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.

This study aims to describe the implementation of the Toulmin argument pattern in teaching argumentative writing to eleventh-grade students at SMK Negeri 9 Muaro Jambi. This research employed a qualitative approach with an intrinsic case study design. The data were collected through observation, interviews, and documentation. The sources of data included one Indonesian language teacher and 27 students of class XI in the Light Vehicle Engineering program. The data were analyzed descriptively through the stages of data organization, reduction, categorization, interpretation, and narrative presentation. The results showed that the implementation of the Toulmin argument pattern had a positive impact on students’ ability to write argumentative texts. The learning process became more structured and systematic as students were guided to construct the elements of argument, namely claim, data, warrant, backing, qualifier, and rebuttal. Based on the analysis of students’ argumentative texts, 80% of the students were categorized as very good and 20% as good. These findings indicate that the Toulmin argument pattern is effective in improving the structure of arguments as well as students’ logical and critical thinking skills in argumentative writing.

As a core functional component of multi-degree-of-freedom precision motion mechanisms, spherical hinges are widely used in high-end equipment fields such as industrial robots, vehicle engineering, and intelligent manufacturing. Their dynamic performance directly determines the motion accuracy and the level of intelligent control of the equipment. The high-precision real-time measurement of two-degree-of-freedom (2-DOF) angles is a key prerequisite for achieving precise closed-loop control of spherical hinges. However, due to the strong coupling characteristics between the 2-DOF angle signals, it is difficult to directly and accurately measure the angular motion parameters of spherical hinges, which has become a core technical bottleneck restricting the improvement in their application efficiency. To address this challenge, this paper presents an improved study of the previously proposed spherical differential quadrature capacitance sensor for measuring the 2-DOF angle signals of spherical hinges. Firstly, the 2-DOF angle signal decoupling model is reconstructed and optimized. Secondly, a real-time decoupling circuit architecture for phase-shift detection with single-frequency signal excitation is innovatively proposed. This solution effectively addresses the incomplete decoupling of 2-DOF angle signals in previous studies, as well as the problems of considerable measurement noise, low resolution, and high calibration difficulty caused by random amplitude and phase errors in the excitation signals. Through the construction of an experimental platform for verification tests, the results show that the proposed scheme can significantly suppress the random errors caused by the parameter dispersion of the device, achieve an angle measurement resolution of 0.001°, and simultaneously considerably reduce the complexity of system calibration, laying a key technical foundation for the engineering application of spherical hinges in the fields of precision measurement and high-performance control.

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 developed and evaluated a STEM-based digital learning module on the Anti-lock braking system (ABS) for Technical and Vocational Education and Training (TVET), implemented in a Light Vehicle Engineering (LVE) program. This pilot study uses a Research and Development (R&D) approach guided by the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Prior to implementation, expert review was conducted to establish media and content quality. Media experts rated the module at 86.95%, highlighting strengths in interface design, software, and usefulness. Content experts rated the module at 79.68%, indicating satisfactory conceptual accuracy and pedagogical usefulness for supporting automotive competency learning, while identifying curricular alignment as the primary area requiring further improvement. The module was then piloted with 35 vocational students using a one-group pre-post test design. Learning outcomes demonstrated a statistically significant improvement in ABS knowledge, with the average score increasing from 51.49 on the pre-test to 71.81 on the post-test. The average gain was 20.32 points, representing a 39.46% improvement relative to the pre-test mean, with a very large effect size (Cohen's d = 3.67). Assuming a maximum score of 100, the normalized gain was 0.42, indicating a moderate learning gain. Overall, these results provide quantitative evidence that a STEM-oriented digital ABS module can enhance learning outcomes and support structured, interactive, and industry-relevant instruction in vocational automotive education.

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.

