Venkatesan MANI1*, Srinivasa Rao YARLAGADDA2, Srikanth RAVIPATI2, Subashkumar CHELLAPPAN SWARNAMMA3
1 Vignan’s Lara Institute of Technology &Science,Andhra Pradesh, India
firstname.lastname@example.org (*Corresponding author)
2 Vignan’s Foundation for Science, Technology & Research, Andhra Pradesh, India
3 PSG Institute of Technology and Applied Research, Coimbatore, India
Abstract: The automobile industry is focusing on renewable power sources for driving Electric Vehicles (EVs), which results in the reduction of pollution. This paper presents an Artificial Neural Network (ANN) optimized hybrid Energy Management System (EMS), which was designed for solar Photovoltaic (PV) Electric Vehicles (EVs). In the proposed EMS, two DC-DC converters are utilized, namely a High Gain Interleaved Boost Converter (HGIBC) and a conventional boost converter. The main use of the HGIBC is to harvest maximum power from the solar PV panel which is accomplished with the help of a Model Predictive Controller (MPC) and the other DC-DC converter is used for maintaining the DC link voltage constant. The model predictive controller not only controls the parameters involved, but it can also predict a future change in these parameters, which cannot be performed by conventional controllers. The purpose of this paper is to propose a hybrid energy supply systemfor EVs based on a Battery and an Ultra-Capacitor. The energy of the battery and of the UC is controlled by an ANN controller and also evaluated by means of a conventional PI controller. Based on the simulation results, it can be concluded that the ANN controller showed a better performance in comparison with the Proportional Integral (PI) controller. The entire structure was analysed for various conditions of the State of Charge (SoC) of the Battery using MATLAB/Simulink.
Keywords: Electric Vehicle, Artificial Neural Network, EMS, HGIBC, MPC.
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Venkatesan MANI, Srinivasa Rao YARLAGADDA, Srikanth RAVIPATI, Subashkumar CHELLAPPAN SWARNAMMA, ANN Optimized Hybrid Energy Management Control System for Electric Vehicles, Studies in Informatics and Control, ISSN 1220-1766, vol. 32(1), pp. 101-110, 2023. https://doi.org/10.24846/v32i1y202310