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The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

Prior work in the literature have shown that pre-chamber spark plug technologies can provide remarkable improvements in engine performance. In this work, three passively fueled pre-chamber spark plugs with different pre-chamber geometries were investigated using in-cylinder high-speed imaging of spectral emission in the visible wavelength region in a single-cylinder direct-injection spark-ignition gasoline engine. The effects of the pre-chamber spark plugs on flame development were analyzed by comparing the flame progress between the pre-chamber spark plugs and with the results from a conventional spark plug. The engine was operated at fixed conditions (relevant to federal test procedures) with a constant speed of 1500 revolutions per minute with a coolant temperature of 90 oC and stoichiometric fuel-to-air ratio. The in-cylinder images were captured with a color high-speed camera through an optical insert in the piston crown.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

Nyquist plots are a classical means to visualize a complex vibration frequency response function. By graphing the real and imaginary parts of the response, the dynamic behavior in the vicinity of resonances is emphasized. This allows insight into how modes are coupling, and also provides a means to separate the modes. Mathematical models such as Nyquist analysis are often embedded in frequency analysis hardware. While this speeds data collection, it also removes this visually intuitive tool from the engineer’s consciousness. The behavior of a single degree of freedom system will be shown to be well described by a circle on its Nyquist plot. This observation allows simple visual examination of the response of a continuous system, and the determination of quantities such as modal natural frequencies, damping factors, and modes shapes. Vibration test data from an auto rickshaw chassis are used as an example application.

High-pressure gasoline injection can improve combustion efficiency and lower engine-out emissions; however, the spray characteristics of high-pressure gasoline (>500 bar) are not well known. Effects of different injector nozzle geometry on high-pressure gasoline sprays were studied using a constant volume chamber. Five nozzles with controlled internal flow features including differences in nozzle inlet rounding, conicity, and outlet diameter were investigated. Reference grade gasoline was injected at fuel pressures of 300, 600, 900, 1200, and 1500 bar. The chamber pressure was varied using nitrogen at ambient temperature and pressures of 1, 5, 10, and 20 bar. Spray development was recorded using diffuse backlit shadowgraph imaging methods.

This study experimentally investigates the impact of Miller cycle strategies on the combustion process, emissions, and thermal efficiency in heavy-duty diesel engines. The experiments were conducted at constant engine speed, load, and engine-out NOx (1160 rev/min, 1.76 MPa net IMEP, 4.5 g/kWh) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Early Intake Valve Closing (EIVC) and Late Intake Valve Closing (LIVC) timing strategies were compared to a conventional intake valve profile. While the decrease in effective compression ratio associated with the use of Miller valve profiles was symmetric around bottom dead center, the decrease in volumetric efficiency (VE) was not. EIVC profiles were more effective at reducing VE than LIVC profiles. Despite this difference, EIVC and LIVC profiles with comparable VE decrease resulted in similar changes in combustion and emissions characteristics.

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COVimep). The classes are; no ignition/high COVimep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz.

An experimental investigation of non-intrusive combustion sensing was performed using a tri-axial accelerometer mounted to the engine block of a small-bore high-speed 4-cylinder compression-ignition direct-injection (CIDI) engine. This study investigates potential techniques to extract combustion features from accelerometer signals to be used for cycle-to-cycle engine control. Selection of accelerometer location and vibration axis were performed by analyzing vibration signals for three different locations along the block for all three of the accelerometer axes. A magnitude squared coherence (MSC) statistical analysis was used to select the best location and axis. Based on previous work from the literature, the vibration signal filtering was optimized, and the filtered vibration signals were analyzed. It was found that the vibration signals correlate well with the second derivative of pressure during the initial stages of combustion.

Innovations in mobility are built upon a management of complex interactions between sub-systems and components. A need for CAE tools that are capable of system simulations is well recognized, as evidenced by a growing number of commercial packages. However impressive they are, the predictability of such simulations still rests on the representation of the base components. Among them, a wet clutch actuator continues to play a critical role in the next generation propulsion systems. It converts hydraulic pressure to mechanical force to control torque transmitted through a clutch pack. The actuator is typically modeled as a hydraulic piston opposed by a mechanical spring. Because the piston slides over a seal, some models have a framework to account for seal friction. However, there are few contributions to the literature that describe the effects of seals on clutch actuator behaviors.

A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes Coulomb friction for simplicity, even though it does not represent the physics of hydrodynamic torque transfer. In practice, the Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, recent examination of friction material shows that the elastic modulus of surface fibers significantly exceeds the contact load, implying no deformation of fibers.

The article solves the problem of reducing electric power losses of the traction induction machine rotor to prevent its overheating in nominal and high-load modes. Electric losses of the rotor power are optimized by the stabilization of the main magnetic flow of the electric machine at a nominal level with the amplitude-frequency control in a wide range of speeds and increased loads. The quasi-independent excitation of the induction machine allows us to increase the rigidity of mechanical characteristics, decrease the rotor slip at nominal loads and overloads and significantly decrease electrical losses in the rotor as compared to other control methods. The article considers the technology of converting the power of individual phases into a single energy flow using a three-phase electric machine equivalent circuit and obtaining an energy model in the form of equations of instantaneous active and reactive power balance.

