Search Results (459)

The growing adoption of hybrid and plug-in electric vehicles increases heat generation in power electronic modules, driving demand for effective thermal management materials and reliable Al/Cu joining methods. However, large-area Al/Cu joints are challenging as conventional brazing requires high temperatures and flux, and fusion welding performs poorly with dissimilar metals. Contact-Reaction Brazing (CRB), which relies on eutectic-phase formation during heating, presents a promising alternative. Direct CRB of AA6063 and Cu might lead to severe aluminum dissolution above 570 °C. To mitigate this, large-area CRB of AA6063/Cu using a 4104 aluminum-foil interlayer is examined. Brazing temperature, holding time, and pressure are systematically varied to evaluate their influence on joint formation. Interfacial microstructures are characterized by SEM and XRD. Shear testing is used to assess fracture behavior and mechanical performance. A satisfactory shear strength of 48.8 MPa is achieved for the AA6063/AA4104/Cu joint under a brazing temperature of 540 °C, a holding time of 10 min, and an applied pressure of 600 Pa. Full article

The commercialization of electric and hybrid vehicles in Colombia is a strategic component of the national energy-transition agenda and of the country’s climate-governance commitments. However, despite relevant regulatory progress, market deployment remains territorially uneven and institutionally fragmented. The article describes how regulation affects the market for electric and hybrid cars, considering regulatory consistency, inter-institutional cooperation, market obstacles, and the rollout of the energy transition. Based on doctrinal legal analysis, comparative public-policy evaluation, and contextual examination of official vehicle-registration information, the study employs a qualitative and comparative research design. The analysis reviews Colombian legal instruments, policy strategies, and governance arrangements related to electric mobility and compares them with selected experiences in Chile and Mexico. The findings show Colombia has developed tariff reductions, tax benefits, circulation privileges, charging-infrastructure obligations, interoperability rules, and strategic planning instruments. Commercialization faces structural barriers: fragmented regulation, uneven territorial implementation, insufficient charging infrastructure, weak public–private coordination, limited consumer awareness, and a lack of long-term governance for battery replacement and industrial adaptation. The results also show that hybrid electric vehicles dominate national registrations, while battery electric and plug-in hybrid vehicles remain comparatively limited. This distinction shows that Colombia’s current transition is still more strongly associated with hybridization than with full electrification. The article concludes that Colombia requires a more coherent governance architecture capable of integrating regulatory stability, territorial coordination, infrastructure deployment, market facilitation, and long-term energy-transition planning. Full article

While plug-in hybrid electric vehicles (PHEVs) can significantly reduce fuel consumption and emissions, their real-world benefits strongly depend on heterogeneous driver usage patterns. Understanding these usage patterns is therefore essential for optimizing energy management and electrification policies. This study presents a data-driven framework for identifying and characterizing PHEV driving behavior using two primary indicators: the Utility Factor (UF), which quantifies the proportion of electric-mode driving, and the annual vehicle kilometers traveled (VKT), which measures driving intensity. A Gaussian Mixture Model (GMM) is employed in a transformed feature space characterized by the logit of UF and the logarithm of VKT to capture nonlinear relationships and diverse usage patterns. The Bayesian Information Criterion (BIC) is used to find the optimal number of behavioral clusters. To assess the robustness of the clustering, we perform a bootstrap stability analysis and compare it with k-means and a density-based clustering method (DBSCAN). Based on an analysis of real-world PHEVs, three distinct usage patterns are identified. The dominant cluster (96.6%) exhibits moderate electric usage (UF = 0.38) with an annual mileage of 18,617 km and fuel consumption of 3.89 L/100 km, whilst two smaller clusters represent near-full electric operation (2.3%, UF about 1.0, negligible fuel) and low-mileage users (1.1%, approximately 2444 km/year). The clustering demonstrates high assignment confidence (posterior entropy $<{10}^{-4}$) and moderate stability, with a mean Adjusted Rand Index (ARI) of 0.448. The findings indicate that the proposed probabilistic clustering framework offers a comprehensible and statistically robust method for identifying diverse PHEV usage patterns. These insights can support adaptive energy management strategies, effective planning of charging infrastructure, and evidence-based policies to maximize the real-world electrification benefits of PHEVs. 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

