Search Results (525)

Against the background of the global low-carbon transformation, battery charging and battery swap (BaaS) have become two mainstream energy replenishment modes for battery electric vehicles (BEVs). Existing studies lack comparative Stackelberg game analysis covering fuel vehicle manufacturers, BEV manufacturers, and independent battery operators under heterogeneous consumer preferences including purchase price sensitivity, mileage cost perception, and vehicle depreciation attention. This study establishes a tripartite game framework covering three industrial scenarios: independent charging operation, third-party exclusive battery-swap R&D, and vehicle battery joint swap station construction. By solving closed-form equilibrium solutions and conducting parameter comparative statics plus numerical surface simulation, this paper systematically identifies how multi-dimensional consumer preferences affect product pricing, market demand, battery service level, and supply chain profit distribution. The results indicate that under the given baseline parameter settings, battery swap mode yields higher vehicle prices but lower market demand relative to charging mode; joint R&D only achieves bilateral profit win–win when consumers attach high importance to driving cost and automakers undertake low cost-sharing ratios. This study expands the theoretical framework of BEV energy replenishment supply chain games and provides quantitative decision references for vehicle and battery enterprises to select optimal operational cooperation paths. Full article

High-frequency operational records from new energy vehicles (NEVs) are increasingly used to support data-driven sustainable mobility applications, including condition monitoring, energy management, and battery-state estimation. In practice, these records are often incomplete because of sensor faults, communication dropouts, and rapidly changing operating environments, which can distort downstream analyses and reduce the reliability of vehicle-state assessment. This study proposes MDCformer, a multi-period nonstationary modeling framework for reconstructing missing vehicle-speed measurements from multivariate NEV operational time series. MDCformer integrates timestamp-derived temporal descriptors, convolution-enhanced self-attention, and de-stationary attention modulation. The temporal descriptors provide explicit calendar context, the convolutional attention module strengthens local signal consistency before global dependency modeling, and the de-stationary module reintroduces time-varying statistical cues that may be suppressed by normalization. Because the battery electric vehicle (BEV) and fuel cell vehicle (FCV) datasets used in this study cover approximately 18 days and 2.6 days, respectively, the empirical evidence mainly supports daily and short-horizon periodic cues; longer-cycle descriptors are retained as extensible components for longer fleet-level records. Experiments on two real-world NEV datasets show that MDCformer consistently outperforms representative deep-learning baselines under missing rates from 10% to 50%. At a 10% missing rate, compared with the vanilla Transformer baseline, MDCformer reduces root mean square error (RMSE) and mean absolute error (MAE) by 11.35% and 19.00% on the BEV dataset and by 41.41% and 54.83% on the FCV dataset, respectively. Additional scenario-specific tests and downstream state-of-charge prediction further indicate that the reconstructed data preserve more useful temporal structure for sustainable intelligent-transportation analytics. These findings demonstrate the potential of reliable data reconstruction for improving the robustness of intelligent vehicle monitoring and supporting data-driven sustainable transportation applications. Full article

Taxi fleets operate intensively, accumulate high annual mileage, and contribute disproportionately to urban greenhouse gas emissions, making them attractive candidates for transport electrification. Despite growing interest in electric mobility, integrated evaluations of the environmental, energy, and economic implications of taxi fleet electrification remain limited, particularly for medium-sized cities in Türkiye. This research examines the replacement of the commercial taxi fleet operating in the central districts of Denizli with battery electric vehicles (BEVs) through a framework that integrates operational emission estimation, electricity demand, charging infrastructure, and life-cycle cost analysis (LCCA). Operational emissions were quantified using the IPCC Tier 1 fuel-based approach together with a distance-based consistency check. Under the adopted assumptions, electrification reduced annual operational CO₂ emissions by 4217.48 tCO₂ (57.9%). The electrified fleet required 18.36 MWh of electricity per day (6.70 GWh annually), while estimated peak charging demand varied between 1.22 MW and 5.57 MW, depending on the charging strategy. Economic evaluation showed a positive net present value (NPV) of 1.32 million TL per vehicle, an internal rate of return (IRR) of 80.86%, and a discounted payback period (DPP) of 1.37 years. Although profitability varied with energy prices, vehicle costs, and annual mileage, fleet electrification remained economically feasible across all scenarios considered. These results suggest that electrifying commercial taxi fleets can support urban decarbonization while remaining financially attractive when accompanied by appropriate charging infrastructure and coordinated transport planning. Full article

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg₃), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

