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Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

The absence of combustion information continues to be one of the key obstacles to the intelligent development of engines. Currently, the cost of integrating cylinder pressure sensors remains too high, prompting attention to methods for extracting combustion information from existing sensing data. Mean-value combustion models for engines are unable to capture changes of combustion parameters. Furthermore, the methods of reconstructing combustion information using sensor signals mainly depend on the working state of the sensors, and the reliability of reconstructed values is directly influenced by sensor malfunctions. Due to the concentration of operating conditions of hybrid vehicles, the reliability of priori calibration map has increased. Therefore, a combustion information reconstruction method based on priori calibration information and the fused feature deviations of existing sensing signals is proposed and named the "Deviation-based Centroid Displacement Method" (DCDM).

It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

Engine knock is a major challenge that limits the achievement of higher engine efficiency by increasing the compression ratio of the engine. To address this issue, using a higher octane number fuel can be a potential solution to reduce or eliminate the propensity for knock and so obtain better engine performance. Methanol, a promising alternative fuel, can be produced from conventional and non-conventional energy resources, which can help reduce pollutant emissions. Methanol has a higher octane number than typically gasolines, which makes it a viable option for reducing knock intensity. This study compared the combustion characteristics of gasoline and methanol fuels in an optical spark-ignition engine using multiple spark plugs. The experiment was carried out on a single-cylinder four-stroke optical engine. The researchers used a customized metal liner with four circumferential spark plugs to generate multiple flame kernels inside the combustion chamber.

By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy.

Ammonia (NH3) is a carbon-free fuel, which could partially or completely eliminate hydrocarbon (HC) fuel demand. Using ammonia directly as a fuel has some challenges due to its low burning speed and low flammability range, which generates unstable combustion inside the combustion chamber. This study investigated the effect of two different compression ratios (CRs) of 10.5 and 12.5 on the performance of ammonia combustion by using a conventional single spark-ignition (SI) approach. It was found that at a lower CR of 10.5, the combustion was unstable even at advanced spark timing (ST) due to poor combustion characteristics of ammonia. However, increasing the CR to 12.5 improved the engine performance significantly with lower cyclic variations. In addition, this research work also observed the effect of multiple spark ignition strategies on pure ammonia combustion and compared it with the conventional SI approach for the same operating conditions.

Traditionally vehicles are designed for wind noise under ideal steady wind conditions. But, passenger comfort is affected by high modulation of cabin noise while cruising in traffic due to variations of instantaneous wind speed and direction from driving through large-scale turbulence. In consequence, designing a vehicle for the best performance in a low-turbulence wind tunnel may lead to issues during on-road conditions. To predict the interior noise corresponding to on-road turbulence, a simulation approach is proposed combining an upstream turbulence flow simulation with an SEA vehicle model. This work is an extension of existing well validated procedures for steady wind conditions. Time-segmented transient loads on panels and steady-state structural acoustics transfer functions are combined, producing interior noise results for a series of overlapping time segments.

As lithium-ion electric vehicle (EV) batteries are sensitive to the conditions they are exposed to during charging and discharging, operational control has been an important research area. While an understanding of the effects current load and operation temperature has on the ageing stability of a battery has been established, associated control strategies are yet to be fully optimized. Most battery charging studies utilize controlled ambient temperatures and basic defined cycles, which may only apply to a small subset of real-world EV consumers. This leads to control strategies that do not consider electrical grid price fluctuation, user driving habits or local weather conditions. This paper looks to propose improved smart charging strategies of EVs to reduce consumer costs while also increasing the battery longevity. To accomplish the primary objective, A model has been generated that simulates the standard charge cycle of a battery.

Advanced driver assistance systems rely on external sensors that encompass the vehicle. The reliability of such systems can be compromised by adverse weather, with performance hindered by both direct impingement on sensors and spray suspended between the vehicle and potential obstacles. The transportation of road spray is known to be an unsteady phenomenon, driven by the turbulent structures that characterise automotive flow fields. Further understanding of this unsteadiness is a key aspect in the development of robust sensor implementations. This paper outlines an experimental method used to analyse the spray ejected by an automotive body, presented through a study of a simplified vehicle model with interchangeable rear-end geometries. Particles are illuminated by laser light sheets as they pass through measurement planes downstream of the vehicle, facilitating imaging of the instantaneous structure of the spray.

