Search Results (223)

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model was established, and the cogging torque was analytically calculated to compare the motor’s performance under different groove schemes. Secondly, global multi-objective optimization of the rotor groove dimensions was performed using a combined simulation approach involving Maxwell, Workbench, and Optislang, and the optimal rotor groove size structure was selected using the TOPSIS method. Finally, a comparative analysis of the motor’s performance under both rated-load and no-load conditions was conducted for the pre- and post-optimization designs, followed by verification of the mechanical strength of the optimized rotor structure. The research results demonstrate that the combined optimization approach utilizing the genetic algorithm and the TOPSIS method significantly enhances the torque characteristics of the motor. The computational results indicate that the average torque is increased to 165.32 N·m, with the torque ripple reduced from 28.37% to 13.32% and the cogging torque decreased from 896.88 mN·m to 187.9 mN·m. Moreover, the total distortion rates of the air-gap magnetic flux density and the no-load back EMF are significantly suppressed, confirming the rationality of the proposed motor design. Full article

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

The global urgency to mitigate climate change by reducing transport-related emissions drives the accelerated electrification of road freight transport. This paper presents a comprehensive meta-study forecasting the development and corresponding power demand of electric trucks and tractor units in Hamburg up to 2050, emphasizing the shift from conventional to electric vehicles. Utilizing historical registration data and existing commercial and institutional reports from 2007 to 2024, the analysis estimates future distributions of electric heavy-duty vehicles across Hamburg’s 103 city quarters. Distinct approaches are evaluated to explore potential heavy-duty vehicle distribution in the city, employing Mixed-Integer Linear Programming to quantify and minimize distribution uncertainties. Power demand forecasts at this detailed geographical level enable effective infrastructure planning and strategy development. The findings serve as a foundation for Hamburg’s transition to electric heavy-duty vehicles, ensuring a sustainable, efficient, and reliable energy supply aligned with the city’s growing electrification requirements. Full article

As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

The power system, which is also one of the most crucial parts of fuel cell cars, marks the biggest distinction between them and conventional automobiles. Fuel cell hybrid power systems are reviewed in this paper along with their current state of research. Three different kinds of fuel cell hybrid power systems—fuel cell–battery, fuel cell–supercapacitor, and fuel cell–battery–supercapacitor—are thoroughly compared and analyzed, and they are systematically explained in the three areas of passenger cars, buses, and heavy duty trucks. Existing fuel cell hybrid systems and energy strategies are systematically reviewed and summarized, including predictive control strategies based on game theory, power allocation strategies, fuzzy control strategies, and adaptive super twisted sliding mode control (ASTSMC) energy management techniques. This study offers recommendations and direction for the future direction of fuel cell hybrid power system research and development. Full article

Diesel particulate matter—primarily ultrafine particles (UFPs), defined as particles smaller than 0.1 µm—are released by diesel-powered vehicles, especially those used in heavy-duty hauling. While much of the existing research on traffic-related air pollution focuses on urban environments, limited attention has been paid to how complex topography influences the concentration of UFPs, particularly in areas with significant truck traffic. With a focus on Morgantown, West Virginia, an area distinguished by a steep topography, this study investigates how travel over two different terrain conditions affects UFP concentrations close to roadways. Specifically, we sought to determine if the truck count taken from simultaneous video evidence could be used as a surrogate for varying topography in determining the concentration of UFPs. This study shows that “TRUCK COUNT” and “TRUCK SPEED” have a linear relationship and yield a possible surrogate measure of the lung dose of UFP number concentration. Our results demonstrate a statistically significant (p < 0.1) linear relationship between truck count and UFP number concentration (R = 0.77 and 0.40), validating truck count along with truck speed as a medium effect surrogate for estimating near-road UFP exposure. Dose estimation using the Multiple-Path Particle Dosimetry (MPPD) model further revealed that approximately 30% of inhaled UFPs are deposited in the alveolar region, underscoring the public health relevance of this exposure pathway in topographically complex areas. This method ultimately awaits comparison with health effects to determine its true potential as a useful exposure metric. Full article

