Effectual Energy Consumption and User Comfort Optimization Based on Dynamic User Set Parameters in Electric Vehicles

The reduction of energy usage and the enhancement of comfort indices within electric vehicles stand as key areas of focus for modern research. Numerous approaches have been devised to address these concerns, yet each of these models exhibit certain constraints; some effectively reduce energy usage yet do so at the cost of comfort, whereas others succeed in elevating comfort at the price of increased energy consumption. Our research introduces a comprehensive model that leverages optimization, machine learning, data smoothing, and control protocols to defeat these challenges. The purpose of the proposed model is two folds: firstly, to reduce energy use, and secondly, to enhance the comfort of the user. Our proposed model comprises three main modules: the smoothing, the optimization, and control modules. In the smoothing module, the alpha-beta filter, the simplest and most effective filter, has been used to remove noise and smooth the data. The optimization module is segmented into two additional modules: the FA-GA module and the support vector machine module. The purpose of the support vector machine in the optimization module is to make the system fully automatic and dynamic by setting user parameters in the objective function of the FA-GA module. Within the control module, Mamdani fuzzy logic is employed to deliver the appropriate energy to the respective actuators. The proposed technique is evaluated against several proven methods, with results suggesting that our approach achieves commendable results in comparison to similar algorithms.