Browse Topic: Energy conservation
This study prepares high-performance PI/VIP composite thermal insulation materials for buildings by integrating polyimide (PI) composite membranes and vacuum insulation panels (VIPs), and uses EnergyPlus to explore their impacts on building energy conservation, operating costs, and carbon emissions under different climates. Experimental results show the materials have low thermal conductivity, long service life, and excellent thermal insulation and flame-retardant properties due to their internal vacuum structure inhibiting heat transfer. Simulations in Jinan (tropical monsoon), Heilongjiang (cold temperate), and Shenzhen (subtropical humid) climates indicate that compared with traditional XPS and rock wool boards, buildings using PI/VIP composites achieve 21.3%, 34.7%, and 18.9% higher annual energy-saving efficiency respectively, with 27%-41% lower carbon emissions; the most significant effects in Heilongjiang highlight the material’s great promotion potential in severe cold areas.
Hybrid-electric (xHEV) and fuel cell electric vehicles (FCEVs) are expected to play a crucial role in the transition towards sustainable mobility in both the individual and commercial transportation sectors. As their market share increases, there is a need for advanced research to enhance overall vehicle efficiency – particularly through optimized energy management systems. For FCEVs, an optimal energy management strategy is essential to ensure safe and durable operation. For xHEVs, thermal management serves as a central lever for improving efficiency and controlling emissions, making it an integral part of the overall powertrain development process. Considering today’s regulatory landscape, these aspects must be addressed early in development. Consequently, a holistic methodological framework is required, enabling not only technical robustness but also economic benefits, such as reducing engineering effort through effective frontloading. This methodology is composed of integrated simulation and testing approaches to develop components, systems, and operation strategies for future vehicles. Building on component- and system-level evaluations conducted at a dedicated thermal system testbed (ThermoLab), vehicle-level testing is required to calibrate and validate the laboratory results. To bridge the gap between the testbed and real driving events, an innovative approach is developed to replicate essential real-world boundary conditions, with particular focus on thermal and hydraulic conditions. The combination of a dedicated low-temperature extension chamber and an innovative dynamic coolant conditioning unit enables the energy-efficient transfer of thermal and hydraulic boundary conditions to a classic chassis dynamometer that was previously incapable of low-temperature testing. While the dedicated low temperature extension chamber transfers low temperature boundary conditions to the vehicles surrounding, the dynamic conditioning unit (Dynamic Module III) enables the accurate reproduction of relevant temperatures within the vehicle’s powertrain. This study demonstrates an innovative approach for the energy-efficient transfer of real-world low-temperature boundary conditions on a chassis dynamometer incorporating low-temperature extension and dynamic conditioning units as part of a holistic development methodology.
The ongoing efforts for reduction of the traffic-related greenhouse gas emissions and, at the same time, the mitigation of harmful pollutant emissions from vehicle exhaust emissions are important development tasks for the entire automotive industry worldwide according to demand to provide clean and efficient products. Further tightened fleet average FE standards and ultra-low limits for exhaust emissions require the continuous development of new propulsion system types. Due to the given reluctance of the end customer and corresponding low acceptance of fully electrified vehicles, especially in the commercial vehicle segment, new and innovative topologies are needed to meet regulatory requirements and maintain the high versatility of today’s dominating solutions. For further optimization of operating conditions with enhanced fuel efficiency, the technical strategy is also determined by uplifting the attractiveness of electric driving incl. the avoidance of areas with poor ICE efficiency and as well as the coverage of emission-critical operations by electric propulsion. In this context, the support provided by an electric drive on board the vehicle in a combined drive system is becoming increasingly important. This article discusses accordingly various platform strategies for hybridized Diesel powertrains in different sectors of commercial vehicle applications and delivers a comprehensive comparative analysis of different hybrid drive concepts. Specifically, several hybrid powertrain configurations that extend an electric drive platform (hybridized BEVs), such as series and parallel-series topologies, are compared with traditional parallel hybrid powertrain topologies based on internal combustion engines (ICE). The study focuses mainly on two different cornerstone applications: a large light commercial vehicle, ranging from 3,5 to 6,5 to. and a heavy-duty long-haul truck with 40…44 to. gross vehicle weight. It evaluates the advantages in terms of CO2 emissions and Diesel fuel savings and investigates the effects on emission controls aspects. In addition to technical comparisons, the paper addresses also regulatory demands and end customer merits, assessing the integrational effort and commonalities in components with pure ICE and battery electric topologies. Furthermore, it explores the additional impact of advanced operational strategies for Hybrid Diesel powertrains, incorporating insights from innovative observations from executed hybrid technology demonstrator vehicles.
ZF foresees hybrid powertrain technology becoming more popular for commercial transport in the coming years, and it's working earnestly to be a major player in that realm. The supplier unveiled the TraXon 2 Hybrid transmission to the North American commercial vehicle market at last year's ACT Expo and is now evaluating the technology in real-world conditions. The next-gen automated manual transmission (AMT) is optimized to improve fuel efficiency for plug-in and full hybrid heavy-duty trucks and coaches, as well as special applications such as medium- to heavy-duty mobile cranes.
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Sustainability needs to be practical. That was a point Peter Voorhoeve, president of Volvo Trucks North America, made clear at CONEXPO 2026 in Las Vegas. “We're running a business, so we are focusing a lot on efficiency and uptime,” he said, referencing the up-to-10% improvement in fuel efficiency with the new VNL. “That helps our customers to run their operations at a better pace and a lower cost, but at the same time we have a very positive impact on the climate.” Voorhoeve also teased the launch of a new vocational truck. “We are strong in long haul. We are a leading sleeper manufacturer, very strong in regional haul, and we now have renewed focus on vocational,” he said. “In August we will launch a new truck specifically for the vocational segment that's built on the same platform as the VNL and VNR.” (See page 22 for our feature story on the new VNR.)
