Search Results (127)

Background: Road transport accounts for 73% of transport-related greenhouse gas emissions within the EU, 27% of which are attributable to heavy-duty vehicles. In order to reduce emissions in the area of heavy-duty commercial vehicles, electrifying the fleet offers a perspective. As part of a cooperation between TU Berlin, Siemens and BLG Logistics within the Mobility2Grid research campus, an analysis was carried out to determine how an exemplary BLG depot for regional logistics transport with six diesel trucks can be converted to battery–electric trucks. Methods: This analysis was conducted under a fixed depot schedule with defined dwell times and charging opportunities, with the aim of developing practical recommendations for the acquisition of suitable vehicles and infrastructure. To this end, simulations were carried out using the eFlips consumption and depot simulation software developed at TU Berlin. Results and Conclusions The results show that electrification for regional logistics transport can already be fully implemented with the current state of the art technology and that neither very large batteries nor very high charging powers are required for technically feasible and economically balanced operation. Notably, the cost-optimal battery capacities identified (approximately 200–230 kWh) are currently smaller than those of commercially available 40 t electric trucks, revealing a gap between the model-optimal configuration and present market offerings. Based on the identified optimal configuration, a life cycle assessment (LCA) is conducted to evaluate the environmental impact of fleet electrification. Over a 10-year lifetime, the battery–electric fleet reduces cumulative greenhouse gas emissions by approximately 53% compared to the diesel baseline, with operational-phase savings clearly outweighing higher production-related emissions. The combined techno-economic and environmental assessment provides a structured decision basis for depot-centered fleet electrification. Full article

Technological developments in transportation have moved quickly, often faster than the frameworks used to evaluate their broader societal and environmental implications. This study examines the extent to which Tesla’s automotive activities contribute to long-term societal well-being from a consequentialist utilitarian perspective, focusing on two related developments: (1) vehicles equipped with advanced driver assistance systems (ADAS) and (2) vehicles powered by electricity. Tesla provides a useful focal case in that it brings these two developments together in a single, highly visible setting. Using Tesla as an exploratory qualitative case, the analysis assesses both technologies within a single ethical and sustainability framework, examining how their effects combine across safety, environmental, and broader societal outcomes. Because the two technologies act on many of the same outcomes and stakeholders, they interact: they reinforce one another in some respects and offset one another in others. In the case of road safety, for example, the additional mass of an electric vehicle raises the severity of collisions even as driver assistance works to reduce their frequency. The analysis suggests an overall net positive societal impact, while recognizing the uncertainties and trade-offs that remain. This assessment rests mainly on two considerations: the likely reduction in traffic-related injuries and fatalities associated with wider adoption of ADAS-equipped vehicles, and the expectation that, in most contexts, electric vehicles provide a net environmental benefit, particularly through lower levels of harmful air pollutants relative to internal combustion engines. These benefits are not automatic, however, but depend on broader system conditions, including whether electrification and automation move transportation beyond established patterns of car dependence or reinforce them. The paper concludes by outlining the implications of these findings, while acknowledging the limits of the analysis and pointing to areas for future research. Full article

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions. Full article

Road transport is an important source of carbon emissions worldwide, yet the factors driving these emissions may differ under varying geographical conditions. Plateau regions are characterized by high altitude and strong spatial constraints, but their transport carbon emission mechanism remains insufficiently understood. Taking Qinghai Province on the Qinghai–Tibetan Plateau as a case, this study estimates road transport CO₂ emissions from 2003 to 2022 using annual statistical data. It constructs a multidimensional indicator system covering economic development, industrial structure, energy use, and road transport activity, and applies correlation analysis, PCA, and LASSO regression to diagnose variable relationships and identify key drivers. The results show that GDPI, VATSP, CVO, and TPT have stable positive effects on road transport CO₂ emissions, indicating that economic expansion, transport services, vehicle ownership, and passenger mobility are the dominant drivers. Industrial and energy-related variables have more indirect and stage-dependent effects: NMI and NFMO are negatively associated with emissions, whereas NMEC has a weak positive effect. These findings suggest that, under the dispersed spatial development and long-distance transport dependence of plateau regions, emissions are more directly shaped by economic and transport activity than by short-term changes in energy structure. Low-carbon transport policy in Qinghai should therefore combine transport-demand management, more efficient transport organization, public-transport improvement, and gradual transport electrification. The results provide evidence for emission-reduction strategies in high-altitude and ecologically fragile regions. Full article

