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Abstract Carbon fiber reinforced plastic (CFRP) has been used in automobiles as well as airplanes. Because of its light weight and high strength, CFRP is a good choice for making vehicle bodies lighter, which would improve fuel economy. Conventional metal bodies provide a convenient body return for electric wiring and offer good shielding against electromagnetic fields. Although CFRP is a conductor, its conductivity is much lower than that of metals. Therefore, CFRP bodies are usually not useful for electric wiring. In thunderstorms, an automotive body is considered to be a Faraday cage that protects the vehicle’s occupants from the potential harms of lightning. Before CFRP becomes widely applied to automotive bodies, its electric and electromagnetic properties need to be investigated in order to determine whether it also works as a Faraday cage against lightning. In this article, CFRP and metal body vehicles were tested under artificial lightning.

Abstract The objective of this work is to develop an integrated HVAC-VEHICLE model for climate control studies. A published lumped parameter based HVAC model has been used as the framework for the HVAC modeling with some modifications to realize the climate control and to improve the robustness of the model. R134a (1,1,2,2-Tetrafluoroethane) has been used as the refrigerant fluid in this study. The stand-alone HVAC model has been compared qualitatively with the experimental works available in the literature. The experimental trends of the thermodynamic and performance related parameters of HVAC are reasonably well captured by the HVAC model. In particular, Coefficient of Performance (CoP) was found to decrease with increase in compressor speed and increase in ambient temperature but increase with increase in evaporator blower mass flow rate.

Abstract The mechanical efficiency of the current continuously variable transmission (CVT) suffers from high pump loss induced by a high-pressure system. A novel wedge mechanism is designed into the CVT clamp actuation system to generate the majority of clamp force mechanically. Therefore, the hydraulic system can operate at a low-pressure level most of the time, and the pump loss is greatly reduced to improve the CVT’s mechanical efficiency. Through dynamic analysis and design optimization, 90% of clamp force is contributed by the wedge mechanism and the rest of the 10% is generated by a conventional hydraulic system. The optimal design is validated through dynamic modeling using Siemens Virtual.Lab software by simulating the wedge clamp force generation, ratio change dynamics, and system response under tip-in conditions. After that, we built prototype components that target 70% of the clamp force contributed by the wedge mechanism and tested them on a transmission dynamometer.

Abstract To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, “U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards” identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis.

Abstract Vehicle rolling resistance and weight are two of the factors that affect fuel economy. The vehicle tire rolling resistance has a more significant influence than aerodynamics drags on fuel economy at lower vehicle speeds, particularly true for medium- and heavy-duty trucks. Less vehicle weight reduces inertia loads, uphill grade resistance, and rolling resistance. The influence of weight on the fuel economy can be considerable particularly in light- to medium-duty truck classes because the weight makes up a larger portion of gross vehicle weight. This article presents an empirical investigation and a numerical analysis of the influences of rolling resistance and weight on the fuel economy of medium-duty trucks. The experimental tests include various tires and payloads applied on a total of 21vehicle configurations over three road profiles. These tests are used to assess the sensitivity of rolling resistance and weight to the vehicle fuel economy.

Abstract With increased consumer demand for fuel efficient vehicles as well as more stringent greenhouse gas regulations and/or Corporate Average Fuel Economy (CAFE) standards from governments around the globe, the automotive industry, including the OEM (Original Equipment Manufacturers) and suppliers, is working diligently to innovate in all areas of vehicle design. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, mass reduction has been identified as an important strategy in future vehicle development. In this article, the development, analysis, and experiment of multi-cornered structures are presented. To achieve mass reduction, two non-traditional multi-cornered structures, with twelve- and sixteen-cornered cross-sections, were developed separately by using computer simulations.

