electric vehicles, respectively, and the models were rotated to ground contact at angular velocities of 1.75 and 1.79 rad/s. The results indicate that the ICE vehicle satisfied the regulatory requirements. However, the electric vehicle exhibited intrusion of up to 32 mm into the residual space when the same ICE structural concept and passenger configuration were applied. Therefore, some modifications were proposed, including capacity adjustments and alternative battery systems, to preserve the residual space during the rollover scenario. These results highlight the need to account for electric bus specific characteristics rather than directly converting ICE chassis designs.

Accurate estimations of fuel consumption and carbon emissions insights are critical for performance benchmarking, emissions compliance, and the optimization of energy management strategies in vehicles' systems. Unlike model-based predictive approaches that require complex modelling, machine learning (ML) predictive models learn patterns directly from data, w making them flexible, automated, and scalable solutions for complex nonlinear systems that can easily adapt to diverse sets of data with high predictive accuracy. These models typically span from linear and nonlinear models to ensemble approaches, where the latter are often preferred owing to their ability to aggregate multiple learners and more effectively capture intricate relationships.. This study develops a predictive ML framework for estimating vehicle emissions and fuel consumption in lightweight vehicles via a real-world dataset. The primary contribution of this work lies in the fusion and integration of internal combustion engine vehicle (ICEV) and plug-in hybrid vehicle (PHEV) datasets into a common modelling workflow, whereas most existing studies rely solely on combustion-vehicle datasets only. Another contribution is the dual-forecast capability of the proposed model, enabling simultaneous prediction of both vehicle emissions and fuel usage rather than solely predicting emissions, as in most prior studies. In contrast, this study offers a unified framework capable of accurately forecasting both vehicle emissions and energy consumption. The adopted broader and more diverse mixed dataset enhances generalization, in addition to making the proposed model a practical and reliable tool for environmental assessment, sustainable vehicle development, and policy decision-making.

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10-60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5-50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles' use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

Electric vehicles (EVs) are increasingly recognized as a strategy to reduce environmental impacts in urban transport. This study, conducted in 2024 in Arusha City, Tanzania, examines factors influencing EV adoption, focusing on charging infrastructure, financial constraints, and socio-economic considerations. A mixed-methods design was applied, incorporating 32 EV users, 32 internal combustion engine vehicle users, and representatives from key institutions, selected through stratified random sampling. Data were collected through structured questionnaires, semi-structured interviews, and documentary reviews. Analysis included descriptive statistics and corrected principal component analysis on standardized continuous variables, which identified charging infrastructure and financial constraints as the primary factors influencing adoption, together explaining 63% of variance. Respondents reported high satisfaction with private and workplace charging but noted limited public stations, with only two publicly accessible points in the city. Financial barriers were pronounced, as 42% of participants cited high purchase costs as a deterrent, particularly affecting lower-income groups. Despite these constraints, interest in EV adoption was strong among higher-income respondents and tourism operators. The findings provide evidence-based insights into the practical challenges and opportunities for EV integration, supporting policy development, infrastructure planning, and targeted incentives to promote sustainable mobility in similar urban contexts. Limitations include the cross-sectional design and focus on tourism-sector users, which may influence generalizability.

Battery electric vehicles (BEVs) are essential to global decarbonisation roadmaps and are being increasingly adopted in many countries. However, significant techno-economic barriers remain before the adoption of BEVs becomes widespread in the Global South. Issues include higher costs, grid instability due to high electricity demand during peak periods, lack of recharging infrastructure and restrictive driving ranges relative to internal combustion engines. Vehicle-2-Grid (V2G) can play a critical part in load balancing (peak shaving) and reducing costs for BEV owners. In this study, the potential of V2G was explored in more detail, looking at the development of appropriate hardware and software for V2G, the techno-economic assessment of V2G from a user and system perspective, and policy measures to support uptake of electric vehicles. The study shows that households with V2G-enabled BEVs achieve cost parity with households with internal combustion engine vehicles. Systems which connect BEVs to V2G, and supportive V2G metering and tariff policies, would accelerate BEV adoption in emerging markets.

