Date of Award

12-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Automotive Engineering

Committee Member

Dr. Simona Onori, Committee Chair

Committee Member

Dr. Zoran Filipi, Co-Chair

Committee Member

Dr. Mark Hoffman, Co-Chair

Committee Member

Dr. Beshah Ayalew

Abstract

With pressure from strict emission and fuel consumption regulations, researchers are searching for improved internal combustion engine performance. Especially for the heavy-duty vehicles, which takes up 7% of the total vehicle volume while consume around 30% of transportation energy in US. Around 40-60% of energy is wasted as heat in heavy-duty diesel (HDD) vehicles in different engine operating conditions, which mainly includes the waste heat in exhaust gas, exhaust gas recirculation (EGR) circuit, and engine coolant. Waste heat recovery (WHR) techniques are potential to achieve the fuel economy and emission reduction goals. Among the available WHR techniques, organic Rankine cycle (ORC) is preferred by many researchers for its mature technologies and high efficiency. The aim of this dissertation is to analyze the power of HDD vehicle by: (i) building a high fidelity, physics-based ORC-WHR dynamic system plant model, (ii) building a reduced order model framework, and (iii) conducting the power analysis based on the developed plant and reduced models. The dynamic system plant model is built, which includes heat exchangers, a turbine expander, pumps, control valves, compressible volumes, junctions and a reservoir. Components are modelled and calibrated individually. Subsequently, the component models are integrated into an entire ORC-WHR system model. The entire ORC-WHR system model is validated over transient engine conditions. Actuator sensitivity study is conducted for the ORC-WHR power generation analysis using the ORC-WHR plant model. Besides the ORC-WHR plant model, a reduced order model framework is developed utilizing Proper Orthogonal Decomposition (POD) and Galerkin projection approaches. The POD-Galerkin reduced order model framework inherits the system physics from the high fidelity, physics-based ORC-WHR plant model. POD Galerkin reduced order models are compared with three existing models (finite volume model, moving boundary model and 0D lumped model) and show their advantages over the existing models in terms of accuracy or computation cost. In addition, identification method is applied to the low order POD Galerkin reduced order model to increase the accuracy. Given the validated ORC-WHR plant model and POD Galerkin reduced order model framework, the ORC-WHR system power analysis is conducted. Steady state power analysis is conducted over two quasi-steady driving cycles using the ORC-WHR plant model. An engine model is developed to predict the exhaust conditions in transient engine operating conditions. Transient power analysis is conducted with ORC-WHR plant model and engine model co-simulation by optimizing three vapor temperature reference trajectories. Finally, dynamic programming (DP) is implemented with the POD-Galerkin reduced order model to generate ORC-WHR power benchmark in a driving cycle, which can give the guidance on the ORC power optimization and evaluate the controller performance.

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