Search Results (833)

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles. Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Reliable battery health assessment is essential to accelerate battery electric vehicle (BEV) adoption, yet most existing in-vehicle methods do not capture the complex processes driving ageing. Electrochemical impedance spectroscopy (EIS) offers deeper diagnostic insight but faces significant architectural and integration constraints. This study establishes a rigorous system-level framework for practicable onboard EIS implementation, focusing on the integration within Battery Management System (BMS) and powertrain architectures. Various integration topologies for cell-, module- and pack-level EIS are evaluated, highlighting their key trade-offs. The viability of the presented architectures is assessed through an application-specific Multi-Criteria Decision Analysis (MCDA) for mass-market, high-performance and circular economy use-cases. This study confirms the feasibility of onboard EIS while providing industry and scientific stakeholders with practical guidance to advance battery diagnostics for next-generation BEVs. Full article

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options. Full article

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

Road freight logistics is one of the most difficult transport segments to decarbonize. In recent years, battery electric trucks and hydrogen fuel cell electric trucks have emerged as the most promising alternatives to conventional heavy-duty vehicles. However, their integration cannot be reduced to a question of vehicle substitution, as it depends on a broader system of conditions. This paper aims to identify and structure the system-determining parameters that influence the use of battery electric trucks and hydrogen fuel cell electric trucks in road freight logistics. To this end, the study applies a systematic literature review, yielding a final sample of 42 publications. The review shows that drive type suitability depends on parameters across four categories: economic, ecological, performance-related, and external. Accordingly, no single factor determines suitability; rather, outcomes emerge from the interaction of multiple conditions. The reviewed literature does not support a universally superior drive technology. Instead, the suitability of battery electric trucks and hydrogen fuel cell electric trucks depends on the specific configuration of the surrounding system. The paper thus provides a structured framework for future comparative assessments in sustainable road freight logistics. The study is embedded in the Research Campus Mobility2Grid, which provides a practice-oriented context for assessing alternative drive technologies in relation to fleet, depot, energy, and logistics-system requirements. Full article

The European 2035 decarbonisation framework rests on a conditional premise—that higher renewable-electricity penetration accelerates battery electric vehicle (BEV) adoption—yet it has not been tested at the panel level. The question is timely: the December 2025 Automotive Package would soften the 2035 target from 100 to 90 percent CO₂ reduction and permit continued production of plug-in hybrids beyond 2035, while the Alternative Fuels Infrastructure Regulation (AFIR) imposes binding charging-coverage targets from 2025 onwards. We assemble an annual panel of 31 European economies over 2015–2024 (310 country-year observations) and combine a two-way fixed-effects baseline on five disaggregated powertrain shares, an interaction model with public charging coverage as a moderator, and a Hansen-style threshold panel. The within-country BEV-share coefficient on renewable-electricity penetration is statistically null (β = +0.18, p = 0.247), rejecting the linear premise. The plug-in hybrid share, by contrast, responds positively and unconditionally (β = +0.36, p = 0.001)—a “PHEV paradox” of compositional response. The BEV channel, by contrast, is conditional on infrastructure: its marginal effect rises with public charging coverage and is positive only in the upper part of the charging distribution (interaction β₃ = +0.13, p = 0.027). A formal Hansen-style threshold test in the renewable share does not reject the linear specification (sup-F = 0.73, bootstrap p = 0.97), so the BEV conditionality is identified through the charging-coverage interaction. The findings characterise a two-speed European transition. The first channel reflects compliance-led PHEV hedging; the second reflects BEV charging network complementarity enabled by AFIR-mandated coverage. Subsidy rebalancing away from PHEV eligibility, strict AFIR enforcement, and PHEV utility-factor reform are necessary policy levers for the 2035 framework to deliver full electrification rather than the partial electrification that current incentives yield. Full article

With the rapid development of big data and artificial intelligence, motor fault diagnosis has become increasingly important in the fields of intelligent transportation and new energy vehicles. As a core component of the electric vehicle drive system, the operating condition of an asynchronous AC motor is directly related to the safety and reliability of the vehicle powertrain. Once a motor fault occurs, it may lead to powertrain failure, thereby causing traffic accidents, financial losses, and even threats to human life, particularly under high-speed driving conditions where the safety risks are more severe. Therefore, the timely and accurate diagnosis of motor faults in electric vehicles, especially autonomous vehicles, is of great practical significance. To effectively capture the fault-related features embedded in the vibration and voltage signals of asynchronous AC motors and efficiently perform fault diagnosis, this paper introduces a fault-diagnosis model that integrates the exponential moving average (EMA), a two-dimensional multi-scale convolutional neural network (2DMSCNN), and the Squeeze-and-Excitation Network (SENet) mechanism. Experimental validation based on a publicly available asynchronous AC motor fault-diagnosis dataset demonstrates that, compared with traditional machine learning models and ensemble learning methods, the proposed EMA-2DMSCNN-SENet model achieves higher diagnostic accuracy and stronger robustness. Full article

