Title: Hybrid approach for energy management strategy and energy management strategy for plug-in hybrid electric vehicles using GTO-DRN approach

Authors: A. Shri Vindhya; L. Rama Parvathy; K. Paul Joshua; Chinthalacheruvu Venkata Krishna Reddy

Addresses: Department of Computer Science and Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Chennai, 602105, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 632014, Tamil Nadu, India ' Department of ECE, Sri Shanmugha College of Engineering and Technology, Sankari, Salem, 637304, Tamil Nadu, India; Sri Shanmugha Educational Institutions, Sankari, Salem, 637304, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India

Abstract: This manuscript presents a hybrid approach to energy management strategy for plug-in-hybrid electric vehicles (PHEV). The proposed hybrid technique is the joint execution of the Gorilla Troops Optimization Algorithm (GTO) and dilated residual convolutional neural networks (DRN). Hence it is named as GTO-DRN system. The proposed method's main objective is to minimise the energy loss and reduces the cost. The GTO method is utilised to optimise the battery by developing an interaction among the independent operating parameters, voltage and current, using response surface methodology. The GTO method is optimised to charge or discharge the battery, and other control decisions to minimise energy consumption or emissions. The DRN technique is used to model and predict the vehicle's power demand.

Keywords: PHEV; plug-in-hybrid electric vehicles; charge; Gorilla Trops Optimization; DRN; dilated residual convolutional neural networks; battery; energy loss.

DOI: 10.1504/IJHVS.2025.147100

International Journal of Heavy Vehicle Systems, 2025 Vol.32 No.3, pp.392 - 415

Received: 27 Dec 2023
Accepted: 23 Jan 2024
Published online: 10 Jul 2025 *

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