Search Results (2,564)

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

Predictive maintenance of electric vehicle (EV) fleets requires accurate estimation of Remaining Useful Life (RUL), Time-to-Failure (TTF), and State-of-Health (SOH) from heterogeneous Internet of Things (IoT) telemetry. However, real-world degradation patterns are nonlinear, nonstationary, and highly imbalanced near failure. This study proposes Q-TRACNet, a temporal attention framework that combines causal maintenance-aware preprocessing, adaptive temporal condensation, residual refinement, learnable phase modulation, and hybrid Particle Swarm Optimization–Quantum-Guided Descent parameter tuning. The framework is evaluated on the EV-HLM-RUL dataset and three established prognostics benchmarks: NASA CMAPSS, PHM 2012, and XJTU-SY. Chronological training, validation, and testing partitions are used to preserve temporal causality. On EV-HLM-RUL, Q-TRACNet achieves an MAE of 9.8, an RMSE of 14.7, an $R^2$ of 0.979, and a Critical Degradation Awareness Index (CDAI) of 0.91. It reduces RMSE by 20.11% relative to the strongest competing baseline and achieves an NRMSE of 0.102 and a Kendall correlation of 0.89 ($p<{10}^{-4}$). Cross-dataset experiments demonstrate stable performance for RUL, TTF, and short- and long-horizon SOH prediction. Ablation and sensitivity analyses further confirm the contributions of the temporal and attention components and the stability of degradation-aware evaluation. Q-TRACNet also provides lower training cost and inference latency than competing architectures, supporting practical maintenance planning, inspection prioritization, and resource allocation in connected EV fleets. Full article

With the rise in demand for electric vehicles (EV), hairpin welding is gaining popularity for its efficient manufacturing of critical EV motor components. Due to its high electrical and thermal conductivity and relative affordability, copper is commonly used. The tip of the hairpin can be joined by laser welding, micro TIG, and resistance brazing. Of these techniques, infrared (IR) laser welding is commonly used for its high dimensional accuracy and non-contact joining. However, copper is highly reflective to IR wavelengths, limiting the speed of this technique in joining copper hairpin couples. Considering the high manufacturing volume of the EV motor industry combined with copper hairpins being a high-volume component in each EV motor, copper’s high reflectivity to IR wavelengths presents a significant challenge to EV manufacturing efficiency. To accommodate this challenge, many researchers have examined the feasibility of using blue diode lasers to produce copper hairpin welds to leverage copper’s higher absorptivity to blue light wavelengths. Alternatively, this paper investigates a hybrid approach in which both IR and blue diode lasers (BDL) are used simultaneously to benefit from the advantages of both IR and blue wavelengths. To investigate this technique, hairpins were laser spot welded using IR, and hybrid technology was also analyzed and presented. Successful welds were produced in 0.6 s with both IR-only and hybrid techniques. Using IR-only and a power of 1000 W, a shallow weld joint with a fusion zone depth ranging from 0.2 to 2.8 mm was produced. A satisfactory weld joint (weld bead) was achieved when the IR power was increased to 1300 W exhibiting a fusion zone depth of 3.0 to 3.1 mm. When the hybrid method (1000 W IR with 750 W BDL) was employed, a satisfactory weld joint was also achieved with a fusion zone depth of 2.9 to 3.0 mm. Electrical resistivity measurements of the 1300 W IR-only and hybrid methods were on the same order of magnitude as the unwelded copper reference of 1.9 × 10⁻⁴ Ω.cm. Hairpins welded with 1000 W IR resulted in higher electrical resistivities ranging from 1.2 × 10⁻³ to 6.2 × 10⁻³ Ω.cm. Peel force tests demonstrated the highest max peel force of 770 ± 15 N under the 1300 W IR condition, whereas the 1000 W IR and hybrid methods resulted in max peel forces of 410 ± 15 and 720 ± 20 N, respectively. Regarding peel test elongation at failure, the hybrid method was highest at 13 ± 3%, followed by IR with 1300 W at 12 ± 3% and IR with 1000 W at 11 ± 2%. The results of this paper demonstrate comparable microstructural, electrical, and mechanical properties under hybrid welding to higher power IR welding, simultaneously showing hybrid laser welding as a suitable alternative in copper hairpin joining for EV motor application. 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