The transition to electric bus (EB) fleets is a critical step towards sustainable urban transportation, offering substantial reductions in greenhouse gas and pollutant emissions relative to diesel buses. However, transit authorities face multifaceted challenges in this transition, including limited driving ranges of EBs, the need for widespread charging infrastructure, and potential strain on the electric grid, alongside opportunities such as governmental subsidies and increased fare revenues. This paper proposes a comprehensive multi-period mixed-integer programming model seeking to optimize long-term EB fleet transition plans in urban contexts while jointly accounting for all inherent financial, technical, and operational factors impacting such a transition. The model is operationalized using real data acquired from Dubai’s Roads & Transport Authority (RTA), encompassing 71 bus routes and a 25-year planning horizon to meet a 100% electrification target by 2050. A scenario-based analysis evaluates the robustness of the transition plans under variations in key operational parameters. The results illustrate that optimized long-term planning yields substantial cost savings and emissions reductions, where the incorporation of environmental and social externalities and revenue shifts causes profit maximization to emerge as a more appropriate objective. In addition, it turns out that adequate dwell time is crucial for cost containment and full fleet electrification feasibility. While RTA targets 100% electrification by 2050, the base case is deliberately relaxed to 90% as certain routes, notably double-decker lines, are incompatible with currently available EB configurations. Nevertheless, full electrification is restored under the minimum dwell scenario. Also, a policy of purchasing only EBs accelerates full fleet electrification by roughly a decade with only a marginal increase in total cost, unlike imposing strict interim electrification targets. The optimized transition plans provide actionable insights for transit authorities balancing economic efficiency with sustainability goals. Full article

Although critical to modern logistics, heavy-duty commercial vehicles face mounting pressure to improve energy efficiency and reduce emissions. The aim of this study was to evaluate the techno-economic and environmental performance of four vehicle configurations: internal combustion engine (ICE) tractors and battery electric tractors (BETs), each respectively paired with either a conventional or an electrified trailer. To optimize energy utilization while proactively mitigating the longitudinal impact risks that trigger vehicle instability, a coordinated control strategy based on power decoupling and a real-time, efficiency-oriented torque distribution strategy were designed. Simulations under C-WTVC and CHTC-TT cycles revealed that electrified trailers substantially improved the system efficiency. Under fully loaded conditions, BETs paired with electrified trailers reduced the direct energy expenditures by 76.5% compared to conventional ICE vehicles. Notably, compared to pure electric tractors with conventional trailers, the addition of electrified trailers further reduced the energy consumption by 29.1%. Meanwhile, ICE tractors paired with electrified trailers achieved a 35.6% energy cost reduction. Furthermore, a fuel-cycle well-to-wheels (WTW) assessment of the use phase, based on a specified regional grid emission factor, demonstrated that the BETs and hybrid configurations reduced the operational greenhouse gas emissions by 64.9% and 29.3%, respectively, compared to the baseline. These findings indicate that trailer electrification offers consistent economic and environmental benefits under the simulated scenarios, thereby providing a robust theoretical foundation for the low-carbon transition, transportation sustainability, and selection of sustainable technologies in road freight. Full article

Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO₂ emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems. Full article

This paper examines the evolution of urban transport policy in Saudi Arabia from a car-dependent paradigm toward sustainability-oriented planning and early implementation between 2000 and 2025. Using a longitudinal qualitative analysis of national strategies, municipal plans, and giga-project documents, this study traces shifts in policy discourse, governance arrangements, and delivery evidence across three phases: an expansionist phase (2000–2015), a vision transition phase (2016–2020), and a sustainability implementation phase (2021–2025). These phases were selected to capture the transition from pre-Vision 2030 automobile-oriented planning to the early implementation of sustainability-oriented transportation reforms. The findings reveal a clear transition from road-expansion-oriented planning—characterized by highway development, fuel subsidies, and limited public transport—toward system performance, decarbonization, and multimodal integration. Recent years have seen the rollout of metro and bus networks, expansion of rail systems, early electrification of vehicles and public transport, and fuel price rationalization. However, persistent behavioral lock-in, low-density urban forms, climatic constraints, and complex multi-level governance arrangements continue to limit modal shift and equitable mobility outcomes. The findings suggest that infrastructure investment alone cannot achieve substantial modal shift without integrated land-use planning, feeder systems, and demand-management measures. By linking policy ambition to implementation pathways over time, this study provides transferable insights for sustainable mobility transitions in oil-dependent and arid urban contexts. Full article