Abstract When commercial vehicles drive in a mountainous area, the complex road condition and long slopes cause frequent acceleration and braking, which will use 25% more fuel. And the brake temperature rises rapidly due to continuous braking on the long-distance downslopes, which will make the brake drum fail with the brake temperature exceeding 308°C [1]. Meanwhile, the kinetic energy is wasted during the driving progress on the slopes when the vehicle rolls up and down. Our laboratory built a model that could calculate the distance from the top of the slope, where the driver could release the accelerator pedal. Thus, on the slope, the vehicle uses less fuel when it rolls up and less brakes when down. What we do in this article is use this model in a real vehicle and measure how well it works.

Abstract The pursuit of improved fuel economy through weight reduction, reduced manufacturing costs, and improved crash safety can result in increased compliance in automobile structures. However, with compliance comes an increased susceptibility to aerodynamic and vibratory loads. The hood in particular withstands considerable aerodynamic force at highway speeds, creating the potential for significant aeroelastic response that may adversely impact customer satisfaction and perception of vehicle quality. This work seeks an improved understanding in computational and experimental study of fluid-structure interactions between automobile hoods and the surrounding internal and external flow. Computational analysis was carried out using coupled CFD-FEM solvers with detailed models of the automobile topology and structural components. The experimental work consisted of wind tunnel tests using a full-scale production vehicle.

Abstract Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.

Abstract TOC

Abstract In this article, an adaptive state estimation algorithm for precise air-fuel ratio (AFR) control is presented. AFR control is a critical part of internal combustion engine (ICE) control, and tight AFR control delivers lower engine emissions, better engine fuel economy, and better engine transient performance. The proposed control algorithm significantly improves transient AFR control to eliminate and reduce the amplitude of the lean and rich spikes during transients. The new algorithm is first demonstrated in simulation (using Matlab/SimulinkTM and GT-PowerTM) and then verified on a test engine. The engine tests are conducted using the European Transient Cycle (ETC) with HoribaTM double-ended dynamometer. The developed algorithm utilizes a nonlinear physics-based engine model in the observer and advanced control principles with modifications to solve real industrial control issues.

Abstract The principal issue in choosing an energy management strategy (EMS) for hybrid electric vehicles (HEVs) has been the way of determining the optimal share of electric energy in hybrid drive. In this article, a novel EMS is proposed that, along with maximum engine efficiency in the hybrid drive, can optimize the share of battery energy for the maximum efficiency of vehicle power train expanded with an imaginary power plant that, by delivering the electric energy to a grid, feeds the vehicle battery. It is proved that the expanded power train efficiency has the local maximum for a wide range of wheel power demand. The relation between the wheel power demand in hybrid drive, the share of battery energy, and the maximum efficiency of the expanded power train is conducted offline. Downloaded to the onboard control system, it enables the operation with the instantaneously optimal share of battery energy and the control system to operate with the low computational load.

Abstract Diesel trucks play a crucial role in transportation activity and a major contributor to fuel consumption and air pollution. To improve the energy efficiency of diesel trucks, we develop a truck eco-approach and departure (EAD) system based on Signal Phase and Timing (SPaT) message from signal controllers and road grade information along the path. The proposed model consists of two levels—the lower level for powertrain-based fuel consumption estimation and the upper level for optimal trajectory planning. The powertrain model is designed for a diesel engine with a six-gear transmission and well calibrated using on-board Electronic Control Unit (ECU) data. The trajectory planning model is formulated as a shortest path problem with the combination of time, distance, and speed as the state on each node and fuel consumption rate as the cost on state transition.

Abstract An energy management strategy based on model predictive control (MPC) was proposed for the hybrid bus. For the series configuration, MPC was used for power distribution among transmission components. Real-time optimization of the control strategy was achieved, which improved the fuel economy. First, a rule-based energy management strategy was proposed, and the logical thresholds of the stage of charge (SOC) and the demand power were formulated to underlie the subsequent study of the control strategy. Second, an energy management strategy based on global optimization was established where the dynamic programming algorithm was used to determine the SOC optimal reference curve and the limitation of fuel economy. In this way, the target and reference can be provided for the subsequent control strategy. Third, a radial basis neural network speed prediction model based on wavelet transform was formulated.

