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This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

One-dimensional single-zone and two-zone analyses have been exercised to calculate the mass fraction burned in an engine operating on ethanol/gasoline-blended fuels using the cylinder pressure and volume data. The analyses include heat transfer and crevice volume effects on the calculated mass fraction burned. A comparison between the two methods is performed starting from the derivation of conservation of energy and the method to solve the mass fraction burned rates through the results including detailed explanation of the observed differences and trends. The apparent heat release method is used as a point of reference in the comparison process. Both models are solved using the LU matrix factorization and first-order Euler integration.

Smaller locomotives often use mechanical transmissions instead of diesel-electric drive systems typically used in larger locomotives. This paper discusses how Model Based Design was used to develop the complete drive train control system for a 24 ton sugar cane locomotive. A complete MATLAB Simulink machine model was built to fully test and verify the shift control logic, traction control, vehicle speed limiting, and braking control for this locomotive application before it was commissioned. The model included the engine, torque converter, planetary transmission, drive line, and steel on steel driving surface. Simulation was used to debug all control code and test and refine control strategies so that the initial field commissioning in remote Australia was executed very quickly with minimal engineering support required.

This paper deals with the dynamic characterization of an automotive shock absorber, a continuation of an earlier work [1]. The objective of this on-going research is to develop a testing and analysis methodology for obtaining dynamic properties of automotive shock absorbers for use in CAE-NVH low-to-mid frequency chassis models. First, the effects of temperature and nominal length on the stiffness and damping of the shock absorber are studied and their importance in the development of a standard test method discussed. The effects of different types of input excitation on the dynamic properties of the shock absorber are then examined. Stepped sine sweep excitation is currently used in industry to obtain shock absorber parameters along with their frequency and amplitude dependence. Sine-on-sine testing, which involves excitation using two different sine waves has been done in this study to understand the effects of the presence of multiple sine waves on the estimated dynamic properties.

China has become the world’s largest vehicle market in terms of sales volume. Automobiles sales keep growing in recent years despite the declining economic growth rate. Due to the increasing attention given to the environmental impact, more stringent emission regulations are being drafted to control traditional internal combustion engine emissions. In order to reduce vehicle emissions, environmentally-friendly new-energy vehicles, such as electric vehicles and plug-in hybrid vehicles, are being promoted by government policies. The Chinese government plans to boost sales of new-energy cars to account for about five percent of China’s total vehicle sales. It is well known that more electric and electronic components will be integrated into a vehicle platform during vehicle electrification.

Mandated pollutant emission levels are shifting light-duty vehicles towards hybrid and electric powertrains. Heavy-duty applications, on the other hand, will continue to rely on internal combustion engines for the foreseeable future. Hence there remain clear environmental and economic reasons to further decrease IC engine emissions. Turbocharged diesels are the mainstay prime mover for heavy-duty vehicles and industrial machines, and transient performance is integral to maximizing productivity, while minimizing work cycle fuel consumption and CO2 emissions. 1D engine simulation tools are commonplace for “virtual” performance development, saving time and cost, and enabling product and emissions legislation cycles to be met. A known limitation however, is the predictive capability of the turbocharger turbine sub-model in these tools.

NVH is gaining importance in the quality perception of off-highway machines' performance and operator comfort. Booming noise, a low frequency NVH phenomenon, can be a significant sound issue in a motor grader when it is used under certain operating conditions that cause low frequency excitations to the machine. In order to increase operator comfort by decreasing the noise levels and noise annoyance, both simulation and testing techniques were leveraged to reduce the booming noise of a motor grader. Simultaneous structural/acoustics simulations and experimental modal tests were performed to evaluate this phenomenon. The simulation models were validated using test results and then used to evaluate solutions to this noise problem. Further field tests confirmed the validity of these recommended solutions.

Technologies that provide potential for significant improvements in engine efficiency include, engine downsizing/downspeeding (enabled by advanced boosting systems such as an electrically driven compressor), waste heat recovery through turbocompounding or organic Rankine cycle and 48 V mild hybridization. FEV’s Integrated Turbocompounding/Waste Heat Recovery (WHR), Electrification and Supercharging (FEV-ITES) is a novel approach for integration of these technologies in a single unit. This approach provides a reduced cost, reduced space claim and an increase in engine efficiency, when compared to the independent integration of each of these technologies. This approach is enabled through the application of a planetary gear system. Specifically, a secondary compressor is connected to the ring gear, a turbocompounding turbine or organic Rankine cycle (ORC) expander is connected to the sun gear, and an electric motor/generator is connected to the carrier gear.

