Model Predictive Control of Solar-Based Electric Vehicle Charging using a Quadratic Bidirectional Buck/Boost Converter (QBBC)
Keywords:
Electric Vehicle, onboard PV array, DC-DC converter, Vector control, Induction motorAbstract
An Electric Vehicle (EV) setup with an onboard PV array is introduced in this work, integrating an advanced Model Predictive Control system depending on Artificial Neural Network (ANN). The setup comprises a fuel cell, an electrolyzer, and an onboard PV array. The PV cell's role is to support the fuel cell by supplying power when sufficient irradiation is available. During the vehicle's idle state, the PV-generated power is directed to the electrolyzer, converting it into chemical energy. The produced hydrogen is then stored in the hydrogen tank. A quadratic bidirectional buck-boost converter is employed to fulfill the voltage needs of the motor and energy sources. The system is subjected to thorough analysis under various irradiance and speed conditions. To enhance the efficiency and economic feasibility of the PV system, a Maximum Power Point Tracking (MPPT) algorithm is used to obtain the highest available power commencing the PV panel. The system incorporates an enhanced incremental conductance algorithm for MPP. The control strategy encompasses both outer voltage and inner current control, efficiently regulating the DC output voltage of the quadratic bidirectional buck-boost converter. The vehicle drive utilizes an indirect vector-controlled induction motor. ANN-based Model Predictive Control (MPC) is employed to regulate the motor's speed. The paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. To estimate the motor speed, an ANN estimator is employed. The equation for the predicted stator current is redefined and repositioned in accordance with the control procedure and ANN structure. MATLAB/SIMULINK is employed for simulations to assess the performance of the proposed EV configuration.
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