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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.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

A research scheme and preliminary results of a pilot study concerning upper body coordination in reach movements is presented. Techniques for multi-joint arm movements were used to obtain the kinematics of each body segment in reach movements to targets spatially distributed in a horizontal plane. Further understanding of the control mechanisms associated with coordination is investigated by combining the information of gaze orientation and body segment movements during reach activities. The implicit sequence of body segments in reach movement can be derived from their kinematic characteristics. Moreover, an identification of phases composing a reach movement is attempted.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Plenoptic particle tracking velocimetry (PTV) shows great potential for three-dimensional, three-component (3D3C) flow measurement with a simple single-camera setup. It is therefore especially promising for applications in systems with limited optical access, such as internal combustion engines. The 3D visualization of a plenoptic imaging system is achieved by inserting a micro-lens array directly anterior to the camera sensor. The depth is calculated from reconstruction of the resulting multi-angle view sub-images. With the present study, we demonstrate the application of a plenoptic system for 3D3C PTV measurement of engine-like air flow in a steady-state engine flow bench. This system consists of a plenoptic camera and a dual-cavity pulsed laser. The accuracy of the plenoptic PTV system was assessed using a dot target moved by a known displacement between two PTV frames.

Simulation of reach movements is an essential component for proactive ergonomic analysis in digital human modeling and for numerous applications in vehicle design. Most studies on reach kinematics described human movements in static conditions. Earlier studies of reach performance in vibration environments focused mainly on fingertip deviation without considering multi-body dynamics. However, for the proper assessment of reach performance under whole-body vibration exposure, a multi-body biodynamic model needs to be developed. This study analyzes three dimensional reach kinematics of the upper extremity during in-vehicle operations, using a multi-segmental model of the upper body in the vibratory environment. The goals are to identify the characteristics of upper body reach movements and to investigate vibration-induced changes in joint kinematics. Thirteen subjects reached to four target directions in the right hemisphere.

Helmholtz resonators are widely used for the noise reduction in vehicle induction and exhaust systems. This study investigates the effect of specific cavity dimensions of these resonators theoretically, computationally and experimentally. By considering one-dimensional wave propagation through distributed masses in the connector and cavity, a closed-form expression for the transmission loss of axisymmetric configurations is presented, thereby partially eliminating the limitations of a lumped-parameter analysis. Eight resonators of fixed neck geometry and cavity volume with length-to-diameter ratios of the volume varying from 0.32 to 23.92 are studied both computationally and experimentally. The first of the two computational approaches employed in the study implements a finite difference time domain technique to solve the nonlinear governing equations of one-dimensional compressible flow.

Solidification paths and phase stability have been investigated in the creep resistant Mg-Al-Ca based alloys for powertrain applications. The liquidus projection and isothermal sections of the Mg-Al-Ca ternary system were determined, including a ternary (Mg, Al)2Ca intermetallic compound. The solidification of the alloys in the α-Mg primary phase field involves L→α+(Mg, Al)2Ca eutectic reaction in a wide range of compositions and is terminated with invariant reactions that form Mg2Ca or Mg17Al12 phases. The (Mg, Al)2Ca is a high temperature phase and decomposes into Mg2Ca and Al2Ca phases between 773 and 673 K, but the transformation is kinetically quite slow at temperatures below 473 K. Based on this new knowledge, alloy modifications through quaternary elemental additions to improve the solid-solution strength and aging treatments to reinforce the α-Mg phase with precipitates have been demonstrated.

Exhaust Gas Recirculation (EGR) coolers are commonly used in on-road and off-road diesel engines to reduce the recirculated gas temperature in order to reduce NOx emissions. One of the common performance behaviors for EGR coolers in use on diesel engines is a reduction of the heat exchanger effectiveness, mainly due to particulate matter (PM) deposition and condensation of hydrocarbons (HC) from the diesel exhaust on the inside walls of the EGR cooler. According to previous studies, typically, the effectiveness decreases rapidly initially, then asymptotically stabilizes over time. Prior work has postulated a deposit removal mechanism to explain this stabilization phenomenon. In the present study, five field aged EGR cooler samples that were used on construction machines for over 10,000 hours were analyzed in order to understand the deposit structure as well as the deposit composition after long duration use.

Exhaust Gas Recirculation (EGR) coolers are widely used on diesel engines to reduce in-cylinder NOx formation. A common problem is the accumulation of a fouling layer inside the heat exchanger, mainly due to thermophoresis that leads to deposition of particulate matter (PM), and condensation of hydrocarbons (HC) from the diesel exhaust. From a recent investigation of deposits from field samples of EGR coolers, it was confirmed that the densities of their deposits were much higher than reported in previous studies. In this study, the experiments were conducted in order to verify hypotheses about deposit growth, especially densification. An experimental set up which included a custom-made shell and tube type heat exchanger with six surrogate tubes was designed to control flow rate independently, and was installed on a 1.9 L L-4 common rail turbo diesel engine.

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.

Compositional and microstructural variations in a casting can often result in rather significant variations in the response to a given aging treatment, leading to location dependent mechanical properties. The objective of this study is to determine the effect of copper content and solidification rate on the aging behavior of a type 319 cast aluminum alloy. The nominal composition of the alloy is Al-7% Si-3.5% Cu-0.25% Mg, however, typical secondary 319 aluminum specifications allow copper levels to vary from 3-4%. Solidification rates throughout a casting can vary greatly due to, among other factors, differences in section size. To determine the effect of copper level and solidification rate on the aging response, aging curves were experimentally developed for this alloy. Three different copper levels (3, 3.5, 4%) and two solidification rates were used for this study. Aging temperatures ranged from 150-290°C with nine aging times at each temperature.

Privacy has been a rising concern. The European Union has established a privacy standard called General Data Protection Regulation (GDPR) in May 2018. Furthermore, the Facebook-Cambridge Analytica data incident made headlines in March 2018. Data collection from vehicles by OEM platforms is increasingly popular and may offer OEMs new business models but it comes with the risk of privacy leakages. Vehicular sensor data shared with third-parties can lead to misuse of the requested data for other purposes than stated/intended. There exists a relevant regulation document introduced by the Alliance of Automobile Manufacturers (“Auto Alliance”), which classifies the vehicular sensors used for data collection as covered and non-sensitive parameters.