Date of Award
4-2020
Degree Name
Master of Science in Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Dr. Zachary Asher
Second Advisor
Dr. Richard Meyer
Third Advisor
Dr. Jennifer Hudson
Keywords
Hybrid electric vehicle, optimal energy management strategies, dynamic programming, model predictive control, fuel economy improvement
Access Setting
Masters Thesis-Open Access
Abstract
Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real-world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively
Recommended Citation
Patil, Amol Arvind, "Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction" (2020). Masters Theses. 5127.
https://scholarworks.wmich.edu/masters_theses/5127