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Full-Text Articles in Engineering
Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan
All Dissertations
The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …
Distributed Control And Learning Of Connected And Autonomous Vehicles Approaching And Departing Signalized Intersections, Joshua Onyeka Ogbebor
Distributed Control And Learning Of Connected And Autonomous Vehicles Approaching And Departing Signalized Intersections, Joshua Onyeka Ogbebor
LSU Master's Theses
This thesis outlines methods for achieving energy-optimal control policies for autonomous vehicles approaching and departing a signalized traffic intersection. Connected and autonomous vehicle technology has gained wide interest from both research institutions and government agencies because it offers immense promise in advancing efficient energy usage and abating hazards that beset the current transportation system. Energy minimization is itself crucial in reducing the greenhouse emissions from fossil-fuel-powered vehicles and extending the battery life of electric vehicles which are presently the major alternative to fossil-fuel-powered vehicles. Two major forms of fuel minimization are studied. First, the eco-driving problem is solved for a …