This work presents the development of a measuring instrument capable of assessing the possible presence of critical permanent deformations on the connecting rod in hybrid cars equipped with gasoline-powered internal combustion engines. The permanent deformation can be due to incorrect fueling and cause a progressive engine failure through the breaking of one or more connecting rods. The measuring tool developed is a non-invasive, low-cost system and permits the detection of the incipient damage without dismantling the engine, thus assuring a time-saving approach. The instrument is composed of a mechanical system and an electronic interface that permits easy use during measuring operations and the possibility to store the data collected. An experimental campaign was implemented to validate the measurement system’s capability to detect this type of damage and to determine a threshold beyond which it is necessary to proceed with the replacement of connecting rods. The results show the optimal ability to differentiate between usual technological variability of the piston stroke and the range that can be connected to the anomaly studied. The system is also able to permit the measurement of a whole engine in less than 20 min.

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles.

The application of condensed aerosol (CA) as a fire suppression system for motor vehicles has increasingly been promoted as an alternative to conventional streaming agents. However, such equivalence raises fundamental conceptual problems. Streaming agents suppress fire through high-momentum, directional discharge enabling rapid localized fire attack, whereas condensed aerosol operates through volumetric dispersion of submicron particles and is highly dependent on spatial confinement and the achievement of homogeneous agent concentration. This study aims to scientifically analyze the inherent inability of condensed aerosol to function as a streaming agent within vehicle engine compartments, which are semi-open and characterized by high ventilation rates. The research adopts an analytical–qualitative approach integrating particle fluid mechanics, thermodynamics of pyrotechnic aerosol systems, combustion reaction kinetics, and a systematic review of international standards including NFPA 2010, ISO 15779, and LPS regulations. The analysis is reinforced by a synthesis of Scopus-indexed experimental studies, Computational Fluid Dynamics (CFD) simulations, and reviews of real-world fire incident cases. The results demonstrate that condensed aerosol intrinsically lacks flow momentum, throw range, and penetration capability required for effective vehicle fire suppression. Its performance is deterministically degraded by ventilation effects, making suppression failure scientifically predictable. Conversely, condensed aerosol exhibits effective performance only in small, well-confined, and static compartments such as electrical panels and control cabinets, where a total flooding mechanism based on volumetric concentration can be consistently achieved. These findings are fully consistent with international regulatory classifications that restrict condensed aerosol to fixed total flooding systems for small enclosed spaces. This study concludes that the application of condensed aerosol in motor vehicles represents a categorical design error in fire suppression systems.

Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented to minimize the probability of this type of accident occurring. The support for this paper was based on the study of an accident that occurred in Portugal involving a passenger bus that suffered a fire in the engine compartment, which spread to the passenger compartment and caused the destruction of the vehicle, with no personal injuries. This study used infrared image analysis technology, oil ignition temperature analysis, maintenance history, accident history and operator interviews to determine the possible cause of the ignition. It was found that the cause was due to oil leaks from the engine compartment cooling system. The present communication will share a set of explanatory elements of the circumstances in which the accident occurred. In addition to identifying the causes of the accident, the study warns of the importance of more effective and efficient maintenance, particularly when using Condition Based Maintenance (CBM), including periodic visual inspections of the various mechanical and electrical components that make up the vehicles. The conclusions presented in the study also show that these events are not unrelated to the poor or even non-existent maintenance policy for the entire fleet, including the applicable standards.

This study examines the use of mind mapping in a collaborative learning model to strengthen Link-and-Match (8+i) partnerships in a Center of Excellence vocational school. Using a qualitative case study approach, data were collected through observation, in-depth interviews, and document analysis involving school leaders and productive teachers in the Light Vehicle Engineering program at SMK Bhakti Loa Janan, Kutai Kartanegara, Indonesia. The findings show that mind mapping functions as an analytical and collaborative tool to visualize key partnership components, including curriculum alignment, guest teachers, internships (PKL), competency certification, teaching factory, and graduate absorption. The mapped analysis reveals that while partnerships with industry are established through MoUs and several programs have been implemented, gaps remain in the consistent application of project-based learning (PjBL), teaching factory utilization, technology updates, and teacher capacity building. Mind mapping supports stakeholders in identifying implementation gaps and prioritizing improvements to enhance the sustainability and effectiveness of industry collaboration in vocational education.

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.

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.

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.