Design of Self-Optimizing Adaptive Cruise Controller for Radar-Guided Electric Vehicle
Date Issued
2016
Date
2016
Author(s)
Cai, Zhong-Ting
Abstract
Adaptive cruise control (ACC) system is a key component of automotive autopilot. It assists the driver with automatic speed control that lessens driver’s burden in attention and vision. However, several shortcomings appear in the existing ACC system. First, the system is not applicable to many driving scenarios that the driver may encounter. Second, a large headway distance is necessary that diminishes road throughput and may incur cut-in situations. Third, frequent acceleration and deceleration makes the vehicle consume more energy. This thesis presents the self-optimizing adaptive cruise controller that can maintain vehicle speed under various driving resistance, follow leading car even in stop-and-go, and prevent from any collision while a car cut-in. This innovative design is developed on an in-wheel motor-powered electric vehicle that has a front millimeter-wave radar to detect leading car’s relative speed and distance. The self-optimizing adaptive cruise controller consists of a fuzzy PID controller and the adaptive optimal control (AOC) algorithm. Particularly, premise inputs are headway time and inverse time-to-collision (ITTC). Fuzzification actually divides the operating points of the vehicle system into several linear regions, each associated with a PID control law. The AOC algorithm is dedicated to adjust the PID parameters for achieving better cruising performance and energy efficiency. On a simulation system, the proposed design is examined in driving cycles such as UDDS, HWFET, US06, and LA92, including scenarios such as cut-in, stop & go, and car following. The self-optimizing adaptive cruise controller succeeds in every driving cycle and scenario, and outperforms a fuzzy logic controller in term of accumulated error and energy efficiency.
Subjects
Electric vehicle
Adaptive cruise control
Fuzzy PID controller
Adaptive optimal control
Self-optimizing controller
SDGs
Type
thesis
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