To address the poor adaptability to diverse driving cycles and the imbalance between optimization performance and computational efficiency in existing energy management strategies (EMSs) for plug-in hybrid electric vehicles (PHEVs), this paper proposes a lightweight intelligent EMS (IEMS) with adaptive rule-parameter updating. The key contributions lie in constructing an optimized rule library using parameter optimization, and developing an online adaptive updating mechanism for rule parameters combined with driving cycle prediction, realizing dynamic self-adjustment of energy management rules. The results show that compared with the rule-based EMS (RBEMS), the strategy reduces energy consumption by 9.09%, 10.85% and 9.25% under NEDC, WLTC and real-world test cycles, respectively, with drastically lower computation times than dynamic programming (DP). The proposed IEMS can effectively balance fuel economy, driving cycle adaptability and computational efficiency. 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

The transition to a hydrogen-based energy economy emphasizes the potential of hydrogen as a fuel for plug-in hybrid electric vehicles (PHEVs). The performance of a hydrogen engine within a PHEV depends on the choice of its operating modes, which influence both efficiency and emissions. This study proposes a method for developing engine operating lines (EOLs) on engine maps based on minimizing nitrogen oxide (NOx) emissions while considering constraints on maximum engine power. A total of 15 EOLs are proposed for configurations with both constant and variable maximum engine power. Using mathematical modeling of PHEV operation under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the impact of EOL selection on engine characteristics, as well as on battery and generator parameters, is analyzed. For a comprehensive evaluation of EOL effectiveness, five criteria are introduced, considering fuel energy consumption, NOx emissions, wear, mechanical fatigue, and noise, vibration, and harshness (NVH). The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to determine the weighting factors of the criteria and to rank the proposed EOLs, thereby identifying the most efficient configurations. The results show that, for the base hydrogen engine configuration, selecting appropriate operating modes alone enables NOx emissions to be reduced significantly below Euro 6 limits, without any hardware modifications or exhaust aftertreatment. 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

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

The article examines forecasts of electromobility development across seven European countries over a ten-year horizon (until 2035). The introduction provides a characterization and statistical analysis of the electromobility market within the framework of sustainable development. The analysis includes both leading electromobility markets and lower-income countries with relatively small electromobility sectors. First, forecasts for the total number of registered passenger vehicles of all drive types will be generated for each country, followed by forecasts for the number of passenger electric vehicles (Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV)). Based on this data, the degree of electromobility development—defined as the percentage of passenger electric vehicles among all registered passenger vehicles through 2035—will be established. The forecasts will be conducted using an artificial intelligence model, a deterministic chaos theory model and selected trend extrapolation methods. The multi-stage approach applied to the problem, together with the use of single-type models within ensembles and the model selection procedure, constitutes an original, proprietary solution. To the author’s knowledge, a similar approach has not been reported for a forecasting task in the context of electromobility. Three ensemble projections will be presented: low, middle, and high. The article concludes with findings regarding the implementation of European Union (EU) sustainable development goals, specifically the degree of passenger vehicle electrification. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. 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

The real-time performance bottleneck of energy management strategies (EMS) based on model predictive control (MPC) severely restricts their vehicle-grade deployment in series-parallel plug-in hybrid electric vehicles (SPPHEVs). This research develops a real-time adaptive-mode MPC (RTAM-MPC) designed to jointly minimize fuel consumption, electricity usage, and battery aging under strict vehicle-grade execution constraints. An adaptive framework is established by integrating driving pattern recognition (DPR) with MPC, which dynamically adjusts the prediction time grid, solver initialization, and speed prediction configurations. To ensure computational efficiency suitable for embedded systems, a fast numerical optimization method is proposed, alongside a DPR-guided speed prediction model based on a coyote optimization algorithm-optimized kernel extreme learning machine. The results show that RTAM-MPC achieved 98.54% dynamic programming (DP) performance. Compared to the equivalent consumption minimization strategy (ECMS), it demonstrated a 5.37% improvement in economic efficiency and a 24.67% reduction in battery aging. Compared to standard MPC, the average computation time is 11.07 ms, a decrease of 94.48%. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles. Full article