Efficient Energy Management in a Hybrid Electric Vehicle Using a Double Cascade Boost Converter With ANN Optimized MPPT Technique

The increasing adoption of Electric Vehicles (EVs) underscores the growing need for efficient propulsion with Energy Management Systems (EMS). An advanced EMS that integrates Photovoltaic (PV) systems with advanced converters and optimization approaches for a sustainable solution is required for energy management in EVs. Hence, this paper proposes a double cascade boost converter with an ANN-optimized MPPT technique for enhanced efficient energy management in hybrid electric vehicles. The Double Cascade Boost Converter (DCBC) is designed to enhance efficiency by reducing switching losses and providing a high voltage gain. For efficient energy extraction from the PV system under varying solar conditions, an Artificial Neural Network (ANN) optimized Secretary Bird Optimization (SBO) based Maximum Power Point Tracking (MPPT) approach is used. The motor drive uses a 3-phase Voltage Source Inverter (VSI), which converts DC into AC for driving a Brushless Direct Current (BLDC) motor, with a Recurrent Neural Network (RNN) controller employed for precise speed control of the motor. On the Energy Storage System, a Bidirectional DC-DC Converter is integrated for both supercapacitors and storage batteries, providing better predictions of State of Charge (SoC) and battery lifespan of the RNN controller. Simulation outcomes obtained from MATLAB validation illustrate that the proposed work outperforms conventional methodologies in energy conversion efficiency (98.12%) and tracking accuracy (98%).