Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control

Abstract

Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics.

Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

“…Parameter τ can be determined as one third of the system settling time (considering a band of 5%), which is 1.13 s, hence, τ corresponds to 0.38 s [28]. In this way, the plant model is given by Equation (14). It should be noted that the proposed control system is designed to regulate the angular position of the electromechanical system θ(t).…”

“…Concerning robotic applications using fuzzy logic, reference [14] employs a fuzzy control Proportional Integral (PI) in a robotic driving system for hybrid electric vehicles in a way that allows a reliable evaluation of energy efficiency. This depicts the relevance of the efficiency of fuel in hybrid vehicles.…”

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

“…To deal with these problems, a robot driver system that manipulates electric signals was developed [5]. Instead of physical manipulators, this system controls the test vehicle with a controller area network (CAN) and electric signals.…”

“…Moreover, for driving the complicated drive cycle, it was difficult to control the vehicle with static gain values. To solve this problem, [5] used the fuzzy-logic based PI controller to dynamically modify the PI gains and proved that its performance was better than the conventional PI controlled system. However, because the fuzzy logic operates with simple triangular membership functions, it's driving style tends to be aggressive compared with different control methods.…”

A New Event-Triggered Type-3 Fuzzy Control System for Multi-Agent Systems: Optimal Economic Efficient Approach for Actuator Activating

“…One of the challenging topics in the field of MASs is the designing of effective control systems. The dynamics of agents can be uncertain and also the closed-loop system dynamics are commonly perturbed by various faults [4].…”