Predictive energy management strategies (PEMSs) have attracted increasing attention in hybrid electric vehicles (HEVs) for improving fuel economy and powertrain efficiency using anticipated driving information. For PEMS, data-driven velocity prediction is widely used to capture complex driving patterns from historical trajectories and future traffic priors, but often lacks kinematic awareness, leading to physical causality violations and long-horizon state drift. To address these issues, this paper proposes a physics-informed PEMS, where a Physics-Informed Spatio-Temporal Network (PI-STN) provides control-oriented velocity information for an MPC-based energy management controller. Specifically, to address pseudo-motion in velocity prediction under standstill conditions, a global zero-speed gating mechanism is introduced; to suppress acceleration/deceleration trends that violate vehicle kinematic causality, a causal penalty is designed; and to mitigate temporal phase misalignment between data-driven predictions and physical motion priors, a Differentiable Kalman Filter (DKF) is incorporated. At each receding horizon step, the PI-STN-predicted velocity sequence is converted into future power demand through longitudinal vehicle dynamics and used by MPC for engine–battery power allocation under SOC and engine transient constraints. Under the same tested conditions, the proposed strategy reduces engine power fluctuation by 15.1% compared with BiLSTM-Transformer, and achieves an equivalent fuel consumption of 323.74 g, outperforming Transformer-KF by 3.12%. Full article

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions. Full article

To address the issues of increased energy consumption and reduced engine efficiency in plug-in hybrid electric vehicles (PHEVs) under low-temperature conditions due to cabin heating demands, this paper investigates the coupling characteristics between the powertrain system and the cabin thermal management system and proposes an adaptive energy management strategy tailored for low-temperature environments. First, a comprehensive model incorporating vehicle dynamics, the engine, and the passenger compartment thermal management system was established. The impact of different ambient temperatures and equivalent factors on the system’s energy consumption characteristics was then quantitatively analyzed under WLTC conditions. Based on this, an adaptive strategy for minimizing equivalent fuel consumption that accounts for cabin heating demand was designed. By using real-time cabin heating demand and engine waste heat power as state feedback, the equivalent factor is dynamically adjusted to coordinate the allocation of power between propulsion and heating. Simulation and hardware-in-the-loop test results indicate that the optimized strategy, by promoting early engine engagement and improving waste heat recovery efficiency, reduces PTC energy consumption by 0.47 kWh under −20 °C WLTC conditions, decreases additional fuel consumption caused by low temperatures by approximately 59%, and improves the vehicle’s equivalent fuel economy by 4.6%, while effectively maintaining passenger compartment thermal comfort. This study contributes to sustainable transportation by reducing low-temperature-induced energy waste, lowering equivalent fuel consumption, and promoting efficient use of engine waste heat, thereby supporting carbon emission reduction goals in hybrid electric vehicle operations. Full article

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

Cascaded multilevel inverters constitute a promising system concept for battery electric powertrains due to their high efficiency, low harmonic distortion, and advanced battery management capabilities. This study presents a novel electro-thermal simulation framework for the symmetrical Parallel Enhanced Commutation Integrated Nested (PECIN) multilevel inverter. The proposed model employs a control-oriented approach that enables the development and evaluation of advanced inverter and battery control algorithms, which exploit the extensive series-parallel reconfiguration capabilities of the PECIN topology. The framework is based on electrical and thermal equivalent circuit models to capture physical behavior and cross-domain interactions. Electrical network analysis employs algorithms that iterate over each phase-arm network, replacing high-dimensional matrix inversions and thereby enhancing computational efficiency. The overall model is readily adaptable to various system configurations, including different AC and DC charging modes, and scalable with respect to the number of submodules and phases. Simulation results for a 31-level multilevel inverter in a three-phase AC charging configuration demonstrate the model’s operational capabilities. Execution time analysis shows that the current distribution calculation is the key contributor to computational effort as the number of submodules increases, resulting in a quadratic growth of the overall computational time. Full article

Road freight transport is an important driver of global greenhouse gas (GHG) emissions. Decarbonizing this sector demands a comprehensive assessment of emerging powertrain technologies, which are currently lacking in the literature. To fill this knowledge gap, we performed a life cycle assessment (LCA) on 10 impact categories to evaluate road freight transport in the Netherlands of four truck alternatives, assuming similar performance: fuel-cell electric (FCEV), hydrogen internal combustion engine (HICEV), battery electric (BEV), and diesel internal combustion engine (DICEV). We compared locally produced green hydrogen, according to EU regulations, with electricity and diesel as alternative fuel chains, while also considering the environmental impact of road infrastructure. We found that FCEV and HICEV trucks achieve the lowest global warming impact when green hydrogen is used. We identified discrepancies between the transport alternatives, highlighting key factors influencing NO_x and particulate matter emissions. Our research also showed that water consumption (WC) for green hydrogen is strongly influenced by upstream processes, with solar-powered electricity emerging as a crucial contributor. Our results highlight the need for more exploration on the environmental impact of green hydrogen and can be used by researchers and practitioners to further understand the complexity of reducing emissions in road freight transport. Full article

Mild hybrid electric vehicles (MHEVs) are a cost-effective solution for reducing fuel consumption and emissions in the automotive sector, offering a low-level electrification alternative to battery electric and plug-in hybrid vehicles. This study uses the Equivalent Consumption Minimisation Strategy (ECMS) to investigate the optimal sizing of P2 MHEV powertrain components and the individual contributions of hybridisation features such as regenerative braking, idling fuel cut-off, load shifting and electric torque assist. Parametric simulations were performed by varying the power of the electric motor and the capacity of the battery for standard driving cycles. The results show that total fuel consumption for the NEDC driving cycle can be reduced by up to 29%, with regenerative braking providing the largest contribution. The optimal electric motor power for mild hybrid applications was found to be in the 20–30 kW range, depending on the driving cycle. Full article