Hydrogen-powered fuel cell vehicles (HFCVs) are increasingly gaining attention as a clean, sustainable alternative to fossil fuel-based internal combustion engine (ICE) vehicles and battery electric vehicles (BEVs). This work provides comprehensive insights into the latest technological advancements, current status, key challenges, and prospects of hydrogen fuel cell technology in the automotive sector. The widespread adoption of HFCVs hinges on pivotal breakthroughs in several critical areas that collectively enhance overall performance, including catalyst materials, membrane technologies, hydrogen storage solutions, system integration, and thermal management strategies. This study highlights the importance of developing innovative, costeffective non-platinum-based catalysts that provide substantial enhancement in catalytic efficiency, durability, and overall performance. Emerging hydrogen storage technologies, such as solid-state and cryo-compressed systems, show great promise in addressing the efficiency and safety limitations of conventional compressed hydrogen gas storage. The transition toward sustainable mobility through HFCVs will require coordinated efforts, including the generation of renewable electricity for green hydrogen production, supportive government policies, strategic incentives, and robust research and development initiatives aimed at enhancing fuel cell durability and infrastructure. These proactive measures are essential to overcoming the technological and infrastructural barriers and achieving a cleaner, more sustainable transportation future.

Abstract We assess the total cost of ownership (TCO) of internal combustion engine (ICEV), hybrid (HEV), plug-in hybrid (PHEV), and battery electric vehicles (BEVs) in the United States. As previous studies have shown, we find that current new BEVs, with some exceptions for smaller or shorter-range vehicles, have a higher TCO than conventional alternatives. However, we also present the first comparative analysis of the TCO of used vehicles, which make up 70% of all vehicle purchases in the U.S. We find that for used vehicles, BEVs have the lowest TCO among all powertrains. As vehicle TCO varies spatially and with use patterns, we test 5 different vehicle classes, 17 different U.S. cities, and 5 different charging strategies. The finding that BEVs have the lowest total cost for used vehicles is robust across these variables and is largely driven by vehicle depreciation patterns. We conduct a regression analysis based on 260 000 publicly available used vehicle listings, collected from January to December of 2024. We find that BEVs depreciate more rapidly than other powertrains in the first several years of vehicle life but follow similar depreciation patterns afterwards. With a 7 year ownership period, buying a used (3 year-old) midsize SUV vs a new midsize SUV has a TCO savings of approximately $3000 for an ICEV, $1000 for an HEV or PHEV, and $13 000 for a BEV. These results highlight an opportunity for savings via BEV adoption among used vehicle purchasers.

Electric vehicles (EVs) are often promoted as a solution to the impacts of transport on the climate since their GHG emissions are generally less than those of Internal Combustion Engine Vehicles (ICEVs). Considering only tail-pipe emissions, EVs are zero-emission vehicles and are being promoted as a sustainable mode. Hence, many likely believe that having no tail-pipe emissions makes EVs a robust solution to climate change. However, in the current context, EVs do not have significantly lower life-cycle GHG emissions than ICEVs. Further, EVs do not address many other externalities of vehicle use, such as health impacts or congestion. As the running costs of EVs are less than ICEVs, people would likely drive them more, which could exacerbate various externalities. This research examines the opinions of Canadians with driver’s licenses concerning such questions. It further examines how such beliefs might influence decisions to purchase an EV. That analysis details whether it would replace an ICEV or be an additional vehicle and what influences those outcomes. Hence, a survey was conducted, and an interpretable machine learning method was developed. The results suggest that 18.7% (95% confidence intervals: 17.1%–20.5%) of Canadians anticipate driving more due to the lower cost per kilometer of driving an EV. Moreover, the potential EV purchasers are more likely to drive more, which could exacerbate various externalities. Those worried about climate change are also more likely to drive more if they own EVs. The results suggest problems related to a rebound effect, where behavioral reactions could create other problems.

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection.

Standardized driving cycles often inadequately represent the driving patterns specific to a particular city, and variations in vehicle types within the same city further contribute to discrepancies in driving cycles. This study seeks to characterize the driving patterns of a specific vehicle model within a designated city and to provide robust data support for precise predictions of energy consumption and driving range. To achieve this objective, a driving cycle was developed and analyzed using real-world operational data collected from a battery electric vehicle (BEV) in Qingdao, China. The driving cycle was constructed through a process involving data preprocessing, dimensionality reduction via principal component analysis (PCA), and Improved Grey Wolf Optimizer K-means (IGWO-K-means). The analysis of energy consumption per 100 km is concluded by the study. Validation of the constructed driving cycle against the preprocessed data yielded an average relative error of 2.31%, providing a reference for the real-world driving cycle of BEVs in Qingdao, China. Furthermore, a comparative analysis of the driving cycles for BEVs in Qingdao, China, and internal combustion engine vehicles (ICEVs) in Fuzhou and Nanjing, China, revealed notable differences. This underscores the critical need for developing driving cycles that are specifically tailored to distinct cities and vehicle models. The examination of energy consumption per 100 km further corroborated the representativeness of the constructed driving cycle. Furthermore, a comparative assessment of energy consumption across varying ambient temperature ranges demonstrated that it increases as temperatures decrease.

