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Abstract List of Reviewers

2020-2021 Reviewers

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.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

Abstract One of the key factors for accurate mass burn fraction and energy conversion point calculations is the accuracy of the compression ratio. The method presented in this article suggests a workflow that can be applied to determine or correct the compression ratio estimated geometrically or measured using liquid displacement. It is derived using the observation that, in a motored engine, the heat losses are symmetrical about a certain crank angle, which allows for the derivation of an expression for the clearance volume [1]. In this article, a workflow is implemented in real time, in a current production engine indicating system. The goal is to improve measurement data quality and stability for the energy conversion points calculated during measurement procedures. Experimental and simulation data is presented to highlight the benefits and improvement that can be achieved, especially at the start of combustion.

Abstract The 2010 Environmental Protection Agency (EPA) Emission Standard for heavy-duty engines required 0.2 g/bhp-hr over certification cycles (cold and hot Federal Test Procedure [FTP]), and the California Air Resources Board (CARB) standards require 0.02 g/bhp-hr for the same cycles leading to a 90% reduction of overall oxides of nitrogen (NOx) emissions. Similar reductions may be considered by the EPA through its Cleaner Trucks Initiative program. In this article, aftertreatment system components consisting of a diesel oxidation catalyst (DOC); a selective catalytic reduction (SCR) catalyst on a diesel particulate filter (DPF), or SCR-F; a second DOC (DOC2); and a SCR along with two urea injectors have been analyzed, which could be part of an aftertreatment system that can achieve the 0.02 g/bhp-hr standard.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

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 Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

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 In this experimental study, a novel combustion technique, “reactivity-controlled compression ignition” (RCCI), has been investigated using alcohols acting as low-reactivity fuel (LRF) and mineral diesel acting as high-reactivity fuel (HRF). Combustion experiments were performed in a single-cylinder research engine at a constant engine speed of 1500 rpm and a low engine load of 3 bar brake mean effective pressure (BMEP). RCCI combustion is a practical low-temperature combustion (LTC) concept, which was achieved using three primary alcohols: Methanol, Ethanol, and Butanol in different premixed ratios (rp = 0.25, 0.50, and 0.75) with mineral diesel. Results showed a relatively superior performance and emissions characteristics of RCCI combustion compared to conventional compression ignition (CI) combustion. The influence of LRF was visible in RCCI combustion, which exhibited a more stable combustion compared to the baseline CI combustion.

Abstract An innovative analysis approach to evaluate heavy-duty vehicle downhill engine brake performance was developed. The vehicle model developed with GT-Drive simulates vehicle downhill control speeds with different engine brake retarding powers, transmission gears, and vehicle weights at sea level or high altitude. The outputs are then used to construct multi-factor parametric design charts. The charts can be used to analyze the vehicle-level engine brake capabilities or compare braking performance difference between different engine brake configurations to quantify the risk of engine retarding power deficiency at both sea level and high altitude downhill driving conditions.

Abstract The increased scope of active and passive safety in motor vehicles and the enforcement of approval requirements for individual parts and assemblies affect the design and parameters of a car’s motion. The tire, which transmits forces and torques onto the road’s surface is a particularly crucial element in the vehicle. Its structure, type of mixture, and operating conditions determine the safety of vehicle motion. The three-axial force system loads the tires of the car and affects both the tread and sidewall, as well as the suspension and steering system. Taking into account the controllability and stability of movement, the tire is subjected to dynamic and thermal loads, as well as to wear and random damage. This negatively impacts on the joints of composite layers. The sudden loss of pressure in the tire can lead to serious accidents, especially when moving at high speeds, due to changes in the rolling radius.

Abstract The study of road loads on trucks plays a major role in assessing the effect of heavy-vehicle design on fuel conservation measures. Coastdown testing with full-scale vehicles in the field offers a good avenue to extract drag components, provided that random instrumentation faults and biased environmental conditions do not introduce errors into the results. However, full-scale coastdown testing is expensive, and environmental biases which are ever-present are difficult to control in the results reduction. Procedures introduced to overcome the shortcomings of full-scale field testing, such as wind tunnels and computational fluid dynamics (CFD), though very reliable, mainly focus on estimating the effects of aerodynamic drag forces to the neglect of other road loads which should be considered.

Abstract Since the steady-state computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) turbulence models offer low-cost and sensible accuracy, they are frequently utilized for bluff bodies’ external aerodynamics investigations (e.g., upwind, crosswind, and shape optimization). However, no firm certainty is made regarding the best model in terms of accuracy and cost. Based on cost and accuracy aspects, four RANS turbulence models were studied, which are Spalart–Allmaras, realizable k-ε, RNG k-ε, and SST k-ω. Ahmed body with a 25° slant angle benchmark case was introduced for this investigation. Two grids were generated to satisfy the near-wall treatment of each turbulence model. All grid settings were proposed and discussed in detail. Fluid-structure analysis was performed on five different planes.

Abstract The present study assesses the use of a two-equation eddy-viscosity turbulence model, which is a shear-stress transport (SST) k-ω turbulence model, in two-way coupled aerodynamics and vehicle dynamics simulation of a heavy ground vehicle subjected to crosswind. The obtained results are compared with the corresponding results of the improved delayed detached-eddy simulation (IDDES) conducted at similar conditions from the previous literature. The aim is to evaluate the effects of different turbulence models used in aerodynamics simulations to resolve the vehicle dynamics results in two-way coupled simulations. The results present that the absolute relative percent differences between the lateral displacement, yaw angle and roll angle results of the SST k-ω and the IDDES simulations are less than 1%, 3%, and 10%, respectively.

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 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 Excessive brake heating of trucks on downgrades is a cause of continuing concern for the Wyoming Department of Transportation (WYDOT). Brake failure on downgrades characteristically takes a catastrophic toll on lives and property. The Grade Severity Rating System (GSRS) developed by the Federal Highway Administration (FHWA) recommends a maximum safe speed limit that has been identified as a feasible remedy for reducing the incidence of downgrade truck crashes. However, truck characteristics and roadway geometrics have changed over the years following the development of the GSRS. To deal with this development, a research project was initiated by the WYDOT in 2016 to update the GSRS model. The test truck used for the field tests in the prior research project was fitted with disc brakes on the front axle and drum brakes on the rear axle. However, disc brakes represent only about 20% of the brake market.

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.