ADVANCED CONTROL STRATEGIES FOR ELECTRIC VEHICLES

Abstract

The rapid proliferation of electric vehicles (EVs) has accentuated the need for advanced control strategies that ensure optimal performance, energy efficiency, stability, safety, and ride comfort. Modern EV control systems integrate powertrain control, energy management, motor drive algorithms, battery management, regenerative braking, and emerging intelligent control frameworks. This study presents a comprehensive analysis of state-of-the-art control strategies for EV applications, including classical methods (PID, sliding mode, LQR), predictive and model-based controls (Model Predictive Control, MPC), and intelligent data-driven approaches (adaptive, fuzzy logic, and deep learning-based controllers). Methodology encompasses literature synthesis, classification of control architectures, and evaluation of performance metrics across typical EV scenarios. Results highlight that advanced control techniques—especially MPC and intelligent learning-augmented methods—outperform traditional schemes in efficiency, robustness, and adaptability. Comparative tables and charts illustrate controller effectiveness under diverse operating conditions. Implications for future EV systems emphasize hybrid control frameworks, integration of machine learning with traditional controls, and cyber-secure adaptive algorithms. Limitations include computational demands and real-time implementation complexity. Future research directions propose lightweight AI controllers, real-time predictive optimization, and robust safety-critical strategies.