Objectives We examined at-fault injury crashes of four passenger car populations: Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), Battery Electric Vehicles (BEVs) and traditional internal combustion engine vehicles (ICEVs). For these populations, crash rates were calculated in relation to both registration years and mileage. Finally, controlled crash rate ratios were calculated to compare the crash risk between electric vehicles (EVs) and ICEVs. Methods Studied car populations were identified and their vehicle information for the period of 2019–2023, including the mileage (76 billion kilometers for all cars during the study period), was drawn from the national Vehicular and Driver Data Register. In addition, cars in the study populations were identified from the motor liability insurance (MLI) database and the crash data for them was retrieved (11,388 motor vehicle occupant injury crashes in total). Crash rates and crash rate ratios were calculated to evaluate the crash risk of EVs. Negative binomial regression was used to model crash involvement rate ratios both per registration year and per mileage for EVs, controlling the age and gender of the vehicle owner and vehicle size. Results Only battery electric vehicles showed significantly different crash rates than ICEVs per mileage, although the result was weakly significant −15% [−28%; 0%]. There were no significant differences in crash rates per registration years. In addition, there were only a few significant differences in crash circumstances between EVs and ICEVs. On average, the motor vehicle occupant injury crash rate of ICEVs was 151 crashes per billion kilometers and 2.37 crashes per thousand registration years. Conclusions Our results indicate that, when measured by motor vehicle occupant injury crash rate, passenger cars—regardless of powertrain—have not become safer in Finland compared to the situation ten years ago. However, the current crash rate of BEVs is lower than that of ICEVs. Previous studies suggest that some of the differences in crash rate may be explained by varying usage conditions, which our findings support. Part of the difference may be explained by differences in driver populations, which should be investigated further.

ABSTRACT In the context of the global automotive market transition to more sustainable products, this study examines the mechanisms behind users' intentions to switch from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). Grounded in self‐congruity and comparative value theories, it investigates how ICE vehicle users' self‐characteristics (self‐efficacy and self‐innovativeness) influence their intention to switch to BEVs through comparative value under different levels of perceived financial switching costs. Analysis of survey data from 302 ICE users in two Chinese cities indicates that the comparative value of BEVs relative to ICE vehicles fully mediates the positive effect of both self‐efficacy and self‐innovativeness on users' BEV switching intention. Moreover, self‐efficacy strengthens the positive effect of self‐innovativeness on the comparative value of BEVs. In addition, perceived financial switching costs attenuate the effect of self‐efficacy but amplify the effect of self‐innovativeness on comparative value. These findings highlight the interactive role of consumer characteristics and market conditions in driving users switching to new eco‐friendly technologies.

Measurement data from full-scale fire experiments of battery electric vehicles and internal combustion engine vehicles

The rapid increase in electric vehicle (EV) ownership necessitates the adaptation of fuel stations to charging infrastructure and the re-evaluation of capacity planning. In the literature, demand forecasting and installation costs are mostly examined; however, station-scale queue analyses supported by field data remain limited. This study aims to examine the integration of EV charging in fuel stations through simulation-based capacity analyses, taking current conditions into account. In this context, a scenario in which one and two dual-hose pumps at a fuel station located on the Turkey–Istanbul E-5 highway side-road is converted into a charging unit has been evaluated using a discrete-event microsimulation model. The analyses were conducted using a discrete event-based microsimulation model. The simulation inputs were derived from field observations and survey data, including the hourly arrival rates of internal combustion engine vehicles (ICEVs), the dwell times at the station, and the charging durations of EVs. In the study, station capacity and service performance were evaluated under scenarios representing EV shares of 5%, 10%, and 20% within the country’s passenger vehicle fleet. Within the scope of the study, the hourly arrival rates and dwell times of ICEVs were determined through field measurements, while EV charging durations were assessed by jointly analyzing field observations and survey data. Simulation results showed that the average number of waiting vehicles increases as the EV share rises; for example, in the 10% EV share scenario, 15.6 (±0.84) EVs were observed waiting within the station, while 34.06 (±1.23) EVs were identified in the 20% scenario. These queues constrict internal circulation within the station, limiting the maneuverability of ICEVs and causing delays in overall service operations. Furthermore, when two dual-hose pumps are replaced with charging units, noticeable increases in waiting times emerge, particularly during the evening peak period. Specifically, 5.88% of ICEVs experienced queuing between 17:00–18:00, rising to 12.33% between 18:00–19:00. In conclusion, this study provides a practical and robust model for short- and medium-term capacity planning and offers data-driven, actionable insights for decision-makers during the transition of fuel stations to EV charging infrastructure.

