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Abstract The vehicle attributes developed for emerging markets like India are unique because of different topographical conditions, diversity and culture within the different states. Major attributes in vehicle development process is development of safe and sustainable vehicle systems (steering, brakes etc.) for the driver. India is presently an emerging market for automotive sector. With booming economy, purchasing power of the consumer has gone up in the past few years. Most of young population of India have started buying the cars. At the same time, India’s road infrastructure, vehicle regulations have exalted over the years. The consumer cognizance towards the vehicles have started changing now. They want safer, robust system in their vehicles with new convenience features at affordable cost. In recent years, almost all OEM’s in India have migrated steering systems from HPAS to EPAS for payback on fuel economy and weight.

Abstract A methodology for the development of catalyst models is presented. Also, a methodology of the implementation of such models into a modular simulation tool, which simulates the units in succession, is presented. A case study is presented illustrating how suitable models can be found and used for simulations. Such simulations illustrate the behavior of the individual units and the overall system. It is shown how, by simulating the units in succession, the entire after treatment system can be tested and optimized, because the integration makes it possible to observe the effect of the modules on one another.

Abstract This research developed methods for a virtual operator model (VOM) to learn the optimal control inputs for operation of a virtual excavator. Virtual design, used to model, simulate, and test new features, has often been limited by the fidelity of the virtual model of human operators. Human operator learns, over time, the capability, limits, and control characteristics of new vehicles to develop the best strategy to maximize the efficiency of operation. However, VOMs are developed with fixed strategies and for specific vehicle models (VMs) and require time-consuming re-tuning of the VOM for each new vehicle design. Thus, there typically is no capability to optimize strategies, taking account of variation in vehicle capabilities and limitations. A VOM learning capability was developed to optimize control inputs for the swing-to-pile task of a trenching operation. Different control strategies consisted of varied combinations of speed control, position control, and coast.

Abstract An accurate and rapid thermal model of an axle-brake system is crucial to the design process of reliable braking systems. Proper thermal management is necessary to avoid damaging effects, such as brake fade, thermal cracking, and lubricating oil degradation. In order to understand the thermal effects inside of a lubricated braking system, it is common to use Computational Fluid Dynamics (CFD) to calculate the heat generation and rejection. However, this is a difficult and time-consuming process, especially when trying to optimize a braking system. This article uses the results from several CFD runs to train a Stacked Ensemble Model (SEM), which allows the use of machine learning (ML) to predict the systems’ temperature based on several input design parameters. The robustness of the SEM was evaluated using uncertainty quantification.

Abstract When commercial vehicles drive in a mountainous area, the complex road condition and long slopes cause frequent acceleration and braking, which will use 25% more fuel. And the brake temperature rises rapidly due to continuous braking on the long-distance downslopes, which will make the brake drum fail with the brake temperature exceeding 308°C [1]. Meanwhile, the kinetic energy is wasted during the driving progress on the slopes when the vehicle rolls up and down. Our laboratory built a model that could calculate the distance from the top of the slope, where the driver could release the accelerator pedal. Thus, on the slope, the vehicle uses less fuel when it rolls up and less brakes when down. What we do in this article is use this model in a real vehicle and measure how well it works.

Abstract Geometric variation stemming from manufacturing can be a limiting factor for the quality and reliability of products. Therefore, manufacturing assessments are increasingly being performed during the early stages of product development. In the aerospace industry, products are complex engineering systems, the development of which require multidisciplinary expertise. In such contexts, there are significant barriers against assessing the effects of geometric variation on the functionality of products. To overcome these barriers, this article introduces a new methodology consisting of a modelling approach linked to a multidisciplinary simulation environment. The modelling approach is based on the parametric point method, which allows point-scanned data to be transferred to parameterised CAD models. In a case study, the methodology is implemented in an industrial setting.

Mechanical and thermal properties of the rubber compounds of a tire play an important role in the overall performance of the tire when it is in contact with the terrain. Although there are many studies conducted on the properties of the rubber compounds of the tire to improve some of the tire characteristics, such as the wear of the tread, there are a limited number of studies that focused on the performance of the tire when it is in contact with ice. This study is a part of a more comprehensive project looking into the tire-ice performance and modeling. In this study, to understand the effect of different rubber compounds on the tire performance, three identical tires from the same company have been chosen. The tires’ only difference is the material properties of the rubber. Two approaches have been implemented in this study.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract This article presents a multistage, coupled thermal management simulation approach, informed by physical testing where available, to aid design decisions for PACCAR’s SuperTruck II hybrid truck cabin concept. Focus areas include cabin insulation, battery sizing, and sleeper curtain position, as well as heating, ventilating, and air-conditioning (HVAC) component and accessory configurations, to maintain or improve thermal comfort while saving energy. The authors analyzed weather data and determined the national vehicle miles traveled weighted temperature and solar conditions for long-haul trucks. Example weather day profiles were selected to approximate the 5th and 95th percentile weighted conditions. A daylong drive cycle was developed to impose appropriate external wind conditions during rest and driving periods.

