2026-05-24T03:35:29Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1934842024-12-23T18:01:48Zoai_pmh:alloai_pmh:repo_items193484
https://hdl.handle.net/2286/R.2.N.193484
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All Rights Reserved
2024
86 pages
Masters Thesis
Academic theses
Text
eng
Gangwar, Harsh
Chen, Yan Dr.
Zhao, Junfeng Dr.
Suo, Dajiang Dr.
Arizona State University
Partial requirement for: M.S., Arizona State University, 2024
Field of study: Engineering
Electric vehicles (EVs) have emerged as a promising solution to reduce greenhouse gas emissions and dependency on fossil fuels in the transportation sector. However, limited
battery capacity remains a significant challenge, impacting range and overall performance.
This thesis explores the application of Nonlinear Model Predictive Control (NMPC)
techniques to optimize energy management in EVs. The study begins with a comprehensive
review of existing literature on EV energy optimization strategies and NMPC
methodologies. Subsequently, a detailed model of the EV's dynamics, including the
battery, motor, and vehicle dynamics, is developed to formulate the optimization problem.
The NMPC controller is designed to dynamically adjust the power distribution among
different vehicle components, such as the motor, battery, and regenerative braking system,
while considering constraints such as battery state-of-charge, vehicle speed, and road
conditions. Simulation studies are conducted to evaluate the performance of the proposed
NMPC-based energy optimization strategy under various driving scenarios and compare it
with conventional control strategies. The results demonstrate that NMPC offers superior
performance in terms of energy efficiency, range extension, and overall vehicle dynamics.
The findings of this research contribute to the advancement of energy optimization
techniques for EVs, paving the way for more efficient and sustainable transportation
systems in the future.
Robotics
Energy
Computer Science
Electric Vehicles
Energy Management System
Model Predictive Control
Vehicle Control
Model-Predictive Control Enhanced Energy Management and Analysis of Dual-Motor Electric Vehicles