Dissertation Defense: Look-Ahead Energy Management Strategies for Hybrid Vehicles

Committee Members

• Dr. Giorgio Rizzoni, Chair (MAE)
• Dr. David Hoelzle (MAE)
• Dr. Abhishek Gupta (ECE)
• Dr. Shawn Midlam-Mohler (MAE)
• Dr. Qadeer Ahmed

Abstract

Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. Their use of both electrical and traditional fossil-fuel based energy sources make them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, the energy management systems, like most other control systems are designed, prototyped, and validated in a software simulation before implementing it on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and the cost. Traditionally, the design of energy management systems is based on statutory drive cycles and such approaches yield very good results for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identified and solved three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed in the form of a traffic integrated powertrain co-simulation. Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. finally, through the use of optimal control algorithms, a look-ahead energy management system is developed to understand the utility of different look-ahead technologies in the improvement of fuel economy.