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Update on heavy-duty regulations. Presenter Michael J. McCarthy, California Air Resources Board

This paper presents that low-voltage (42V) current intensive Switched Reluctance Machine (SRM) based traction systems are feasible for lightly hybridized vehicles. Power electronic drive as well as electric machine issues are comprehensively addressed. Five different SRM drivers for low-voltage and high-voltage machines are studied. Suitability of the proposed low-voltage, high-current drives is elaborated. Furthermore, four machines with the rating of 7.5 kW are designed and simulated. These traction machines have 6/4 and 8/6 SRM configurations with the operating voltage of 42V and 300V. Higher torque density is the main advantage of the low-voltage machines compared to the high-voltage machines. In addition, 6/4 SRMs have better performance.

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.

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.

Intensifying demands for higher fuel economy from one hand and environmental concerns from the other are driving advanced automotive power systems to be more electric. As a result, automotive electrical systems with higher capacity and more complexity are needed to cope with this expanding electrification trend. As different electrical applications and loads are being introduced in automobiles, multi-converter intensive power electronic systems are emerging as the next generation of the advanced automotive electrical systems. In fact, power electronic converters and electric motor drives are inevitable parts of more electric automotive power systems. When power electronic converters and electric motor drives are tightly regulated to improve system performance and efficiency, they present negative impedance characteristics of constant power loads to the entire automotive electrical system. This destabilizing effect may cause system instability.

For this latest SAE Fatigue Design and Evaluation project, fatigue tests were run by loading, in bending, both welded and machined T-Joint specimens that have the same geometry. The test rig setup consisted of a horizontally mounted actuator, with pinned joints at both ends, where the load is applied to the top of the vertical leg of the “upside down T” of a T-Joint specimen, while the horizontal legs of the “upside down T” were clamped to the bedplate. Specimens were tested until failure or until the specimen was unable to carry the commanded load. They were cycled under constant amplitude (at several load levels and R ratios), block cycle, and variable amplitude loadings. Welded and machined T-Joint specimens of the same geometry were included in the test plan such that fatigue life predictions could be compared to test lives for each case. Those comparisons would demonstrate the methodology’s relative predictive ability to manage welds, residual stress, etc...

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles.

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.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

In any machinery, avoiding noise and vibration completely is a difficult task due to the structural dynamic behaviors of components. To safeguard the operator, it is important to best isolate the operator station from NVH environment. Cabin isolation is an important aspect to minimize structure borne noise and tactile vibrations to be transferred into the cabin. Isolators are selected based on the isolation system inertial properties at mounting locations in the operating frequency range interested. The most important assumption to select isolators are that the active side and passive side of the isolators are nearly rigid so impedance mismatch is created for effective isolation. This paper describes the importance of dynamic stiffness of the structures on both the active and passive side for better NVH performance.

Operator noise is an important aspect for noise and vibration of off-highway vehicles and a quieter cab is critical for the operator comfort. The noise level inside the cab is influenced by structural and acoustic transfer paths. In this paper, we used hybrid FE-SEA approach to consider both structural and acoustic transfer path as FEM and SEA methods individually face limitations in high and low frequencies respectively. A hybrid FE-SEA cab model was built to predict the structural and acoustic transfer functions. The analysis model was built with the systematic approach validated at each step with the laboratory test results. For the structural transfer function, structural excitations were applied at four cab mount locations and accelerations at various locations on the cab were validated. For the acoustic transfer function, the cab was excited with the volume velocity source inside the cab and sound power output of various panels were calculated and compared to the test results.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.

A framework to study the response of seated operators to whole-body vibration (WBV) is presented in this work. The framework consists of (i) a six-degree-of-freedom man-rated motion platform to play back ride files of typical heavy off-road machines; (ii) an optical motion capture system to collect 3D motion data of the operators and the surrounding environment (seat and platform); (iii) a computer skeletal model to embody the tested subjects in terms of their body dimensions, joint centers, and inertia properties; (iv) a marker placement protocol for seated positions that facilitates the process of collecting data of the lower thoracic and the lumbar regions of the spine regardless of the existence of the seatback; and (v) a computer human model to solve the inverse kinematics/dynamic problem for the joint profiles and joint torques. The proposed framework uses experimental data to answer critical questions regarding human response to WBV.

Off-highway machine mounting system isolation, especially the cab mounting system, significantly affects the operator comfort by providing damping to the harsh inputs and isolating the structure-borne energy from traveling into the cab. Mounting system isolation performance is decided not only by the isolation component, but also the mounting bracket structure, and should be treated as a system. This paper gives a review of how the mounting system isolates structural energy and the effect of the bracket structure stiffness to the mounting system isolation performance.

Low temperature combustion (LTC) modes are among the advanced combustion technologies which offer thermal efficiencies comparable to conventional diesel combustion and produce ultra-low NOx and particulate matter (PM) emissions. However, combustion timing control, excessive pressure rise rate and high cyclic variations are the common challenges encountered by the LTC modes. These challenges can be addressed by developing model-based control framework for the LTC engine. In the current study, in-cylinder pressure data for dual-fuel LTC engine operation is analyzed for 636 different operating conditions and the heat release rate (HRR) traces are classified into three distinct classes based on their distinct shapes. These classes are named as Type-1, Type-2 and Type-3, respectively.