Abstract Regulations limiting GreenHouse Gases (GHG) from Heavy-Duty (HD) commercial vehicles in the United States (US) and European Union will phase in between the 2024 and 2030 model years. These mandates require efficiency improvements at both the engine and vehicle levels, with the most stringent reductions required in the heaviest vehicles used for long-haul applications. At the same time, a 90% reduction in oxides of nitrogen (NOx) will be required as part of new regulations from the California Air Resources Board. Any technologies applied to improve engine efficiency must therefore not come at the expense of increased NOx emissions. Research into advanced engine architectures and components has identified improved turbomachine efficiency as one of the largest potential contributors to engine efficiency improvement. However this comes at the cost of a reduced capability to drive high-pressure Exhaust Gas Recirculation (EGR).

Abstract Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by utilizing two different power sources: an internal combustion engine and an electrical motor. The power distribution between these two components must be controlled using some algorithm, be it rule based, optimization based, or reinforcement learning based. In the design of such control algorithms, it is important to evaluate the impact that variations of certain design parameters will have on the system performance, in this case, fuel economy. Traditional methods of sensitivity analysis have been applied to various power flow control algorithms to determine their robustness to the variations of HEV design parameters. This article presents a sensitivity analysis of three power flow control algorithms: twin delayed deep deterministic policy gradient (TD3), deep deterministic policy gradient (DDPG), and adaptive equivalent consumption minimization strategy (A-ECMS).

Abstract The article describes the results achieved in developing a new diesel combustion system for passenger car application that, while capable of high power density, delivers excellent fuel economy through a combination of mechanical and thermodynamic efficiencies improvement. The project stemmed from the idea that, by leveraging the high fuel injection pressure of last generation common rail systems, it is possible to reduce the engine peak firing pressure (pfp) with great benefits on reciprocating and rotating components’ light-weighting and friction for high-speed light-duty engines, while keeping the power density at competitive levels. To this aim, an advanced injection system concept capable of injection pressure greater than 2500 bar was coupled to a prototype engine featuring newly developed combustion system. Then, the matching among these features has been thoroughly experimentally examined.

Abstract Insulating combustion chamber surfaces with thermal barrier coatings (TBCs) provides thermal efficiency improvement when done appropriately. This article reports on insulation heat transfer, engine performance characteristics, and damage modelling of “temperature swing” TBCs. “Temperature swing” insulation refers to the insulation material applied on surfaces of combustion chamber walls that enables selective manipulation of its surface temperature profile over the four strokes of an engine cycle. A combined GT Suite-ANSYS Fluent simulation methodology is developed to investigate the impact of thermal properties and insulation thickness for a variety of TBC materials for its “temperature swing” characteristics. This one-dimensional transient heat conduction analyses and engine cycle simulations are performed using scaled-down thermal properties of yttria-stabilized zirconia.

Abstract Addition of hydrogen to hydrocarbons in premixed turbulent combustion is of technological interest due to their increased reactivity, flame stability and extended lean extinction limits. However, such flames are a challenge to reaction modelling, especially as the strong preferential diffusion effects modify the physical processes, which are of importance even for highly turbulent high-pressure conditions. In the present work, Reynolds-averaged Navier-Stokes (RANS) modelling is carried out to investigate pressure and hydrogen content on methane/hydrogen/air flames.

Abstract In order to meet the worldwide increasingly stringent particulate matter (PM) and particulate number (PN) emission limits, the diesel particulate filter (DPF) is widely used today and has been considered to be an indispensable feature of modern diesel engines. To estimate the soot loading amount in the DPF accurately and in real-time is a key function of realizing systematic and efficient applications of diesel engines, as starting the thermal regeneration of DPF too early or too late will lead to either fuel economy penalty or system reliability issues. In this work, an open-loop and on-line approach to estimating the DPF soot loading on the basis of soot mass balance is developed and experimentally investigated, through establishing and combining prediction models of the NOx and soot emissions out of the engine and a model of the catalytic soot oxidation characteristics of passive regeneration in the DPF.