This paper proposes a control method for electric vehicles’ powertrains that concentrates on improving the efficiency of the drivetrain as a whole by targeting regenerative energy recovery. The design of the system is based on a traditional six transistor voltage source inverter using an asymmetric control scheme together with a hybrid CNN-TD3 controller for speed control purposes. The asymmetric control method distinguishes the dynamics of the two operating modes of the inverter, that is, the traction and regenerative braking modes, by controlling the gain values based on the operating mode of the system. The TD3 algorithm adjusts the values of three continuous control parameters, Kpv, Kiv, and Ks, in real time. A multi-objective reward function aims to maximize the system’s energy efficiency, regenerative energy recovery efficiency, speed control accuracy, driving comfort, current harmonics reduction, and battery safety. Simulations performed using the WLTP Class 3 driving cycle show energy efficiency of 15.3% (25.2 to 21.44 kWh/100 km), 59.5% speed regulation error reduction, 19.6% increase in regenerative recovery efficiency from 65.2% to 77.34%, and 34.2% reduction in current THD from 8.58% to 5.65%. Full article

The transition from internal combustion engine (ICE) vehicles toward hybrid and battery electric vehicles is reshaping both automotive engineering and the sensory experience of driving, yet its neurophysiological consequences remain underexplored. This single-subject, repeated-measures study investigated the neurophysiological and autonomic responses of one professional driver across 37 real-road sessions in three vehicle categories: electric (EV; n = 16), internal combustion engine (ICE; n = 15), and hybrid (n = 6). Electroencephalography, heart rate, heart-rate variability, galvanic skin response, and peripheral oxygen saturation were continuously recorded across three phases: a three-minute pre-drive baseline, 15–20 min of active driving, and a three-minute post-drive recovery. EEG power was analysed in the theta, alpha, low-beta, and high-beta bands. Because all sessions were completed by the same driver, vehicle group, phase, and their interactions were tested with a rank-based mixed-effects model treating vehicle session as a random intercept. Vehicle group significantly affected broadband EEG power: ICE sessions showed higher theta, low-beta, and high-beta power than EV sessions (all post hoc p ≤ 0.025), with the Hybrid group statistically intermediate; a nonparametric alpha-band effect (p ≤ 0.025) did not survive a repeated-measures-adjusted mixed-effects model (p = 0.107) and is reported with lower confidence. Galvanic skin response declined steeply and uniformly from the pre-drive baseline in every group (phase p < 0.001), whereas heart-rate variability (RMSSD) increased during active driving (p = 0.013). No modality showed a significant Group × Phase interaction (all p ≥ 0.053). Within this single-driver protocol, propulsion type was systematically associated with cortical arousal—ICE imposing a higher cortical load than EV—while autonomic session dynamics were uniform across vehicle types; findings cannot be generalized beyond this driver. Full article

Lithium-ion batteries (LIBs) are essential for current technological infrastructure, driving the development of portable electronics, electric vehicles or grid-scale energy storage. The performance and sustainability of LIBs are critically dependent on their anode materials. This comprehensive review analyzes the evolution and characteristics of key anode materials, highlighting the specific properties they confer to the final battery products. Beyond material properties, the synthesis methods employed for these materials, from conventional techniques (such as solid-state reactions, sol–gel, hydrothermal/solvothermal, co-precipitation, etc.) to innovative and greener approaches (like electrospinning and a novel induction furnace-oxidation hybrid method for complex oxides), are a crucial part in the development of sustainable materials. While these methods offer different advantages, the challenges in achieving optimal electrochemical performance, including issues related to material stability, capacity retention and scalability, remain significant for both research and manufacturing industries. Furthermore, a significant focus is placed on strategies for mitigating the environmental impact associated with anode material production, emphasizing the importance of unconventional and sustainable synthesis routes. Ultimately, the sustainable evolution of LIB technology to achieve future energy demands hinges on overcoming existing limitations. This necessitates integrated research combining advanced material modeling and design, scalable and environmentally conscious synthesis techniques and in-depth electrochemical characterization. Full article

