Computer Aided Design Algorithmic Technique for Very Large Scale Integrated Circuit Design and Smart Home Embedded System Design

To tackle the challenges at circuit level and system level VLSI and embedded system design, this dissertation proposes various novel algorithms to explore the efficient solutions. At the circuit level, a new reliability-driven minimum cost Steiner routing and layer assignment scheme is proposed, and the first transceiver insertion algorithmic framework for the optical interconnect is proposed. At the system level, a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems, which optimizes system energy consumption under stochastic fault occurrences, is proposed. The embedded system design is also widely used in the smart home area for improving health, wellbeing and quality of life. The proposed scheduling scheme for multiprocessor embedded systems is hence extended to handle the energy consumption scheduling issues for smart homes. The extended scheme can arrange the household appliances for operation to minimize monetary expense of a customer based on the time-varying pricing model.

DEVELOPMENT OF THE ECOCAR 3 PROPOSAL AND GUIDELINES FOR MODELING AND DESIGN IN YEAR ONE OF ECOCAR 3

This report summarizes the work done for the Vehicle Powertrain Modeling and Design Problem Proposal portion of the EcoCAR3 proposal as specified in the Request for Proposal from Argonne National Laboratory. The results of the modeling exercises presented in the proposal showed that: An average conventional vehicle powered by a combustion engine could not meet the energy consumption target when the engine was sized to meet the acceleration target, due the relatively low thermal efficiency of the spark ignition engine. A battery electric vehicle could not meet the required range target of 320 km while keeping the vehicle weight below the gross vehicle weight rating of 2000 kg. This was due to the low energy density of the batteries which necessitated a large, and heavy, battery pack to provide enough energy to meet the range target. A series hybrid electric vehicle has the potential to meet the acceleration and energy consumption parameters when the components are optimally sized. A parallel hybrid electric vehicle has less energy conversion losses than a series hybrid electric vehicle which results in greater overall efficiency, lower energy consumption, and less emissions. For EcoCAR3, Michigan Tech proposes to develop a plug-in parallel hybrid vehicle (PPHEV) powered by a small Diesel engine operating on B20 Bio-Diesel fuel. This architecture was chosen over other options due to its compact design, lower cost, and its ability to provide performance levels and energy efficiency that meet or exceed the design targets. While this powertrain configuration requires a more complex control system and strategy than others, the student engineering team at Michigan Tech has significant recent experience with this architecture and has confidence that it will perform well in the events planned for the EcoCAR3 competition.

MILITARY VEHICLE OPTIMIZATION AND CONTROL

It is remarkable that there are no deployed military hybrid vehicles since battlefield fuel is approximately 100 times the cost of civilian fuel. In the commercial marketplace, where fuel prices are much lower, electric hybrid vehicles have become increasingly common due to their increased fuel efficiency and the associated operating cost benefit. An absence of military hybrid vehicles is not due to a lack of investment in research and development, but rather because applying hybrid vehicle architectures to a military application has unique challenges. These challenges include inconsistent duty cycles for propulsion requirements and the absence of methods to look at vehicle energy in a holistic sense. This dissertation provides a remedy to these challenges by presenting a method to quantify the benefits of a military hybrid vehicle by regarding that vehicle as a microgrid. This innovative concept allowed for the creation of an expandable multiple input numerical optimization method that was implemented for both real-time control and system design optimization. An example of each of these implementations was presented. Optimization in the loop using this new method was compared to a traditional closed loop control system and proved to be more fuel efficient. System design optimization using this method successfully illustrated battery size optimization by iterating through various electric duty cycles. By utilizing this new multiple input numerical optimization method, a holistic view of duty cycle synthesis, vehicle energy use, and vehicle design optimization can be achieved.