Search Results (152)

In-wheel motor (IWM) systems enable compact architectures and advanced control strategies, making them increasingly relevant in hybrid and electric vehicle applications. This work presents the design and the integration of a front-axle IWM system for a Formula SAE combustion vehicle, within a parallel hybrid configuration. The study includes vehicle dynamics analysis, battery pack sizing under strict regulatory constraints, and an initial evaluation of motor and transmission requirements. A MATLAB R2023a-based algorithm was developed to design and optimize a compact two-stage planetary gearbox. This structured and scalable approach supports future development phases and offers a valuable methodology for early-stage hybrid powertrain design. Full article

Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport. Full article

A fuel cell (FC)/supercapacitor (SC) hybrid powertrain is proposed for rubber tire gantry (RTG) cranes, aiming to address their characteristics of high peak/low average power demand and huge potential energy recovery. Unlike conventional design methods that neglect the coupling effects of energy management strategies (EMSs), this paper adopts a joint optimization (JO) for the powertrain parameters’ design. Parameters are preliminarily sized based on routine container handling tasks, then refined via a dynamic programming (DP)-based EMS for secondary optimization to minimize the total crane operation costs that cover hydrogen consumption as well as FC degradation. Iterations of the optimization process continue until targets are met. The results indicate that the JO framework achieves dual energy-economic goals, exhibiting a 57.33% enhancement in fuel economy compared to diesel-powered cranes through port validation while concurrently decreasing the SC’s capacity redundancy by 12.7%. These findings aid FC/SC RTG crane configuration design in ports. Additionally, the theoretical optimal operation cost obtained by the DP-based EMS can be used as a benchmark for evaluating other EMSs. 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 requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems. Full article

The design of novel aircraft concepts powered by electric propulsion systems is a highly multidisciplinary task that requires expert knowledge to be included in the early design phase. Such expertise is typically provided by engineering routines that are not directly linked to the overall aircraft design. This paper presents a digital framework to employ heterogeneous methods at component level to size a complete electrical powertrain within the aircraft design process. A standardized interface and an automated execution workflow are developed to enable consistent data exchange between disciplines, integration of the powertrain architecture into the data model, and synthesis of component results within the overall aircraft design process. The framework is applied for the sizing of the powertrain of a plug-in hybrid electric aircraft. By supporting the integration of expert knowledge at component level in the aircraft design process, this paper facilitates technology assessment at the early design stage. Full article

The decarbonization of railway transportation requires energy-efficient propulsion technologies capable of reducing fossil fuel dependence and improving the operational efficiency of rail systems. Hydrogen fuel cell (FC)–battery hybrid powertrains have emerged as a promising alternative for non-electrified high-speed railway lines due to their potential for energy-efficient operation and reduced environmental impact. However, the optimal sizing and coordinated operation of these hybrid energy sources remain a challenging problem because energy efficiency, component degradation, and system cost are strongly interrelated. This study proposes a degradation-aware mixed-integer linear programming (MILP) framework for the optimal sizing and energy management of a FC–battery hybrid propulsion system for high-speed trains. The optimization simultaneously determines the capacities of FC stacks, battery modules, and hydrogen storage while minimizing the overall lifecycle cost and improving system energy utilization. Battery and FC degradation models are incorporated into the optimization problem through linearized formulations to ensure realistic long-term operation. The proposed framework is evaluated using real operational data in the approximately 71 min high-speed rail corridor between Bursa and Osmaneli in Türkiye. Simulation results show that increasing battery capacity significantly reduces FC stress while enabling more efficient energy utilization through regenerative braking and power balancing. The results indicate that optimal battery sizing can notably improve system performance, reducing the total lifecycle cost from $1.12\times {10}^9$ USD to $5.65\times {10}^8$ USD, while decreasing the required number of fuel cell units from 31 to 18 and mitigating fuel cell degradation. The proposed approach provides an effective design tool for energy-efficient hydrogen-powered railway systems. Full article

This study presents the application of an in-house developed safety assessment method on the scaled flight demonstrator e-Genius-Mod, which is equipped with distributed electric propulsion. Thereby, simplified aerodynamic and propulsive models are derived from existing flight test data. The safety assessment method is extended by modeling approaches for spanwise lift distribution and propeller slipstream effects on lift generation to incorporate an approximation of aero-propulsive effects. Selected failure case scenarios, namely single propulsor failures, are used to define suitable flight test scenarios as preparation for future validation of model predictions against flight test data. The application of the safety assessment method is shown to yield valuable predictions of failure effects on top-level aircraft performance and indicates that yaw moment-related failure effects are still dominant. Therefore, the effect of reducing vertical tail size on aircraft controllability and performance is examined. Model predictions indicate that propulsor failures at high thrust and low speed may exceed the yaw control authority of the aircraft, especially for the configurations with reduced vertical tail size. Furthermore, a simplified non-dimensionalised failure case depiction is presented to ease the transfer of insights to larger-scale aircraft designs and different powertrain architectures. Full article