Electric vehicle (EV) remanufacturing is critical to circular economy development. However, consumers suffer from battery-induced range anxiety, limited transferable factory warranties for pre-owned EVs, and low trust in remanufactured components due to unstandardized aftermarket supervision. To mitigate these quality concerns, secondhand EV retailers have adopted a full original-price refund guarantee. While existing studies primarily focus on subsidies, carbon policies, and quality disclosure, few explore refund guarantee mechanisms in EV remanufacturing, especially their threshold effects and interactions with pure and hybrid component sourcing strategies. This study addresses this gap by establishing six game-theoretic models and deriving market equilibria via backward induction. We systematically analyze how failure rates, procurement costs, consumer-perceived quality, and guarantee strength affect market performance, optimal sourcing strategies, and social welfare, with numerical simulations validating our theoretical results. The findings reveal a universal threshold effect: refund guarantees boost sales only beyond a critical strength level, whereas weak guarantees fail to cover compensation costs and may even reduce social welfare. In particular, refund guarantees significantly improve social welfare under pure-remanufactured sourcing. This work offers actionable insights for remanufacturers, consumers, and regulators. Firms can optimize guarantee efficiency by simplifying claim processes, while policymakers can implement differentiated warranty regulations and tiered incentives. Full article

High-velocity oxy-fuel (HVOF)-sprayed NiCrBSi coatings containing 0, 10, 30, and 50 wt.% WC-Co were evaluated on AZ91D magnesium alloy to determine how the discrete reinforcement level affects surface topography, phase constitution, Vickers microhardness, dry-sliding friction, mass loss, and wear-track microchemistry. As-sprayed surfaces were characterized by three-dimensional profilometry; coating cross-sections and worn surfaces by optical microscopy and SEM/EDS; phase constitution by XRD; and mechanical response by HV_0.1 indentation. Dry-sliding tests were performed at 10, 30, and 50 N over 100–1000 m. Increasing WC-Co content raised Sa from 8.8 ± 0.3 to 13.0 ± 0.5 µm and Vickers microhardness from 776 ± 4 to 959 ± 5 HV_0.1. XRD indicated a γ-Ni-based matrix containing boride/carbide constituents, while WC, W₂C, and Co became increasingly prominent in the reinforced coatings. Boride assignments are based on diffraction evidence, whereas B and C EDS signals were treated semi-quantitatively. The 50 wt.% WC-Co coating exhibited the lowest mass loss and mean coefficient of friction at every load. Its mean friction coefficients were 0.31, 0.35, and 0.41 at 10, 30, and 50 N, corresponding to reductions of 40.1%, 38.9%, and 36.2% relative to AZ91D. At 1000 m, its mass-normalized wear rate indices were 9.0 × 10⁻⁴, 4.0 × 10⁻⁴, and 5.3 × 10⁻⁴ mg N⁻¹ m⁻¹, respectively. Post-wear mapping showed the largest field-scale W-Co-rich fraction in the 50 wt.% coating; however, isolated spectra containing more than 94 wt.% Mg are compatible with local coating penetration/substrate exposure and/or Mg-rich debris. The 50 wt.% composition therefore provided the best combined response among the four tested levels, while intermediate compositions are required to identify a continuous-composition optimum. Full article

As a crucial flexible regulation resource in distribution networks, electric vehicle fast-charging stations exhibit high-power, stochastic fluctuation characteristics. This randomness makes false data injection attack (FDIA) particularly challenging to detect with conventional methods, thereby posing a significant threat to the secure operation of vehicle-to-grid cyber–physical systems. To address this issue, this paper proposes an FDIA detection method based on ResNet-iTransformer. Firstly, a residual network (ResNet) is employed to capture abnormal patterns in the measurement data layer by layer. Secondly, the dimension reversal technique of iTransformer is utilized to model the interrelationships among channels, enabling information exchange among feature variables through multi-head self-attention. Then, attention pooling is introduced to filter multi-channel features and focus on key channels, thereby achieving accurate attack detection. Finally, simulation tests are conducted on the IEEE 33-bus system. The results show that, compared with existing common detection methods, the proposed method achieves significant improvements in detection precision, recall, and F1 score, enabling more accurate detection of FDIA in the vehicle-to-grid cyber–physical system. Full article

This research concentrates on the electric vehicle (EV) industry in China and Japan. China is the largest and Japan the third-largest auto production country after the U.S. This study explores the current and future transition to battery electric vehicles and hybrid electric vehicles in Japan and China. Three Japanese auto companies, Toyota, Honda, and Nissan, and BYD from China are studied in this research. Toyota, Honda, and Nissan are actively pursuing the development of hybrid electric vehicles in Japan. Meanwhile, the research examines the Chinese EV company BYD, which is a major global competitor in the EV industry. This study compares the companies in terms of their strategies, strengths, weaknesses, and export destinations and delineates their competitive strategies and outlooks. In addition, the study examines the elements of the supply chain needed for building EVs, such as lithium, nickel, and cobalt. Furthermore, this research discusses some of the issues with EVs, such as the challenges related to the production and recycling of batteries and the implications as far as green and sustainable practices regarding EVs in the selected countries are concerned. The final part of this research explores how the production of EVs can affect the global reduction of carbon emissions. The findings of this study indicate that the transition to EVs depends on the structural position as far as the supply chain, the manufacturing of electric batteries, charging stations, and the size of the operations are concerned. The results indicate that BYD is in a stronger position in terms of the infrastructure necessary for the production of EVs. Meanwhile, Japanese auto companies are focused on hybrid EVs due to infrastructure related to EV batteries, supply sources, and charging stations. In addition, this research provides informative insights into the future of electric vehicles in the global market. The study offers recommendations for a comprehensive approach that integrates national policies, technological innovation, and the environmental impact of the transition to electric vehicles on a global scale. Full article

