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Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

To perform coastdown tests on heavy-duty trucks, both long acceleration and coasting distances are required. It is very difficult to find long flat stretches of road to conduct these tests; for a Class 8 truck loaded to 80,000 lb, about 7 miles of road is needed to complete the coastdown tests. In the present study, a method for obtaining coastdown coefficients from data taken on a road of variable grade is presented. To this end, a computer code was written to provide a fast solution for the coastdown coefficients. Class 7 and Class 8 trucks were tested with three different weight configurations: empty, “cubed-out” (fully loaded but with a payload of moderate density), and “weighed-out” (loaded to the maximum permissible weight).

The Texas Project is a multi-year study of the emissions and fuel economy of aftermarket conversions of light-duty vehicles, including passenger cars, light light-duty trucks, and heavy light-duty trucks. The test fleet, consisting of 86 mostly 1994 model year vehicles, includes eight different types of light-duty vehicles that have been converted to dual fueled operation for either CNG or LPG and corresponding gasoline controls. Virtually every type of aftermarket conversion technology (referred to as a “kit” for convenience) is represented in the test matrix: eight different CNG kits and seven different LPG kits, all of which have closed loop control systems. One goal of The Texas Project is to evaluate the different kits for each of the applications. One method used for evaluating the different kits was by assessing their potential for attaining LEV certification for each of the vehicle applications.

The Texas Project involves the conversion of light-duty vehicles, up to and heavy light-duty trucks, to bi-fueled vehicles using commercially available aftermarket CNG and LPG conversion systems. The test fleet includes 68 dual fueled conversions. Virtually every type of aftermarket conversion technology for CNG and LPG was evaluated: eight different CNG and seven different LPG conversion “kits”, all of which are modern systems incorporating closed-loop control. The kits were installed and calibrated according to the manufacturer's guidelines and recommendations. The emissions when operating on the alternative fuel were compared to those when operating on certification gasoline to determine the “success” of the conversion. Many of these conversions, performed according to the manufacturer's requirements, were not “successful” (worse emissions than for gasoline operation). In almost all cases, the problem was NOx emissions that were too high when operating on the alternative fuel.

Improved submodels for use in a dynamic engine/vehicle model have been developed and the resulting code has been used to analyze the tip-in, tip-out behavior of a computer-controlled port fuel injected SI engine. This code consists of four submodels. The intake simulation submodel is similar to prior intake models, but some refinements have been made to the fuel flow model to more properly simulate a timed port injection system, and it is believed that these refinements may be of general interest. A general purpose engine simulation code has been used as a subroutine for the cycle simulation submodel. A conventional vehicle simulation submodel is also included in the model formulation. Perhaps most importantly, a submodel has been developed that explicitly simulates the response of the on-board computer (ECM) control system.

This paper discusses the Formula SAE Student Engineering Design Competition that was held May 24-26, 1984. As was the case of previous student engineering design competitions, the purpose of the Formula SAE Competition is to enhance engineering education by requiring students to apply the technical knowledge gained in their coursework to a practical engineering design problem including choice of appropriate design criteria, design, fabrication, testing, and evaluation. For the Formula SAE Competition, the design problem chosen is to design, construct, and compete a low powered Formula type race car. The purpose of this paper is to describe the 1984 Formula SAE Competition and to present the results of this event. It is expected that this paper will serve as a guide to hosts of similar competitions and will aid future Formula SAE competitors.

This paper discusses the Formula SAE Student Engineering Design Competition that was held May 26-28, 1983. As was the case of previous student engineering design competitions, the purpose of the Formula SAE Competition is to enhance engineering education by requiring students to apply the technical knowledge gained in their coursework to a practical engineering design problem including choice of appropriate design criteria, design, fabrication, testing, and evaluation. For the Formula SAE Competition, the design problem chosen is to design, construct, and compete a low powered Formula type race car. The purpose of this paper is to describe the 1983 Formula SAE Competition and to present the results of this event. It is expected that this paper will serve as a guide to hosts of similar competitions and will aid future Formula SAE competitors.

This paper discusses the Formula SAE Student Engineering Design Competition that was held May 27–29, 1982. As was the case of previous student engineering design competitions, the purpose of the Formula SAE Competition is to enhance engineering education by requiring students to apply the technical knowledge gained in their coursework to a practical engineering design problem including choice of appropriate design criteria, design, fabrication, testing, and evaluation. For the Formula SAE Competition, the design problem chosen is to design, construct, and compete a low powered Indianapolis-type race car. The purpose of this paper is to describe the 1982 Formula SAE Competition and to present the results of this event. It is expected that this paper will serve as a guide to hosts of similar competitions and will aid future Formula SAE competitors.