This paper presents an experimental investigation of the impact of EGR dilution on the tradeoff between flame and end-gas autoignition heat release in a Spark-Assisted Compression Ignition (SACI) combustion engine. The mixture was maintained stoichiometric and fuel-to-charge equivalence ratio (ϕ′) was controlled by varying the EGR dilution level at constant engine speed. Under all conditions investigated, end-gas autoignition timing was maintained constant by modulating the mixture temperature and spark timing. Experiments at constant intake pressure and constant spark timing showed that as ϕ′ is increased, lower mixture temperatures are required to match end-gas autoignition timing. Higher ϕ′ mixtures exhibited faster initial flame burn rates, which were attributed to the higher laminar flame speeds immediately after spark timing and their effect on the overall turbulent burning velocity.

This work investigates the potential improvements in vehicle fuel economy possible by optimizing gear shift strategies to leverage a novel boosting device, an electrically assisted variable speed supercharger (EAVS), also referred to as a power split supercharger (PSS). Realistic gear shift strategies, resembling those commercially available, have been implemented to control upshift and downshift points based on torque request and engine speed. Using a baseline strategy from a turbocharged application of a MY2015 Ford Escape, a vehicle gas mileage of 34.4 mpg was achieved for the FTP75 drive cycle before considering the best efficiency regions of the supercharged engine.

The Particulate Matter Index (PMI) is a helpful tool which provides an indication of a fuel’s sooting tendency. Currently, the index is being used by various laboratories and OEMs as a metric to understand the gasoline fuels impact on both sooting found on engine hardware and vehicle out emissions. This paper will explore a new method that could be used to give indication of the sooting tendency of the gasoline range fuels, called the Particulate Evaluation Index (PEI), and provide the detailed equation in its initial form. In addition, the PEI will be shown to have a good correlation agreement to PMI. The paper will then give a detailed explanation of the data used to develop it. Initial vehicle PM/PN data will also be presented that shows correlations of the indices to the vehicle response.

Simulations conducted on-board in a vehicle control module can offer valuable information to control strategies. Continued improvements to on-board computing hardware make online simulations of complex dynamic systems such as drivetrains within reach. This capability enables predictions of the system response to various control actions and disturbances. Implementation of online simulations requires model initialization that is consistent with the physical drivetrain state. However, sensor signals and estimated variables are susceptible to errors, compromising the accuracy of the initialization and any future state predictions as the simulation proceeds through the numerical integration process. This paper describes a drivetrain modeling and analysis method that accounts for initialization errors, thereby enabling accurate simulations of system behaviors.

As European and Chinese tailpipe emission regulations for gasoline light-duty vehicles impose particulate number limits, automotive manufacturers have begun equipping some vehicles with a gasoline particulate filter (GPF). Increased understanding of how soot morphology, reactivity, and GPF loading affect GPF filtration and regeneration characteristics is necessary for advancing GPF performance. This study investigates the impacts of morphology, reactivity, and filter soot loading on GPF filtration and regeneration. Soot morphology and reactivity are varied through changes in fuel injection parameters, known to affect soot formation conditions. Changes in morphology and reactivity are confirmed through analysis using a transmission electron microscope (TEM) and a thermogravimetric analyzer (TGA) respectively.

Early exhaust valve opening (eEVO) increases the exhaust gas temperature by faster termination of the power stroke and is considered as a potential warm up strategy for diesel engines aftertreatment thermal management. In this study, first, it is shown that when eEVO is applied, the engine main variables such as the boost pressure, exhaust gas recirculation (EGR) and injection (timing and quantity) must be re-calibrated to develop the required torque, avoid exceeding the exhaust temperature limits and keep the air fuel ratio sufficiently high. Then, a two-step procedure is presented to optimize the engine operation after the eEVO system is introduced, using a validated diesel engine model. In the first step, the engine variables are optimized at a constant eEVO shift. In the second step, optimal eEVO trajectories are calculated using Dynamic Programming (DP) for a transient test cycle.

Electric power assisted steering (EPAS) systems are widely adopted in modern vehicles to reduce the steering effort of drivers. In rack EPAS, assist torque is applied by a motor and transmitted through two key mechanical components: ball bearing and ball nut assembly (BNA) to turn the front wheels. Large combined load and manufacturing errors not only make it hard to accurately calculate the load distribution in the ball bearing and BNA for the purpose of sizing, but also make the friction behavior of EPAS gear complicated. Rack EPAS gear is well known to suffer from “stick-slip” (i.e., sticky feel sensed by the driver), which affects the user experience. “Stick-slip” is an extreme case of friction variation mainly coming from ball bearing and BNA. Finite Element Analysis (FEA) in commercial software like ANSYS is usually conducted to study the load distribution and friction of ball bearing and BNA.