As the power and voltage of electric vehicles increase, the thermal stability of battery components, such as busbars, becomes critical. This study proposes a method for inorganic thermal insulation and insulating coating using alumina–silica hybrid particles deposited via electrophoretic deposition (EPD). We investigated the effects of copper substrate pre-treatment and the weight ratio of tetraethylorthosilicate (TEOS) to methyltrimethoxysilane (MTMS) on the quality of the coating. AFM analysis confirmed that a 1-min pre-treatment optimized surface roughness, which helped prevent cracks during the drying process. Among the various compositions tested, the sample with 30% TEOS demonstrated the highest thermal stability (670 °C) and dispersion stability (zeta potential of −15.5 mV), attributed to the increased density of the siloxane network. All samples maintained insulation performance up to 6.3 kV. These findings present an effective strategy for enhancing the fire safety of EV electrode materials. 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

Residential photovoltaic generation and household demand are temporally mismatched, affecting grid dependence and local storage use. This study formulates a 24 h mixed-integer linear programming model for a grid-connected residential prosumer with rooftop PV, a stationary behind-the-meter battery, and an electric vehicle capable of vehicle-to-home operation. Four configurations are compared under common external inputs: no storage, battery only, V2H only, and a hybrid battery–V2H system. The model resolves the main power routes, enforces charging, discharging, and cyclic state-of-charge constraints, allows grid charging, and excludes storage-to-grid export. PV generation is reduced, and residential demand is increased through a prescribed deviation-scaling parameter evaluated at five levels for clear-day and synthetic partly cloudy profiles. Numerical consistency is checked using an independent no-storage calculation and equation residuals. At $\rho$ = 0.3 under the clear-day profile, the hybrid configuration reduces daily operating cost from USD 26.113 to USD 17.807, increases PV self-consumption from 69.684% to 89.121%, lowers utility purchase from 106.380 to 88.700 kWh/day, and reduces export from 36.170 to 12.980 kWh/day. Under the partly cloudy profile, all storage-based configurations reach 100% PV self-consumption and zero export, while the hybrid case retains the lowest operating cost. The results are conditional on the adopted capacities, continuous EV connection, tariff structure, and exclusion of degradation and investment costs. Full article

Vehicle-to-everything (V2X) information can support energy-efficient vehicle operation in signalized traffic. This paper presents a sequential hierarchical eco-driving strategy for fuel-cell hybrid electric vehicles (FCHEVs) traveling through multiple signalized intersections under prescribed signal-phase and road-speed settings. The upper layer employs an ${\mathrm{A}}^{\ast }$-inspired adaptive heuristic graph search in a discretized time–distance graph. A dimensionless motor-power-demand-based numerical ranking score, evaluated at the average speed of each candidate edge under a zero-acceleration edge approximation, introduces approximate powertrain-load information into node ranking. This score follows the supplied numerical implementation and is neither edge-integrated energy nor equivalent hydrogen cost; therefore, the upper-layer procedure is not claimed to inherit the admissibility, optimality, or bounded-suboptimality guarantees of standard ${\mathrm{A}}^{\ast }$ or weighted ${\mathrm{A}}^{\ast }$. The lower layer uses a twin delayed deep deterministic policy gradient (TD3)-based energy management strategy that penalizes raw equivalent hydrogen cost, SOC deviation, fuel-cell degradation increments, and battery degradation increments. The reported simulations show a lower raw equivalent hydrogen cost than the baseline strategy in the tested scenarios, while terminal-SOC correction reveals scenario-dependent trade-offs among corrected equivalent hydrogen cost, SOC regulation, and model-based component degradation indicators. The results support a practical multi-objective balance under the reported deterministic simulation settings rather than uniform superiority in every individual indicator. Full article