Ride quality—encompassing vehicle comfort, vibration isolation, and noise, vibration, and harshness (NVH)—has become a key competitive differentiator in modern automobiles. This paper presents a comprehensive update to the foundational literature on passenger car ride quality, capturing the rapidly growing literature and new technological paradigms that have emerged over the past decade. Established approaches to human vibration response, vehicle dynamics modelling, and road surface characterisation are examined within the ISO 2631 framework. This review critically surveys advances driven by battery electric vehicle (BEV) powertrains—where the absence of internal combustion engine noise unmasks motor whine, inverter switching noise, and tyre–road excitation, lowering the perceptual ride–NVH boundary from ~25 Hz toward 15–18 Hz—as well as intelligent semi-active and active suspension technologies, deep reinforcement learning for suspension control, machine learning for ride quality prediction, and connected vehicle infrastructure enabling predictive preview control. Key research gaps are identified: the absence of validated ISO 2631 frequency weightings for autonomous vehicle postures, the lack of standardised open benchmark datasets for cross-study comparison, and the unresolved sim-to-real validation gap for data-driven suspension controllers. Ten priority research directions are proposed for the coming decade. Full article

Battery electric vehicle (BEV) fires in road tunnels can intensify thermal, smoke transport, visibility, and CO exposure hazards under confined ventilation. This study evaluated the combined mitigation performance of water mist and semi-transverse mechanical ventilation. A three-dimensional PyroSim/FDS model of a 200 m × 10 m × 5 m tunnel was established with a 7 MW BEV design fire at the midpoint. The prescribed-source model was assessed against a reduced-scale lithium-ion battery tunnel experiment; at the representative monitoring location, the simulated temperature history reproduced the main trend, with deviations of approximately 7% and 10% at the first and second peaks. Thirty-six coupled cases examined ventilation mode, nominal opening velocity, nozzle arrangement and spacing, flow rate input, droplet diameter, and spray cone angle. Supply ventilation improved hot-smoke-layer cooling and visibility, whereas exhaust ventilation more effectively reduced the local CO volume fraction. Under the baseline weighting scheme, the highest-ranked case reduced the peak local ceiling-region and near-fire gas temperatures by 77.8% and 82.2%, increased average visibility during 200–500 s by 42.9%, and achieved a comprehensive relative mitigation index (CRMI) of 56.6%. Two supplementary nominal 10 MW simulations showed that this case retained substantial thermal control, reducing the two peak temperatures by 65.7% and 74.1%, but did not improve local visibility or CO. Thus, the thermal-mitigation trend persisted at the higher nominal input, whereas the full multi-hazard ranking was not transferable across fire sizes. Full article

This study investigates the performance, energy flow, efficiency, and environmental impact of a C-segment battery electric vehicle (BEV). As BEVs are increasingly considered a sustainable alternative to conventional internal combustion engine vehicles, a detailed understanding of their energy utilization and operational emissions is essential. A MATLAB/Simulink-based vehicle model incorporating an 88.5 kWh battery pack, a 160 kW permanent magnet synchronous motor (PMSM), regenerative braking, and longitudinal vehicle dynamics was developed. The developed model was validated by comparing the simulated vehicle performance characteristics with the publicly available specifications and performance data of the reference TOGG T10F vehicle. The vehicle was evaluated under the WLTP Class 3 driving cycle, while the effects of aggressive and high-speed driving conditions were further investigated using the US06 and Artemis Motorway 150 cycles. The results indicate a net vehicle energy consumption of 136.4 Wh/km and a driving range of 623 km under WLTP conditions. The PMSM achieved average efficiencies of 93.4% in traction mode and 92.7% in regenerative braking mode, while the cumulative battery-to-wheel drivetrain efficiency reached 81.5%. In addition, approximately 19.9% of the consumed energy was recovered through regenerative braking. Vehicle emissions were also assessed using different electricity generation mixes based on the rated energy consumption, including charging losses, yielding operational emissions between 27.7 and 116.0 gCO₂e/km. The findings demonstrate that the developed model provides realistic performance predictions and confirm the potential of BEVs to achieve high efficiency and substantially lower emissions than conventional passenger vehicles. Full article

This study quantifies whether vehicle electrification reduces greenhouse gas emissions on one of the world’s most fossil-intensive electricity grids. A transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment compares a mid-size battery electric vehicle (BEV, 60 kWh) with a comparable gasoline car over 225,000 km, using a fully source-traceable process-sum inventory and life cycle (well-to-wheel) emission factors for both energy carriers. On the 2024 Saudi grid (692 g CO₂e/kWh, 99.8% fossil) the BEV emits 37.8 t CO₂e (168 g CO₂e/km) against the gasoline car’s 50.6 t (225 g CO₂e/km)—a 25% reduction, with the BEV’s 1.9 times higher production emissions repaid at 76,000 km, approximately three years of typical Saudi driving. The advantage rises to 44% on the world-average grid, 53% under Saudi Arabia’s 50% renewable-electricity target for 2030, and 66–80% on the EU and French grids; grid parity would require 991 g CO₂e/kWh, above any national grid. The result is robust to hot climate energy consumption (+15%, advantage 25%), Gulf-sourced materials (break-even shortens to 68,000 km), battery capacity (40–80 kWh), and 10,000-run Monte Carlo uncertainty propagation (BEV superior in 99.6% of draws). Electrification is therefore a sound climate strategy even in fossil-grid economies, and its benefit roughly doubles with the announced power-sector transition. Full article

