INTELLIGENT SLIDING MODE CONTROL OF HIGH-GAIN BIDIRECTIONAL DC–DC CONVERTERS FOR MODERN ELECTRIC VEHICLES

Abstract

The increasing demand for efficient and sustainable transportation has accelerated the adoption of electric vehicles (EVs), where effective power management plays a crucial role in overall system performance. A high-gain bidirectional DC–DC converter (HGBDC) is a key component that facilitates efficient energy transfer between the battery and the motor, particularly during motoring and regenerative braking operations. This work presents a comparative analysis of three control strategies—Proportional–Integral–Derivative (PID), Artificial Neural Network (ANN), and Sliding Mode Control (SMC)—applied to the HGBDC in EV applications. The PID controller offers simplicity and acceptable steady-state performance but struggles under nonlinear and dynamic conditions. The ANN controller improves adaptability and transient response through learning capability, though it introduces computational complexity and training requirements. In contrast, the SMC technique demonstrates superior robustness, faster dynamic response, and improved stability under varying operating conditions and system uncertainties. The proposed system is modeled and simulated using MATLAB/Simulink to evaluate performance in both forward motoring and regenerative braking modes. Comparative results indicate that SMC significantly reduces overshoot, settling time, and oscillations while enhancing efficiency. Hence, SMC is identified as the most effective control strategy for high-gain bidirectional converters in modern electric vehicle powertrains.