NVH is gaining importance in the quality perception of off-highway machine performance and operator comfort. Booming noise, a low frequency NVH phenomenon, can be a significant sound issue in an off-highway machine. In order to increase operator comfort by decreasing the noise levels and noise annoyance, a tuned mass damper (TMD) was added to the resonating panel to suppress the booming. Operational deflection shapes (ODS) and experimental modal analysis (EMA) were performed to identify the resonating panels, a damper was tuned in the lab and on the machine to the specific frequency, machine operational tests were carried out to verify the effectiveness of the damper to deal with booming noise.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

A method is presented for precise mounting of a hose model with any specified twist. Once mounting points and directions are specified, a hose of a specified length can be developed using discrete beams. A divide and conquer approach is employed to position, orient, decouple the free end of the hose model in a twist free state that is then twisted to a specified angle. The development of the kinematic elements necessary to do this is presented. Some Cosserat models have been shown to branch into multiple solutions while the method presented here has always converged to the minimum energy solution. The method for linking the hose model to other linkages is discussed as well one common error committed by users in implementing the link. In order to model the torsional properties of the hose, the torsional stiffness must be modified. A method for doing this using digital scans is discussed.

This paper describes the heat transfer model of a composite aluminum brake rotor and compares the predicted temperatures to dynamometer measurements taken during a 15 fade stop trial. The model is based on meshed surface geometry which is simulated using RadTherm software. Methods for realistically modeling heat load distribution, surface rotation, convection cooling and radiation losses are also discussed. A comparison of the simulation results to the dynamometer data shows very close agreement throughout the fade stop trial. As such, the model is considered valid and will be used for further Steel Clad Aluminum (SCA) rotor development.

An automobile and a tracked military vehicle were instrumented with multiple tachometers, one for each drive wheel/sprocket and operated with accelerometers mounted at suspension, chassis, and powertrain locations on the vehicles. The Time Variant Discrete Fourier Transform, TVDFT, order tracking method was then used to extract the order tracks and operating shapes estimated based on each tachometer. It is shown that under some conditions a different operating shape is excited by each of the wheels/sprockets simultaneously. This is due to the asymmetries present in the vehicles. The strengths of the TVDFT order tracking method are shown for this type of analysis, which is difficult due to the closeness, within 0.001 orders, and crossing of the orders. Benefits of using multiple tachometers and advanced order tracking methods become apparent for solving a class of noise and vibration problems.

This is the second part of a study on using port water injection to quantifiably enhance the knock performance of fuels. In the United States, the metric used to quantify the anti-knock performance of fuels is Anti Knock Index (AKI), which is the average of Research Octane Number (RON) and Motor Octane Number (MON). Fuels with higher AKI are expected to have better knock mitigating properties, enabling the engine to run closer to Maximum Brake Torque (MBT) spark timing in the knock limited region. The work done in part I of the study related increased knock tolerance due to water injection to increased fuel AKI, thus establishing an ‘effective AKI’ due to water injection. This paper builds upon the work done in part I of the study by repeating a part of the test matrix with Primary Reference Fuels (PRFs), with iso-octane (PRF100) as the reference fuel and lower PRFs used to match its performance with the help of port water injection.

In the automotive embedded system domain, the measurements from vehicle and Hardware-In-Loop are currently evaluated against the testcases, either manually or via automation scripts. These evaluations are localized; they evaluate a limited number of signals for a particular measurement without considering system-level behavior. This results in defect leakage. This study aims to develop a tool that can notify anomalies at the signal level in a new measurement without referring to the testcases, considering a more significant number of system-level signals, thereby significantly reducing the defect leakage. The tool learns important features and patterns of each maneuver from many historical measurements using deep learning techniques. We tried two CNN (convolution neural network) models. The first one is a specially designed CNN that does this maneuver classification and class-specific feature extraction.

Large channel count data acquisition systems have seen increasing use in the acquisition and analysis of rotating machinery, these systems have the ability to generate very large amounts of data for analysis. The most common operating measurement made on powertrains or automobiles on the road or on dynamometers has become the order track measurement. Order tracking analysis can generate a very large amount of information that must be analyzed, both due to the number of channels and orders tracked. Analysis methods to efficiently analyze large numbers of Frequency Response Function (FRF) measurements have been developed and used over the last 20 years in many troubleshooting applications. This paper develops applications for several FRF based analysis methods as applied for efficient analysis of large amounts of order track data.

This paper describes a project on the dynamic characterization of automotive shock absorbers. The objective was to develop a new testing and analysis methodology for obtaining equivalent linear stiffness and damping of the shock absorbers for use in CAE-NVH low- to- mid frequency chassis models. Previous studies using an elastomer test machine proved unsuitable for testing shocks in the mid-to-high frequency range where the typical road input displacements fall within the noise floor of the elastomer machine. Hence, in this project, an electrodynamic shaker was used for exciting the shock absorbers under displacements less than 0.05 mm up to 500 Hz. Furthermore, instead of the swept sine technique, actual road data were used to excite the shocks. Equivalent linear spring-damper models were developed based on least-squares curve-fitting of the test data.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.