This study evaluates the 10-year total cost of ownership (TCO) of Chiang Mai’s public Songthaew vehicles, comparing diesel internal combustion engine vehicles (DICEVs) with both converted and factory-produced battery electric vehicles (BEVs) at an annual mileage of 50,000 km. Real-world driving test data inform the analysis, which includes purchase price, financing, energy, maintenance, insurance, depreciation, and battery replacement. In Group 1 (Isuzu TFR, legacy vehicles), BEV conversions exhibited a lower TCO than diesel equivalents (1.46 vs 1.68 million Baht), primarily due to reduced energy costs, despite significant battery replacement expenses. In Group 2 (Toyota Hilux Revo vs King Long Dracon), factory BEVs showed a higher TCO (2.20 vs 1.94 million Baht), driven by higher purchase prices, depreciation, and battery costs. Sensitivity analysis indicates that a 100% subsidy for BEV conversions delivers immediate cost competitiveness, while the EV3.5 policy’s 100,000 Baht subsidy improves initial affordability but does not offset long-term battery costs. A 50% battery price reduction substantially improves BEV TCO stability and long-term competitiveness. These findings underscore those targeted subsidies for conversions, enhanced purchase incentives for new BEVs, and battery cost reduction strategies are critical for accelerating the electrification of high-usage public transport fleets in Thailand.

This paper explores the nuances of car dependence in England and Wales by identifying four distinct archetypes that span structural and conscious forms. Employing the 2011 England and Wales Census, archetype prevalence is mapped across the study area at the LSOA level, and a demographic analysis is performed. We find that while dependence exists across the study area, structural dependence is found more in rural areas, particularly the east coast of England and Wales, while conscious dependence is more prevalent in and around urban centers. The demographic makeup of each archetype differs significantly, with disability, socio-economic class, and ethnicity arising as notable significant indicators. This work highlights that an equitable transition to a sustainable transport system requires geographically and demographically specific policies tailored to the unique needs of each archetype. This transition away from car dependence, especially internal combustion engine vehicles, is imperative for a just and climate-resilient transport system.

Electric vehicles (EVs) demand highly efficient power management systems to enhance reliability and extend operational autonomy. Unlike traditional internal combustion engine (ICE) vehicles, which use alternators to supply low-voltage systems, EVs rely on DC-DC converters, commonly referred to as Auxiliary Power Modules (APMs). This paper presents the analysis, design, and experimental validation of a half-bridge converter with a current-doubler (HB-CD) topology tailored for APM applications. A generalized structure is proposed to support the parallel operation of multiple modules, aiming to mitigate current stress and reduce output ripple. A 3~kW laboratory prototype was implemented using two 1.5 kW modules operating in parallel, enabling interleaved operation and effective current sharing. Experimental results demonstrate the converter’s ability to maintain high efficiency, reaching up to 94.6 % under nominal conditions and 95.9 % under minimum input voltage operation, confirming the feasibility of the proposed topology for high-current automotive applications.

Abstract India’s automotive industry is undergoing a fundamental transformation driven by the rapid emergence of electric vehicles (EVs). The growing penetration of EVs is increasingly influencing the demand, production, and strategic orientation of conventional internal combustion engine (ICE) vehicles. This study examines the impact of electric vehicles on conventional vehicles in India by analysing adoption trends, segment-wise displacement, economic implications, environmental outcomes, and the role of government policy interventions. Using secondary data from industry reports, policy documents, and academic literature, the study observes that EV penetration reached approximately 7.8% by FY2025, with significant disruption in the two-wheeler and three-wheeler segments. The transition is being supported by incentives, lower operating costs, and emerging charging networks, while it is constrained by infrastructure gaps, battery supply chain dependence, and uncertainty in subsidy continuity. Overall, the findings suggest that EVs are not fully replacing conventional vehicles at present, but they are steadily reshaping market dynamics, competitive strategies, and consumer preferences. With continued policy support and infrastructure expansion, EVs are projected to account for nearly 20–30% of total vehicle sales by 2030, indicating a gradual but decisive shift in India’s mobility ecosystem. Keywords: Electric vehicles; conventional vehicles; internal combustion engines; EV adoption in India; market displacement; charging infrastructure; sustainable mobility.