Since 1 January 2024, newly produced heavy-duty trailers are subject to the assessment of their performance regarding CO₂ and fuel consumption according to Implementing Regulation (EU) 2022/1362. The method is based on the already established approach for the CO₂ and energy consumption evaluation of trucks and buses, i.e., applying a combination of component testing and vehicle simulation using the software VECTO (Vehicle Energy Consumption calculation TOol). For the evaluation of trailers, generic conventional towing vehicles in combination with the specific CO₂ and fuel consumption-relevant properties of the trailer, such as mass, aerodynamics, rolling resistance etc., are simulated in the “VECTO Trailer” software. The corresponding results are used in the European HDV CO₂ standards with which manufacturers must comply to avoid penalty payments (2030: −10% for semitrailers and −7.5% for trailers compared with the baseline year 2025). Methodology and legislation are currently being extended to also cover the effects of electrified trailers (trailers with an electrified axle and/or electrically supplied auxiliaries) on CO₂, electrical energy consumption, and electric range extension (special use case in combination with a battery-electric towing vehicle). This publication gives an overview of the developed regulatory framework and methods to be implemented in a future extension of VECTO Trailer as well as a comparison of different e-trailer configurations and usage scenarios regarding their impact on CO₂, energy consumption, and electric range by applying the developed methods in a preliminary potential analysis. Results from this analysis indicate that e-trailers that use small batteries (5–50 kWh) to power electric refrigeration units achieve a CO₂ reduction of 5–10%, depending primarily on battery capacity. In contrast, e-trailers designed for propulsion support with larger batteries (50–500 kWh) and e-axle(s) (50–500 kW) demonstrate a reduction potential of up to 40%, largely determined by battery capacity and e-axle rating. Despite their reduction potential, market acceptance of e-trailers remains uncertain as the higher number of trailers compared with towing vehicles could lead to slow adoption, especially of the more expensive configurations. Full article

Heavy-duty vehicles (HDVs), such as buses and commercial trucks, display unique dynamic characteristics due to their high mass and specific actuator properties. These factors make HDVs particularly sensitive to changes in vehicle load and road gradient, which significantly affect their longitudinal control performance. In other words, such variations present considerable challenges in maintaining stable and efficient longitudinal control of HDVs. To address these challenges, this study proposes a model reference adaptive control (MRAC) framework explicitly designed for HDVs. The control system utilizes a state predictor to mitigate actuator load problems caused by high-frequency components in the adaptive control input. In addition, when input constraints are present, the reference model is modified using the μ-modification technique. The system satisfies Lyapunov stability conditions and ensures stable longitudinal control performance across a range of driving conditions. The proposed closed-loop longitudinal control system was evaluated by implementing the controller using the vehicle dynamics simulation software IPG TruckMaker 12.0.1 and integrated with MATLAB/Simulink R2022b. The test scenarios included repetitive speed change maneuvers, which accounted for uncertainties such as road gradients, headwinds, and vehicle load conditions. The simulation results show that the control system not only effectively suppresses disturbances but also enables stable longitudinal speed tracking by considering actuator load and constraints, outperforming conventional MRAC. These results suggest that the proposed closed-loop longitudinal control system can be effectively applied to HDVs. The findings suggest that the proposed closed-loop longitudinal control system can be effectively applied to HDVs, ensuring improved stability and performance under real-world driving conditions. Full article

Achieving long downhill constant-speed driving of heavy vehicles is of great significance for improving vehicle transport safety. As a kind of auxiliary brake, the hydraulic retarder has the characteristics of large braking torque and compact structure. More importantly, the hydraulic retarder is capable of braking for a long period of time, which enables the vehicle to travel downhill at a constant speed with less or no use of mechanical brakes. However, due to the complexity of hydraulic retarder braking conditions, its output braking torque presents time-varying and non-linear characteristics, and the control of the hydraulic retarder filling rate in order to achieve the vehicle’s long downhill constant-speed braking is a challenging problem. This research proposes a constant-speed control strategy utilizing the robust control method to address the issue of prolonged downhill braking at constant speed for heavy-duty vehicles, which achieves constant-speed and stable driving downhill by controlling the filling rate of the hydraulic retarder. Initially, the dynamic model of the downhill process for heavy-duty vehicles and the physical model of the hydraulic retarder are established. Then, based on the concept of sliding mode control, the sliding mode controller with saturation function and the high-frequency robust controller are developed to modulate the filling rate of the hydraulic retarder in response to variations in vehicle speed. In order to verify the effectiveness of the algorithm, three different operating conditions were set according to the vehicle mass and road gradient, and simulation tests were carried out in the MATLAB/Simulink environment. Simulation results indicate that the high-frequency controller exhibits remarkable robustness against dynamic disturbances within the system. Additionally, when variations in vehicle mass and road gradient occur, the root mean square error of the high-frequency controller’s speed, in comparison to the fuzzy controller, decreases by 0.1157 km/h, while the maximum absolute error in vehicle speed diminishes by 0.248 km/h. Simultaneously, the high-frequency controller proficiently suppresses chatter, thereby meeting the demand for consistent speed braking in big trucks on prolonged downhill gradients. Full article

