Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle

Abstract

The phenomenal growth of the Electric Vehicle (EV) technology demands efficient and intelligent control strategies for the propulsion system. In this work, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively. The controller parameters and the fuzzy logic controller's membership functions are tuned and updated in real-time using the multi-objective ACO technique. The proposed controller's speed tracking performance is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform. The proposed controller outperforms the ACO-based fuzzy integer-order PID (IOPID), FOPID, and traditional IOPID controllers. The sensitivity analysis confirms the robustness of the proposed controller for varying parameters of the EV model. The stabilization of EV speed in the presence of external disturbance is also confirmed. In the proposed work, an attempt is made to analyze the system's stability using Matignon's theorem, considering the linearized EV model. The proposed controller gives optimum speed tracking performance compared to the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) based fuzzy FOPID controllers. Additionally, the optimized fuzzy FOPID controller is realized using a secondgeneration current conveyor with extra inputs (EX-CCII) and fractional-order capacitors with electronic tunability. The controller circuit's performance evaluation is carried out in the Cadence Analog Design Environment using GPDK 180 nm CMOS process.

AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

“…Derivative control action cannot be utilized alone because this control is efficient only during transient period [30]. The PID controller decreases the time response with less overshoot and most common technique is the control by a great margin.…”

“… computational work than optimization processes, iterative techniques, or simple heuristics. Intrinsically, they are effective methods for optimization processes [33]. Most academic writing on metaheuristics is new, illustrating practical solutions based on computer tests with the systems.…”

Series Motor Four Quadrants DC Chopper For DC Drive Electric Vehicle Driving Cycle Test

“…An in-depth analysis of the chopper on simulation model development [2], Controlling chopper operation [3][4][5] according to earth terrain and under specifics requirements such state of charge (SOC) was done using expert systems [4], adaptive neuro fuzzy inference system (ANFIS). A New European Driving Cycle (NEDC) [5][6][7][8] test for testing vehicle performance and emission which is part of the requirement of commercial vehicle can be extended to Electric Vehicle. The expected vehicle result pattern can be followed using simulation model that can have long duration and high speed simulation time such as one developed using anumerical representation utilising Taylor series [9].…”

In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control

“…The fuzzy self-tuning PID control method of the combined brake of the in-wheel motor-driven electric vehicle was studied. George et al 14 used the ant colony optimization (ACO) algorithm to tune the parameters of the PID controller to effectively control the speed of electric vehicles. Dutta and Nayak 15 reported that brushless DC motors were superior to brushed DC motors.…”