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The human body changes as it becomes older. The automotive safety community has been interested in understanding the effect of aging on restraint performance. Recent research has been focused on assessing the structural and material changes associated with age. In this study, structural tissue distribution was determined using the computed tomography (CT) scan data of more than 19,000 patients, aged 16 and up. The data consisted of subcutaneous fat cross-sectional area, visceral fat cross-sectional area, and trabecular bone density taken at each vertebral level. The data was quantified as a function of five age groups with the youngest group defined as 16-29 years old and the oldest group as 75 and up. An additional analysis stratified on gender was carried out. Overall, visceral fat increased with age.

The influence of boost pressure (Pin) and fuel chemistry on combustion characteristics and performance of homogeneous charge compression ignition (HCCI) engine was experimentally investigated. The tests were carried out in a modified four-cylinder direct injection diesel engine. Four fuels were used during the experiments: 90-octane, 93-octane and 97-octane primary reference fuel (PRF) blend and a commercial gasoline. The boost pressure conditions were set to give 0.1, 0.15 and 0.2MPa of absolute pressure. The results indicate that, with the increase of boost pressure, the start of combustion (SOC) advances, and the cylinder pressure increases. The effects of PRF octane number on SOC are weakened as the boost pressure increased. But the difference of SOC between gasoline and PRF is enlarged with the increase of boost pressure. The successful HCCI operating range is extended to the upper and lower load as the boost pressure increased.

Exhaust Gas Recirculation (EGR) is an important parameter for control of diesel engine combustion, especially to achieve ultra low NOx emissions. In this paper, the effects of EGR on engine emissions and engine efficiency have been investigated in a heavy-duty diesel engine while changing combustion control parameters, such as injection pressure, injection timing, boost, compression ratio, oxygenated fuel, etc. The engine was operated at 1400 rpm for a cycle fuel rate of 50mg. The results show that NOx emissions strongly depend on the EGR rate. The effects of conventional combustion parameters, such as injection pressure, injection timing, and boost, on NOx emissions become small as the EGR rate is increased. Soot emissions depend strongly on the ignition delay and EGR rate (oxygen concentration). Soot emissions can be reduced by decreasing the compression ratio, increasing the injection pressure, or burning oxygenated fuel.

The influence of different combustion chamber configuration, intake temperature, and coolant temperature on HCCI combustion processes were investigated in a single-cylinder optical engine. Two-dimensional images of the chemiluminescence were captured using an intensified CCD camera in order to understand the spatial distribution of the combustion. N-heptane was used as the test fuel. Three combustion chamber geometries with different squish lip, salient, orthogonal, reentrant shape, referred as V-type, H-type, and A-type respectively, were used in this study. Intake temperature was set to 65°C and 95°C, while coolant temperature was set to 85°C. The experimental data consisting of the in-cylinder pressure, heat release rate, chemiluminescence images all indicated that the different combustion chamber geometries result in different turbulence intensity in the combustion chamber, and thus affect the auto-ignition timing, chemiluminescence intensity, and combustion processes.

Based on the sample data obtained from the bench test of a four-cylinder naturally aspirated CNG engine, three different machine learning models, BP, SVM and GRNN, were used to develop the intake charge prediction model for the intake system of this engine, in which engine speed, intake manifold pressure and intake temperature, VVT angle and gas injection time were taken as input parameters and intake charge was used as output parameter. The comparative analysis of the experimental data and model prediction data showed that the mean absolute error (MAE) of BP model, GRNN model, and SVM model were 2.69, 8.11and 5.13, and the root mean square error (MSE) were 3.53, 9.29, and 7.17, respectively. BP model has smaller prediction error and higher accuracy than SVM and GRNN models, which is more suitable for the prediction of the intake charge of this type of four-cylinder naturally aspirated CNG engine.