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation. Full article

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and hydrogen fuel cell trucks. Combined with the LSTM neural network and vehicle ownership model, this study predicts the fleet emission reduction potential from 2020 to 2050. The results show that all new energy trucks can achieve TCO parity with diesel trucks before 2050, and electrification shows better economic competitiveness than hydrogen fuel cell technology across all vehicle types in the Chinese context. Fuel cell trucks powered via solar-powered water electrolysis exhibit the lowest carbon intensity, and grid decarbonization can significantly improve the emission reduction effects of electric and fuel cell trucks. Freight fleet carbon emissions are expected to peak around 2030. In an ideal scenario, emission reductions of 19.5%, 41.9%, and 82.9% can be achieved by 2030, 2040, and 2050, respectively. Heavy-duty trucks are the main emission contributors (47–58%) and the main target of emission reduction strategies. Short-term reduction depends on fuel economy, while long-term reduction prioritizes new energy substitution. Policy recommendations include promoting alternative fuel trucks, upgrading emission standards, and adopting differential taxation. Full article

The rapid electrification of road transport has increased interest in distributed energy strategies that reduce grid demand and support decarbonization. Vehicle-integrated photovoltaics (VIPV), including vehicle-applied photovoltaic configurations (VAPV), can generate electricity directly on the vehicle. This systematic review examines peer-reviewed VIPV literature published between 2015 and 2026, focusing on the distinction between theoretical photovoltaic generation and practically usable energy. A Scopus search conducted on 2 May 2026 identified 196 records, of which 88 studies were included after screening against predefined criteria. Due to heterogeneity in vehicle types, climates, technologies, modeling assumptions, and reported metrics, no meta-analysis was performed. Instead, the review applies a multi-layered framework covering climate, geometry, thermal effects, electrical mismatch, battery state-of-charge interactions, fleet-scale modeling, economics, and life-cycle implications. The evidence shows that VIPV is technically feasible and can deliver measurable energy yields, especially in high-irradiance regions and vehicles with favorable daytime parking exposure. However, useful contribution depends strongly on curvature losses, dynamic shading, electrical configuration, SOC limits, charging behavior, seasonality, and vehicle energy demand. Therefore, VIPV is best understood as a context-dependent supplementary energy strategy rather than a transformative standalone solution. Its strongest value lies in specific vehicle classes, climates, and usage patterns where on-board generation can reduce charging demand, support operational resilience, or improve distributed self-consumption. The review also proposes minimum reporting requirements for future studies, including annual energy yield, Wh/km contribution, PV area or capacity, mileage assumptions, SOC modeling, and curtailment treatment. The review was not formally registered, and no formal risk-of-bias or certainty assessment was applied. Full article

The transition to low-emission road transport in Europe depends not only on the growth of plug-in electric vehicle (PEV) uptake, but also on the timely expansion of publicly accessible charging infrastructure. This article provides a descriptive and diagnostic assessment of the relationship between electrification dynamics and public charging infrastructure development in Europe. The analysis combines a long-run descriptive window (2015–2024, with 2025 treated separately as a scenario observation) and a core diagnostic window (2020–2024) for which a consistent proxy of potential infrastructure strain—plug-in vehicles per public recharging point (VPP)—is available. The results show a strong increase in PEV share in new registrations, from 1.0% in 2015 to 20.92% in 2024, while the number of public recharging points rose from 67,064 to 900,000 over the same period. In the core sample, VPP declined from 15.24 in 2020 to 13.92 in 2024, which is consistent with a catch-up phase in infrastructure deployment after 2021. At the same time, the short-window relationship between PEV share, infrastructure scale and average CO₂ emissions of newly registered cars remains weak and unstable, indicating the role of additional structural factors. The article contributes a transparent, replicable indicator-based framework for describing infrastructure strain in aggregate European data. In policy terms, the findings support a shift from simple point-count targets toward functionally and spatially differentiated infrastructure planning, including interoperability, power structure, and accessibility in underserved areas. Full article

This paper presents the toolset, methodology and procedure followed to create a dataset from battery electric vehicle trajectories, called DEVRT—Dataset of Electric Vehicle Real Trips. Understanding the behaviour of electric vehicles and their battery consumption under real-life conditions and journeys is required in the shift towards the electrification of transport of people and goods. This paper aims to contribute with the provision of real measurements in different types of routes and environmental contexts at the time of driving to support data analytics and modelling techniques, essential for extracting actionable insights from electric vehicle battery consumption. The preparation, on-route and post-processing steps of the followed methodology are depicted. The outcome dataset consists of probe data collected over 4 days following heterogeneous routes performed by four different drivers using two electric vehicles (one more suitable to city usage and the other one more suitable for longer trips). This probe data is complemented with associated road network characterisation information, traffic flow measurements and weather extracted from auxiliary data sources. The paper presents a comprehensive description of the geographical characteristics of the trajectories, qualitative and quantitative characterisation of planned routes to create these trajectories, and criteria used to select them. Full article

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO₂-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis. Full article