In order to provide an innovative form of urban air mobility, a new and versatile generation of small, highly automated aircraft is currently being developed. This is made feasible by the development of new technologies such as electrified powertrains, Vertical Take-Off and Landing capabilities and distributed propulsion systems. The operation of these novel aircraft types will generate a new source of air traffic noise. In particular, the perception of noise and the annoyance caused by these aircraft and their distributed propulsion systems are likely to deviate from those of conventional aircraft and will also depend on psychoacoustic effects. Thus, the noise emission and its subjective perception will be key factors for the success of urban air mobility vehicles and their acceptance by society. In order to investigate acoustic effects that enable low-noise aircraft design, a multidisciplinary approach is applied to develop new aircraft concepts for urban air mobility. This approach includes the conceptual design of two vehicles, one vehicle with tilt-rotors and one with tiltable, ducted fans; the sizing of an electric powertrain; the design and manufacturing of a wingtip rotor; and the design and manufacturing of the low-speed ducted fans. This paper presents the design of the two vehicle architectures, including their electric powertrain, as well as the aerodynamic and acoustic performance of the rotor and fan. Full article

When looking towards the goal of reducing greenhouse gas (GHG) emissions, automotive manufacturers face several challenges when planning future vehicle offerings in different markets. The planned vehicle offerings must cope with uncertainties in the supply chains of critical materials and adhere to regulatory requirements in different regions, all while appealing to customer preferences and maintaining low cost. Regulatory requirements, which are often based on tailpipe GHG emissions, do not necessarily align with Lifecycle Analysis (LCA) of GHG emissions, which becomes yet another challenge towards attaining sustainability goals. Planning the future mix of vehicles to be manufactured under all such considerations can be a complex task, often relying on methods with poor transparency, unguaranteed optimality, or requiring difficult-to-predict a priori knowledge. This paper considers the special case of a short time window (one future model–year), which allows for modelling the future planning decisions as a linear programming (LP) problem, which in turn, can be solved to global optimality via well-established algorithms, such as Dual-Simplex. The proposed formulation is demonstrated via one simple example, as well as a scaled-up study with two regions, two vehicle size categories, and four powertrain configurations. A key insight that the proposed formulation is able to demonstrate in the scaled-up study is how the optimum (lowest) LCA GHG solution depends on the availability of battery materials, ranging from an increased share of hybrids under low battery supply to an increased share of electric vehicles for abundant battery supply. Full article

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios. Full article

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg. Full article

Heavy-duty trucking is central to the U.S. economy, and improving its long-term sustainability requires cost-effective, energy-efficient, and reliable operations. Emerging technologies—advanced powertrains, batteries, and alternative fuels—offer potential solutions, but their economic and operational viability remains uncertain. This study evaluates the performance of Class 8 battery electric (BEV), plug-in hybrid (PHEV), fuel cell electric (FCEV), and diesel trucks in terms of energy use and the levelized cost of driving (LCOD) to determine when these technologies become competitive without compromising operational reliability. The analysis explores how evolving fuel prices and vehicle technology improvements in 2023, 2035, and 2050 influence the cost competitiveness of each powertrain. By comparing the results at both the technology level and the fleet level, the study demonstrates that powertrains that appear cost-effective on individual routes may not always scale to fleet-wide viability, and vice versa. The analysis is based on real-world data from over 15,700 Class 8 truck trips recorded in California in 2022, capturing diverse driving scenarios, payload conditions, and operational constraints. The results show that BEV250 can deliver cost-effective performance in short-haul operations (0–250 miles) under depot electricity prices below USD 0.34/kWh and maintain this advantage through 2050 as battery costs decline. In the 250–500-mile segment, the technology-level analysis indicates that BEV500 often achieves the lowest LCOD on individual tours, particularly under low electricity prices, while the fleet-level results show that FCEVs provide a more consistent cost performance across all tours, especially when the route variability is high. For long-haul operations (>500 miles), where BEVs are assumed to operate without en-route charging, FCEVs emerge as the most cost-effective non-diesel option by 2050, provided hydrogen prices fall below USD 6/kg. PHEVs show a limited long-term competitiveness and are mainly viable under transitional fuel price conditions. Overall, the findings underscore that there is no one-size-fits-all solution. Powertrain adoption must be range-aware, infrastructure-sensitive, and fleet-structured. By integrating technology-level and fleet-level perspectives, this study provides actionable insights for fleet operators, policymakers, and industry stakeholders seeking to balance cost, reliability, and sustainability in heavy-duty freight. Full article

The electrification of non-road mobile machinery is advancing to enhance sustainability and reduce emissions. This study investigates how to maximize the efficiency of the retrofitting of a material handler from an internal combustion engine to a battery-powered electric motor, while keeping the hydraulic system unchanged. Using a previously validated model, this study proposes three control strategies for the electric motor and hydraulic pump to enhance efficiency and performance. The first control strategy optimizes hydraulic pump performance within its most efficient displacement range. The second strategy maximizes powertrain efficiency by considering both efficiencies of the electric motor and hydraulic pump. The third strategy uses a servo-actuated valve to adjust the load-sensing margin and exhibits energy savings up to 14.2% and an 11.5% increase in efficiency. The proposed strategies avoid complex optimization algorithms, ensuring practical applicability for small- and medium-sized enterprises, which often face cost constraints and limited scalability. Full article

Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper introduces a novel optimization paradigm, termed physics-informed gradient-enhanced multi-objective optimization (PI-GEMO), to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition, powertrain sizing, and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model, which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PI-GEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results. Full article