Light-duty trucks (LDTs) are often used to tow trailers. Towing increases the load on the engine, and this additional load can affect exhaust emissions. Although heavy-duty towing impacts are widely studied, data on LDT towing impacts is sparse. In this study, portable emissions measurement systems (PEMSs) were used to measure in-use emissions from three common LDTs during towing and non-towing operations. Emission rates were characterized by operating modes defined in the Environmental Protection Agency’s (EPA’s) MOVES (MOtor Vehicle Emissions Simulator) model. The measured emission rates were compared to the default rates used by MOVES, revealing similar overall trends. However, discrepancies between measured rates and MOVES predictions, especially at high speed and high operating modes, indicate a need for refinement in emissions modeling for LDTs under towing operations. Results highlight a general trend of increased CO₂, CO, HC, and NOx when towing a trailer compared to non-towing operations across nearly all operating modes, with distinct CO and HC increases in the higher operating modes. Although emissions were observed to be notably higher in a handful of scenarios, results also indicate that three similar LDTs can have distinctly different emission profiles. Full article

Wet multi-disc clutches in transmission systems suffer from the incomplete separation of the friction components, which raises the drag torque and results in power loss and heightened fuel consumption. This incomplete separation arises from the force imbalance between resistance forces, such as the oil viscosity force, and the lack of an axial separating force. Therefore, providing an axial separating force is a potential solution to this problem. In this investigation, small-angle conical separate plates were designed which can provide the elastic restoring force during the separation process. Based on its structural properties, a model describing the clutch engagement and separation process was established. Through bench tests, the feasibility of the model was verified. The influence of the conical plate on the dynamics of the clutch was studied, including the influence of the separation gap, uniformity, and drag torque. Though the transmitted torque was reduced by 10.31% in the low-piston-pressure condition and by less than 2% in the high-piston-pressure condition, the problem of incomplete separation was successfully resolved. The results show that when applying the conical plates, the separation time was reduced by 18.78%, with a 25.31% increase in the uniformity of the gaps. Accordingly, the drag torque was reduced by 37.73%. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

Decarbonizing Oregon’s heavy-duty trucking sector, which accounts for 24% of the state’s transportation emissions, is essential for meeting carbon reduction targets. Drop-in fuels such as renewable diesel, biodiesel, and synthetic fuels provide an immediate and effective solution, reducing emissions by up to 80% while utilizing the existing diesel infrastructure. In 2023, Oregon’s heavy-duty trucks consumed 450 million gallons of diesel, with drop-in fuels making up 15% of the fuel mix. Renewable diesel, which is growing at a rate of 30% annually, accounted for 10% of this volume, thanks to incentives from Oregon’s Clean Fuels Program. By 2030, drop-in fuels could capture 40% of the market, reducing CO₂ emissions by 3.5 million metric tons annually, assuming continued policy support and advancements in feedstock sourcing. Meeting the projected demand of 200 million gallons annually and securing sustainable feedstock remain critical challenges. Advances in synthetic fuels, like Power-to-Liquids (PtL) from renewable energy, may further contribute to decarbonization, with costs expected to decrease by 20% over the next decade. Oregon aims for a 50% reduction in emissions from heavy-duty trucks by 2050, using a mix of drop-in fuels and emerging technologies. While hydrogen fuel cells and electric trucks face challenges, innovations in infrastructure and vehicle design will be key to the success of Oregon’s long-term decarbonization strategy. Full article