Forward-looking, Velocity-driven, Powertrain Modeling and Optimal Control for Continuous Variable Transmission
Author | : Paresh Deshmukh |
Publisher | : |
Total Pages | : 23 |
Release | : 2018 |
ISBN-13 | : OCLC:1123215305 |
ISBN-10 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Forward-looking, Velocity-driven, Powertrain Modeling and Optimal Control for Continuous Variable Transmission written by Paresh Deshmukh and published by . This book was released on 2018 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is the second part of a two-part study focused on improving Continuous Variable Transmission (CVT) ratio management and control. The objective of the overall project was to develop a methodology for a vehicle with a CVT and a downsized gasoline engine to deliver the maximum vehicle fuel economy within drivability and performance constraints. The first part of this study, as described in [1], focuses on developing a cycle driven model for optimizing the CVT ratio. The study presented in this paper focuses on developing a velocity driven model to simulate the real-time behavior of a vehicle. The results from the optimization schedule presented in backward-looking velocity driven model were utilized to develop a new powertrain optimal operating line (hereafter referred to as P-OOL) which considers powertrain (engine and CVT) efficiency. This P-OOL was created to ensure that the control strategies utilized in the forward- looking model could be used in a real-time vehicle. The proposed P-OOL has been simulated in the model using the Federal Test Procedure (FTP-75) test cycle. Simulation results show that the engine operating points deviate away from the P-OOL. The reasons for deviation of operating points from the P-OOL are vehicle dynamics and powertrain response lag. A control algorithm was simulated which considers powertrain loss and inertia torque due to CVT ratio changes to minimize powertrain response lag. Simulations show a significant improvement in the fuel economy by applying the powertrain response lag compensation algorithm.