Modelling of Electric Vehicle with PID Controller Transfer Function using GA and Model-Reduced Order DRA Algorithm

Abstract

In this paper, a model of an electric vehicle transfer function using GA and a model-reduced order discrete time realization (DRA) algorithm is presented. The electric vehicle (EV) control system regulates vehicle speed according to the driver’s command signal and brings the vehicle to its equilibrium point, i.e., the desired speed under any abnormal conditions. The controller transfer function is designed based on EV's dynamic differential equations. An infinite-order transcendental transfer function for the EV model is approximated to find high-reliability discrete-time state-space reduced-order models (ROMs). Reduced the robustness and increased the predictions' accuracy of ROM output in terms of accurate numerical simulations of the EV transfer function. Also, the design of the PID controller parameter using optimization techniques (OT) for the tuning of conventional controllers for the desired speed is done using a linearized model. In this study, the performance of a conventional PID controller is compared with that of a GA-based PID controller. In terms of maximum overshoot, peak time, rising time, settling time, and steady state error, the simulation results show that the suggested controller is preferable.

Performance Enhancement of Boost Converter for Solar Panels System using Genetic Algorithm

“…The proposed approach is illustrated as follows: In order to enable the controller to be tuned and converge more quickly, GA first produces a stochastic population that is performed with a small size population [23]. The three-term controller parameters 𝑘 𝑝 , ki, and kd are encoded into binary strings known as chromosomes to set the initial population [25], [26]. Each chromosome's fitness is determined by translating its binary code into such a real value which stands for the controller parameters gain, which will be the input to controller.…”