The increasing number of new battery electric vehicle (BEV) registrations worldwide and the development of advanced batteries with higher energy density and pack capacity have contributed to large volumes of batteries approaching the end of their first service life. The retired batteries may be repurposed in second-life applications or recycled. The degradation profile of BEVs is a critical determinant in second-life potential of the retired batteries. This study presents a comprehensive analysis of calendar and cycle mechanisms in lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC) batteries across 37 commercially available BEV models in the European Union (EU) market. Three scenarios are considered, based on the operational temperature—Scenario I with a 273.15 K, Scenario II with a 298.15 K, and Scenario III with a 318.15 K operational temperature—and two degradation metrics are determined for each analyzed BEV: state-of-health (SoH) and remaining capacity at end-of-life (EoL). The results show that the SoH of both chemistries is highly dependent on the state-of-charge (SoC), temperature, depth of discharge (DoD), and real usable capacity. Moreover, the Tesla Model 3–Premium RWD, Volkswagen ID.3, ID.4, and ID5–GTX, and the Tesla Model Y–Premium AWD all exhibit a remaining capacity between 40 and 77 kWh at EoL, depending on degradation profile, making them a viable option for second-life applications. Full article

The decarbonisation of the automotive sector requires product innovation, circular end-of-life management and energy-system transformation to be treated as interdependent strategic choices. This study proposes a decision-oriented life cycle assessment (LCA) framework for evaluating sustainable product innovation in sport utility vehicles (SUVs), focusing on how powertrain selection and post-consumer management support the low-carbon transition. Six SUV powertrain technologies—petrol, diesel and CNG internal combustion engine vehicles (ICEVs), petrol plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)—were assessed for 2025–2050 using ReCiPe 2016, IPCC 2021, Cumulative Energy Demand, CML-IA and Ecological Scarcity 2021. Landfilling and recycling scenarios were combined with fuel- and energy-cycle modelling, including well-to-tank (WTT) and tank-to-wheel (TTW) emissions and a Paris Agreement-compatible 2050 pathway. Recycling generally outperformed landfilling, reducing greenhouse gas emissions by 26–35%, cumulative energy demand by 28–59%, carcinogenic air emissions by 27–43% and heavy-metal impacts on soil by 62–80%, although eutrophication revealed category-specific trade-offs. BEV and FCEV configurations were particularly sensitive to material recovery and energy-supply decarbonisation, whereas ICEV impacts remained dominated by fuel use. The findings show that sustainable SUV design requires strategic alignment of product architecture, circular supply chains, recycling technologies and low-carbon energy policy. Full article

The rapid expansion of electric mobility is reshaping both transport infrastructure and power-system operation. This narrative and critical review examines the connected evolution of new energy vehicle (NEV) markets, charging and battery-swapping infrastructure, policy mechanisms, and vehicle-to-grid (V2G) systems. In this paper, NEV includes battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel-cell electric vehicles (FCEVs); conventional non-plug-in hybrid electric vehicles are discussed only where regional statistics require clarification. Peer-reviewed studies, official statistics, policy documents, market reports, and technical standards available through June 2026 are synthesized thematically and compared across China, Europe, the United States, and selected emerging markets. The review distinguishes verified 2025 observations from scenario-based projections, evaluates policy instruments by their outcomes and limitations, and extends the V2G discussion to bidirectional charger requirements, interoperability, aggregation, DSO-TSO coordination, battery degradation, cybersecurity, and economic viability. Unlike reviews centered on a single technology or region, the proposed market–infrastructure–policy–V2G framework explains how market structure, infrastructure governance, standards, and electricity-market design jointly shape commercialization pathways. The synthesis indicates that infrastructure scale alone is insufficient: utilization, grid hosting capacity, interoperable communication, credible revenue stacking, and equitable access determine whether charging, battery swapping, and V2G can deliver system-level value. Full article

