Intelligent energy management of a novel non-isolated bidirectional dual-input single-output converter for electric vehicle applications

For use in electric vehicle (EV) applications, this paper suggests a non-isolated bidirectional Dual-Input Single-Output (DISO) DC–DC converter with an intelligent energy management method. The proposed converter integrates photovoltaic (PV) and battery energy sources in a single power conversion structure, which results in fewer components, higher efficiency and bidirectional power flow capability. The converter is designed based on a two-phase interleaved boost topology with a relay-assisted reconfiguration stage that facilitates flexible source selection, battery charging/discharging and regenerative braking operation. A Modified Deep Neural Network (MODNN) based intelligent energy management strategy is proposed to enhance energy utilization and adaptive power sharing. The proposed MODNN modifies a conventional single-task feedforward DNN by introducing a shared-trunk, multi-head architecture that jointly predicts the discrete operating mode and the continuous converter duty ratios through a composite classification-regression loss function, benchmarked against recent machine-learning baselines (k-NN, a bagged neural-network ensemble, and LSTM) under identical training/test conditions. In the proposed MODNN framework, the PV voltage, PV current, battery state-of-charge, battery voltage, and load demand information are used to decide the optimal operating mode and power flow condition under different operating scenarios. The converter provides flexible power distribution between available energy sources, productive use of regenerative braking energy, uninterrupted load supply and effective battery management. The performance of the proposed converter is verified by MATLAB/Simulink simulations, and the converter topology is experimentally validated with a laboratory prototype of 500 W. The experimental results show that the five operating modes operate successfully with a peak efficiency of 96.7%, which validates the effectiveness of the proposed converter for EV energy management applications.