| PhD Viva

Name of the Speaker: Mr. Subhadeep Kumar (EE15D211)
Guide: Dr. Ramkrishna Pasumarthy
Co-Guide: Dr. Nirav Pravinbhai Bhatt
Venue: Online
Online meeting link: https://meet.google.com/ahx-znpb-hhb
Date/Time: 4th July 2024(Thursday), at 6.30 PM
Title: An Aggregated Powertrain Model and a Scaled-Down Vehicle for Advancing Autonomous Vehicle Technology

Abstract :

Contemporary research on Autonomous Vehicles (AVs) predominantly centers on electric vehicles (EVs), while a notable preference persists among end-users for Internal Combustion Engine (ICE) vehicles driven by factors such as cost and familiarity. At the same time, widespread deployment of AVs necessitates their ability to navigate diverse traffic scenarios, including dense, laneless roads shared with human-driven vehicles of varying automation levels. Conducting real-world testing under such conditions poses safety concerns for all road users. To address these challenges, this work extends the scope of AV research to encompass ICE vehicles and introduces a scaled-down platform for testing AV algorithms. We consider an electronically actuated ICE powertrain with a push belt type continuous variable transmission (CVT) associated with a double-pinion planetary gear set. The electronically controlled actuators create the opportunity to implement autonomous driving controllers. Our focus lies on developing detailed control-oriented models of vehicular systems, which is crucial for developing autonomous driving controllers. We propose a novel model for the planetary gear set as a differential-algebraic-equation (DAE) system with switching dynamics. We extend the dynamic torque converter model and combine CVT variator kinematics and dynamics of pulley motion and hydraulics to develop an aggregated model. Further, we construct data-driven models for the CVT's traction coefficient and equilibrium force ratio. To operate the CVT, we design a rule-based controller that makes the CVT function at discrete steady-state ratios. We combine these models with the existing models of powertrain components and vehicle dynamics to study the utility of the developed powertrain model. We consider three case study examples with realistic scenarios resembling vehicle maneuvers in traffic, stop-and-go motion, and reverse motion to examine the model's ability to capture transient and steady-state characteristics and compare the resulting behaviour with the expected response. The powertrain model can be integrated with models of other vehicular subsystems and vehicle motion dynamics to develop autonomous driving controllers for ICE vehicles. Thereafter, we present a full vehicle model and conceptualize a scaled-down vehicle platform to validate AV algorithms where physical testing is a safety issue. We introduce a rear-wheel driven and front-wheel steered one-tenth-scale electric vehicle designed by emphasizing vehicle state measurement, precise control, perception sensing, and electric safety. Our approach utilizes actuators that provide feedback, enhanced reliability, and precision, in contrast to conventional scaled platforms built with actuators of radio-controlled cars. Apart from the conventional sensors used in the existing scaled platforms, our vehicle incorporates novel sensor modules to measure wheel angular velocities, steering angles, and battery cell voltages, which can also be adapted into the existing scaled platforms. We demonstrate the vehicle's motion with manual and autonomous operation and showcase its features and functionalities for validation. Overall, our research work broadens the applicability of AV technology to ICE vehicles and puts forth a platform for validating autonomous driving algorithms where real-time testing is unsuitable.