An advanced coupling framework was leveraged to assemble analytic sensitivities of lifting line theory aerodynamic loads with respect to externally-defined blade geometry parameters for optimization of main rotor performance of conventional helicopter configurations. Three vehicle weights and two flat-plate-equivalent drag configurations were examined across the flight envelope from hover to an advance ratio of 0.3. Two types of twist controls were investigated: quasi-static and fully active. Power savings were strongly correlated to the forward flight to hover power, ranging between 1.5 and 3.5% for quasi-static geometries and 2.0 and 4.5% for fully active controls when the installed power is twice of that required in hover. Blade twists optimized at higher power ratios were observed to favor high shaft tilt angles. Optimal twist deformation relative to hover-optimized designs is nonlinear across the blade span. Minimal penalties to aerodynamic vibrations were incurred through the use of either quasi-static or fully active twist controls as measured with a vibration intrusion index.
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
Aerodynamic simulations are crucial in vehicle design and performance evaluation. Traditionally, these simulations utilize Computational Fluid Dynamics (CFD) techniques to compute flow quantities such as velocity, pressure, and wall-shear stresses. Accurate prediction of these quantities is vital for estimating drag and lift forces, which directly impact fuel efficiency, stability, and acoustics. This study focuses on developing an AI surrogate for aerodynamic design of production mideo-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 102 different geometry variants at a uniform inlet velocity of 38.89 m/s and a fixed set of boundary conditions. The DoMINO (Decomposable Multiscale Iterative Neural Operator) AI model, part of the PhysicsNeMo framework, is then used to train on this dataset, accurately predicting surface pressure and flow fields around vehicles for rapid estimation of critical aerodynamic metrics such as drag and lift. DoMINO is a neural operator that learns local geometry representations from point cloud data and predicts PDE solutions on discrete points using dynamically constructed computational stencils in local regions. By leveraging both short- and long-range geometric features, the DoMINO model predicts solution fields on the vehicle’s surface and in the surrounding flow domain—capabilities essential for informed design and engineering decisions in industrial applications. In this study, the DoMINO model is evaluated on a realistic production mid-size SUV vehicle designed by General Motors. Comprehensive hyperparameter tuning is conducted to optimize model performance, along with an analysis of input grid sensitivity. The findings highlight DoMINO's effectiveness as a robust and accurate tool for aerodynamic analysis in the automotive sector, enabling accelerated design cycles and enhanced vehicle performance.
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to enhanced energy-efficiency, reduced component and system costs, and a cut-down in development time of EVs, enabled by digital-twin-based design methodologies. Beyond these performance gains, SmartCorners establishes the foundational principles of modularity, adaptability, and software integration that underpin the evolution toward SDVs. The role of thermal and cabin comfort control is completely different for EVs and internal combustion engine vehicles, with the latter using waste heat from the combustion of fossil fuels for cabin heating, ventilation, and cooling (HVAC). In EVs the required energy is directly taken from the traction battery and precise thermal and cabin comfort control affecting essential components of the vehicle but also the user-perceived driving experience. These project achievements highlight a critical bridge between innovation and electrification on component-level, and the holistic software-defined mobility systems of the future.
Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The testing aims to verify the system’s effectiveness in improving energy efficiency, clarify the cost of autonomous vehicles electrification, and identify the most cost-effective low-carbon fleet composition under different scenarios. The results demonstrate that ride-pooling system outperforms both ride-hailing and private vehicles. Ride-pooling achieves 15–25% lower carbon intensity and 18–25% energy savings compared to private vehicles. It is also found that EVs present, on average, an 8–12% higher trip rejection rate than ICE fleets, demonstrating that electrifying Robotaxis comes at the cost of reduced service levels or increased costs. The study ultimately finds that electrifying Robotaxis at a moderate level (40–60%) can achieve a good trade-off between environmental benefits, service quality, and cost.
Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density and mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia decomposition technology resolves this problem through on-demand hydrogen real-time production. Among existing ammonia decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for onboard hydrogen production given its high processing flow rate，decent hydrogen conversion rate, and transient response capability. Prevailing research predominantly relies on experimental approaches, with insufficient understanding of the effects of specific electrical field parameters and inlet pressure on system performance. This study established a quasi-one-dimensional numerical model for GAP-assisted ammonia decomposition. A comprehensive analysis was conducted to examine the influence of key electric field parameters, such as reduced electric field strength (REFS) and electron density (De), on ammonia conversion rate and energy efficiency. Furthermore, the study explored the synergistic effects of inlet pressure and electric field parameters on system performance under constant mass flow rate conditions. The results indicate that increasing REFS and De significantly substantially elevates ammonia conversion rate, but energy efficiency decreases as these parameters increase. Keeping a constant NH3 inlet mass flow rate, the gas velocity decreases when the inlet pressure increases and then extends the residence time. Consequently, the ammonia conversion rate significantly improves while the energy efficiency slightly decreases. By increasing inlet pressure and simultaneously reducing REFS or De, system energy efficiency can be effectively enhanced without altering ammonia conversion rates. This study demonstrates the synergistic regulation mechanism of electric field parameters and inlet pressure on hydrogen production performance, providing optimization strategies for GAP reactor design.
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