Ukraine’s parliament is considering a monthly ownership levy on battery-electric vehicles (BEVs) of up to UAH 4000 as a road-fund replacement following the reinstatement of 20% value-added tax on BEV imports from January 2026, a fiscal reversal of the sustained incentive framework (zero customs duty since 2015; full VAT and excise relief from 2018) that has underpinned a fleet of approximately 246,000 registered BEVs in a wartime economy with acute petroleum import dependence. A five-year total-cost-of-ownership (TCO) model calibrated to Ukrainian market data (April 2026) computes the breakeven monthly levy across three charging scenarios and two energy-price assumptions; a structured comparative policy analysis maps the instrument against cross-jurisdictional adoption-suppression evidence; and a legal doctrinal analysis applies the fair-balance test under Article 1 of Protocol No. 1 to the European Convention on Human Rights. The breakeven levy (L*) ranges from UAH 931 to UAH 1626 per month under baseline conditions and from UAH 63 to UAH 862 under adverse energy-price assumptions, placing the proposed UAH 4000 rate at 2.5 to 4.3 times the threshold. Cross-jurisdictional evidence positions Ukraine in the high adoption-suppression zone for flat ownership charges. The legal analysis indicates that the incentive programme satisfies ECHR legitimate-expectation criteria and that the proposed levy is likely to fail both the proportionality and transitional adequacy limbs of the fair-balance test. The preferred alternative combines a kWh surcharge on public charging with a phased per-kilometre road-user charge and three-year grandfathering for existing owners. Full article

Transport decarbonization requires infrastructure that can use time-resolved carbon information without overstating the representativeness of short proof-of-method runs. This study introduces Adaptive Energy Stations (AESs), multi-fuel transport-energy nodes that integrate marginal grid-emission signals, fuel life-cycle carbon intensities, wholesale electricity prices, and vehicle operating constraints into a station-level dispatch optimization. The implemented case is a one-week winter proof-of-method for CAISO/CAISO_NORTH using 168 hourly service events over 1–8 January 2026 Pacific time, archived WattTime marginal operating emissions, CAISO locational marginal prices, eGRID CAMX annual-average factors, and declared vehicle and fuel-pathway parameters. In the audited CAISO scenario, the attached dispatch outputs report a reduction from 181.76 to 123.38 g CO₂e/km relative to the specified static baseline, corresponding to a 32.12% reduction for the one-week winter service-event stream. The populated dispatch trace shows that the carbon-priority AES plug-in hybrid electric vehicle (PHEV) run selected cellulosic E85 for all 168 events and selected no electric events; this result is interpreted as an operational scenario result for the archived week, not as an annual fleet-average, smart-charging benefit, or deployment forecast. The revised analysis explicitly separates implemented CAISO evidence from ERCOT, MISO-MROW, and ISO–NE extension sensitivities, which remain hypothetical until equivalent marginal-emissions, price, and service-event data are supplied. Battery-production amortization is treated as a separate sensitivity because it can change battery electric vehicle (BEV)–cellulosic E85 equivalence conclusions: at 50–100 kg CO₂e/kWh over 240,000 km, a 75 kWh BEV pack contributes 15.6–31.3 g CO₂e/km and a 14 kWh PHEV pack contributes 2.9–5.8 g CO₂e/km. Practical-equivalence claims are therefore conditional on the declared boundary, equivalence margin, and production-emissions treatment. Full deployment requires validated marginal-emission access, transparent dispatch-audit outputs, supply-chain verification, user-behavior characterization, cost sensitivity analysis, and cybersecurity safeguards. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

We propose a decentralized strategy for coordinating the bidirectional charging and discharging of battery electric vehicles (BEVs) in renewable-powered parking lots. The framework combines mean-field games (MFGs) and model predictive control (MPC) to address the coupled stochastic dynamics induced by uncertain renewable generation and random vehicle arrivals and departures. Solar and wind power fluctuations are modeled using autoregressive moving-average (ARMA) processes, while the time-varying vehicle population is represented through finite Poisson processes. The coordination problem is formulated as a large-scale game, where an aggregator designs individual cost functions to maximize available energy utilization while promoting fairness through near-equal states of charge (SOCs) at departure. Scalability is achieved through MFG theory, ensuring convergence and stability even under highly volatile generation and fluctuating agent populations. Numerical simulations validate the proposed strategy against two straightforward algorithms: capacity-ordered saturation allocation (COSA) and capacity-ordered fair allocation (COFA). These centralized approaches achieve high target fulfillment in static, low-intensity environments, where available energy accommodates a stable fleet without exceeding power limits. However, their efficacy degrades significantly in dynamic, high-intensity environments, where the interplay of volatile generation, continuous fleet turnover, and strict power constraints strains the system. In contrast, the proposed MFG-MPC framework provides a decentralized response that elegantly navigates the trade-offs between energy availability, demand stochasticity, and power limits. Ultimately, this approach ensures robust energy utilization while safeguarding vehicle equity, confirming its strong suitability for real-time deployment. Full article