Some car shapes produce a substantial drag component from the generation of trailing vortices. This vortex (or lift dependent) drag is difficult to quantify for the whole vehicle, for reasons that are discussed. It has previously been shown that vortex drag may be assessed for some car features by consideration of the relationship between changes in drag and lift. In this paper this relationship is explored for some different vehicle shape characteristics, which produce positive and negative lift changes, and their combinations. Vortex drag factors are determined and vortex drag coefficients considered. An interference effect is identified between some of these features. For the simple bodies investigated the vortex drag contribution can be considerable.

Hybrid powertrain has been proven to be an effective fuel-saving technology in commercial vehicles, but many hybrid commercial vehicles still use conventional diesel engines, resulting in limited fuel savings. The main purpose of this study is to enhance the thermal efficiency of a dedicated hybrid diesel engine focusing on the characteristic operating conditions. Via fundamental thermodynamics process analysis of internal combustion engine, steel piston with high compression ratio, air system involving two-stage turbocharger(2TC) with an intercooler, and late intake valve closing(IVC) timing are proposed to improve the thermal efficiency of the engine. Experimental results show that high compression ratio and lower thermal conductivity of the combustion chamber surface lead to lower heat release rates, requiring optimization of piston profile to accelerate the mixing rate. Besides, high compression ratio also leads to higher mechanical losses.

The commercial vehicle development process needs to consider the vehicle aerodynamics not only in ideal flow conditions, but also in the turbulent real world environment. The turbulent real world environment includes not only atmospheric turbulence, but also the vehicle to vehicle interactions that happen when driving around other vehicles or into and out of the wake of in/on coming vehicles. A vehicle driving into the wake of an oncoming vehicle not only experiences an increase in the total aerodynamic forces, it also experiences unsteady transient loads over the vehicle components such as windshield, mirror, sunvisor, door and side fairing. To properly design specific components, designers need to understand the magnitude of unsteady forces on various vehicle components, otherwise these components may fail which imposes warranty and safety risks. In this paper, we attempt to understand the various forces acting on the primary vehicle during a passing maneuver.

Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form.

Hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology leads to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology [1]. In this paper, a previously proposed energy management strategy based on an offline optimization using dynamic programming is investigated. This is then coupled with an online model predictive control strategy to follow the predetermined optimal battery state of charge trajectory prescribed by the dynamic program.

For fuel cell vehicles, it is essential that the hydrogen tank be both compact and have sufficient hydrogen to ensure reasonable driving range for which there is a need to pressurize the hydrogen in the tank at levels much higher than that of atmospheric pressure. Furthermore, fast filling is an important consideration in order to minimize time to refuel hydrogen in the tank. In this article, we investigate a Computational Fluid Dynamics (CFD) methodology to see whether we can simulate the fast filling of the hydrogen tank. We performed simulations on an existing validation case using coupled simulation approach between the PowerFLOW® flow solver and PowerTHERM® the thermal solver. For an accurate simulation at elevated pressure levels, we implemented a real gas behavior that is more accurate than the ideal gas equation of state for under these conditions. We observe good agreement with experimental data for both bulk and local variations in temperature.

A drag coefficient, which is representative of the drag of a car undergoing a particular drive cycle, known as the cycle-averaged-drag coefficient, has been previously developed. It was derived for different drive cycles using mean values for the natural wind. It assumed terrain dependent wind velocities based on the Weibull function, equi-probable wind direction and shear effects. It did not, however, include any effects of turbulence in the natural wind. Some recent research using active vanes in the wind tunnel to generate turbulence has suggested that the effect on drag can be evaluated from the quasi steady wind inputs. On this basis a simple quasi-steady theory for the effect of turbulence on car drag is developed and applied to predicting the cycle-averaged-drag coefficient for a range of cars of different types. The drag is always increased by the turbulence but in all cases is relatively small.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.

Selective Catalytic Reduction (SCR) is a process where one injects an aqueous solution of urea into a diesel exhaust system in order to reduce NOx emissions. The urea solution known as AdBlue® or Diesel Exhaust Fluid (DEF) is stored in a DEF Tank that can under cold weather conditions freeze over. Since AdBlue® is unusable while frozen, we use heaters installed in the tanks to melt AdBlue® with government regulations mandating time required to melt AdBlue® in the tank. In this article, we investigate whether a CFD (Computational Fluid Dynamics) based methodology can accurately evaluate time required in melting AdBlue® for a given DEF Tank and heater coil design for a production vehicle as per standard testing procedure. Simulations used a coupled methodology with PowerFLOW® as the flow solver and PowerTHERM® as the thermal solver. The flow simulation did require an accurate modelling of phase change from solid to liquid for AdBlue®.