With the evolution of telemetry technology in vehicles, Advanced Automatic Collision Notification (AACN), which detects occupants at risk of serious injury in the event of a crash and triages them to the trauma center quickly, may greatly improve their treatment. An Injury Severity Prediction (ISP) algorithm for AACN was developed using a logistic regression model to predict the probability of sustaining an Injury Severity Score (ISS) 15+ injury. National Automotive Sampling System Crashworthiness Data System (NASS-CDS: 1999-2015) and model year 2000 or later were filtered for new case selection criteria, based on vehicle body type, to match Subaru vehicle category. This new proposed algorithm uses crash direction, change in velocity, multiple impacts, seat belt use, vehicle type, presence of any older occupant, and presence of any female occupant.

As an excellent nanoscale material, carbon nanotubes (CNTs) play a very important role in improving the batteries of new energy vehicles. The micro-scale combustion flame synthesis method is a promising method for preparing carbon nanotubes. To explore the optimal growth condition of carbon nanotubes under micro-scale combustion, the detailed mechanism of methanol C3 (114 species, 1999 reactions) was reduced based on whole-species sensitivity analysis, then a suitable model of methanol combustion was established by using Fluent software coupling with simplified mechanism (16 species, 65 reactions) of methanol. The model was used for the numerical simulation of micro-scale coaxial diffusion combustion of methanol, and then it was verified by the experimental results of micro-scale combustion of methanol.

Electric motor whine is one of the main noise sources of hybrid and electric vehicles. Motor air gap eccentricity due to propulsion system deflection, part tolerances and manufacturing variation is typically ignored in motor NVH design and analysis. Such eccentricity can be a dominant noise source by amplifying critical motor whine orders up to 10 dB, leading to poor NVH robustness. However, this problem cannot be explained by conventional method based on symmetric 2D approach. New 3D electromagnetic (EM) and NVH analyses are developed and validated to accurately predict air gap induced motor noise to enhance NVH robustness: First, a true 3D full 360-degree electric motor model is developed to model asymmetric air gap distribution along motor stack length. Predicted 3D EM forces are mapped to mechanical finite-element mesh over the cylindrical stator surface.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

Electric motor is among the main sources of noise and vibration for electrified propulsion systems. This paper focuses on the electric motor rotor NVH optimization, which is identified as a key enabler to reduce the motor whine, and balances other performance such as motor torque and efficiency. First, conventional rotor NVH design technologies such as rotor skew and asymmetric rotor pole-to-pole design are discussed, along with their associated tradeoff including reduced motor torque and additional sideband orders. Next, a special notch feature is proposed on the rotor surface with one notch per pole at every q-axis. A DOE study leads to the optimal notch design which significantly reduces the dominant motor torque ripple order by up to 20 dB, with minimum impact to motor torque or loss. Further design studies are then performed to explore additional d-axis notches which are symmetrically located within the top layer magnet opening angles.

In electric vehicle applications, the majority of the traction motors can be categorized as Permanent Magnet (PM) motors due to their outstanding performance. As indicated in the name, there are strong permanent magnets used inside the rotor of the motor, which interacts with the stator and causes strong magnetic pulling force during the assembly process. How to estimate this magnetic pulling force can be critical for manufacturing safety and efficiency. In this paper, a full 3D magnetostatic model has been proposed to calculate the baseline force using a dummy non-slotted cylinder stator and a simplified rotor for less meshing elements. Then, the full 360 deg model is simplified to a half-pole model based on motor symmetry to save the simulation time from 2 days to 2 hours. A rotor position sweep was conducted to find the maximum pulling force position. The result shows that the max pulling force happens when the rotor is 1% overlapping with the stator core.

Pulse Width Modulation or PWM has been widely used in traction motor control for electric propulsion systems. The associated switching noise has become one of the major NVH concerns of electric vehicles (EVs). This paper presents a multi-disciplinary study to analyze and validate current ripple induced switching noise for EV applications. First, the root cause of the switching noise is identified as high frequency ripple components superimposed on the sinusoidal three-phase current waveforms, due to PWM switching. Measured phase currents correlate well with predictions based on an analytical method. Next, the realistic ripple currents are utilized to predict the electro-magnetic dynamic forces at both the motor pole pass orders and the switching frequency plus its harmonics. Special care is taken to ensure sufficient time step resolution to capture the ripple forces at varying motor speeds.