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This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development of a multimaterial lightweight Body-in-White (BiW) structure to support an all-electric powertrain combined with an interior package that maximizes volume to enable a variety of interior configurations and activities for Generation Z users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

This set consists of three books, Design of Automotive Composites, CAE Design and Failure Analysis of Automotive Composites, and Biocomposites in Automotive Applications all developed by Dr. Charles Lu and Dr. Srikanth Pilla. Design of Automotive Composites reports successful designs of automotive composites occurred recently in this arena, CAE Design and Failure Analysis of Automotive Composites focuses on the latest use of CAE (Computer-Aided Engineering) methods in design and failure analysis of composite materials and structures, and Biocomposites in Automotive Applications, focuses on processing and characterization of biocomposites, their application in the automotive industry and new perspectives on automotive sustainability. Together, they are a focused collection providing the reader with must-read technical papers, hand-picked by the editors, supporting the growing importance of the use of composites in the ground vehicle industry. Dr. Charles Lu is H.E.

Series hybrid powertrain design and control strategies for high-speed, tracked, off-road vehicles depend on driving conditions, requiring a comprehensive approach to defining operational parameters prior to the design process. Although some vehicle speed and road grade profiles are available for tracked vehicles, these driving cycles are insufficient for hybrid powertrain characterization since they often neglect highly transient torque requirements for differential speed steering. Generating a difference in track speeds requires high traction torque, often with opposite directions, to overcome immense friction and is a significant contributor to both powertrain design and control decisions. This research presents a track model based on Finite Element Analysis (FEA) to calculate the steering load, which is then incorporated with ground speed and grade information to formulate more realistic driving cycles for tracked vehicles.