Abstract Off-highway equipment are subjected to diverse environmental conditions, severe duty cycles, and multiple simultaneous operations. Due to its continuous, high-power adverse operating conditions, equipment are exposed to high thermal loads, which result in the deterioration of its performance and efficiency. This article describes a model-based system simulation approach for thermal performance evaluation of a self-propelled off-highway vehicle. The objective of developing the simulation model including thermal fidelity is to quantify the impact of thermal loads on vehicular system/subsystems performance. This article also describes the use of simulation models for driving architectural design decisions and virtual test replication in all stages of product development.

2020-2021 Reviewers

Abstract List of Reviewers

Abstract A new method that allows data-enabled (empirical) models, commonly used for automotive engine calibration, to extrapolate beyond the range of training data has been developed. This method used a physics-based system-level one-dimensional model to improve interpolation and allow extrapolation for three data-based algorithms, by modifying the model input (feature) space. Neural network, regression, and k-nearest neighbor predictions of engine emissions and volumetric efficiency were greatly improved by generating 736,281 artificial feature spaces and then performing feature selection to choose feature spaces (feature selection) so that extrapolations in the original feature space were interpolations in the new feature space. A novel feature selection method was developed that used a two-stage search process to uniquely select the best feature spaces for every prediction.

Abstract Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested post-processing techniques and down-selected functional relationships for uncertainty propagation.

Erratum

Abstract Particulate Matter (PM) is one of the most sought-after exhaust emissions from Heavy-Duty Diesel Engines (HDDEs) to reduce. Several regulations in Europe and North America have led the way in drastically reducing PM of both on-road and off-road engines through stringent adoption of Diesel Particulate Filters (DPFs) and advanced combustion techniques. The effects of these advanced aftertreatment systems were studied using standardized testing procedures and equipment. While PM is defined as a “single” criteria pollutant, its complex structure entails several chemical compounds and molecules, displaying a whole spectrum of particle sizes. In addition, the morphology of some volatile compounds is shown to be affected by the interaction with background air during exhaust dilution and cooling.

Abstract The increased market penetration of hybrid electric powertrains in medium heavy-duty (MHD) applications has provided a novel platform for vehicle research. One example of such a platform is the MHD parallel hybrid truck developed by Odyne Systems, LLC. In collaboration with Odyne Systems, LLC and the Department of Energy (DOE), Oak Ridge National Laboratory (ORNL) developed a validated vehicle plant model for this truck and tested the Odyne powertrain in a hardware-in-the-loop (HIL) environment. While testing in the HIL environment, the effects of reduced engine load, and thus catalyst heating, on the selective catalytic reduction (SCR) catalyst produced diminished hybrid improvement as the level of energy storage usage increased. This article will discuss these results and the potentially unforeseen interactions with modern aftertreatment systems when hybridizing conventional powertrains.

Abstract Autonomous vehicle technology has become an unprecedented trend in the development of the automobile industry, which can ensure highly efficient use of resources, effectively improve the driving experience, and greatly reduces the driver’s burden. As one of the key technologies of autonomous vehicles, path planning has an important impact on the practical applications of autonomous vehicles. Planning a proper and efficient path is a prerequisite, which can improve the driving experience of autonomous vehicles. Therefore, in-depth research and development on applications of AI technology in path planning definitely have significant value in academic research. In this article, we will introduce a variety of path planning approaches for autonomous vehicles. We summarize the attributes of these path planning algorithms; simultaneously, we analyze the improvements to these algorithms. Then, we have a preliminary discussion on the applications in vehicle positioning and navigation.

Abstract The main objective of active downsizing is to increase the power train efficiency. In order to consistently enhance an approach of active downsizing, it is inevitable to disable and additionally to disengage part of the overall engine displacement volume. The disengagement avoids the friction loss of the piston group as well as its crank- and valve-train section. Therefore, this beneficial approach, the Split-Crankshaft Engine (SCE) is currently under development at the Chair of Internal Combustion Engines in cooperation with the Gear Research Centre (FZG), at the Technical University of Munich. The SCE concept consists of two partial internal combustion engines, which are arranged inline. The Primary Engine (PE) is permanently running while the Secondary Engine (SE) can be switched on and off load-dependently during driving operation.

Abstract The article considers the issues of a systemic approach to studying safety levels in transport operations and ways to increase the safety of the operator-machine system in Russian transport. The principal and problematic issues of reducing the risk of injury by preventing traffic accidents and reducing the severity of their impact have not been sufficiently addressed. When performing transport operations, there are often disagreements between the elements of the “Operator, Machine, and Environment” technological system due to the influence of external conditions and parameters of the constantly-changing environment in the workplace. This leads to a sharp increase in the number of failures of system elements, which reduces the level of safety of transport operations.