Heuristic Adaptive Dynamic Programming-based Energy Optimization Strategies for Hybrid Electric Vehicles

This study suggests a more accurate series-parallel hybrid electric vehicle (HEV) energy management control technique based on heuristic adaptive dynamic programming (HADP). This article provides a solution to the energy management problem of Electric Vehicle using the Stimulant Dependent Heuristic...

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Bibliographic Details
Published in:	Tehnički vjesnik Vol. 31; no. 1; pp. 145 - 150
Main Authors:	Yasin, Mohammad, Abirami, Manoharan, Kbvsr, Subrahmanyam
Format:	Journal Article Paper
Language:	English
Published:	Slavonski Baod University of Osijek 01-01-2024 Josipa Jurja Strossmayer University of Osijek Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
Subjects:	Adaptive algorithms Adaptive control Algorithms Automobiles, Electric Back propagation networks Batteries Charge efficiency Control methods Decision theory Distance learning Dynamic programming Electric vehicles Energy management energy management system Feedforward control Force and energy Heuristic Hybrid electric vehicles Hybrid vehicles Machine learning neural network Neural networks SDHADP algorithm
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Summary:	This study suggests a more accurate series-parallel hybrid electric vehicle (HEV) energy management control technique based on heuristic adaptive dynamic programming (HADP). This article provides a solution to the energy management problem of Electric Vehicle using the Stimulant Dependent Heuristic Adaptive Dynamic Programming (SDHADP) algorithm. The reinforced Q learning model along with the three-layer feed-forward neural network with backpropagation is detailed with the necessary diagrams and equations. The approach to connecting the battery of a BEV to a residential system is novel and innovative. The experimental results highlight the importance and effectiveness of the SDHADP algorithm using an electric vehicle. This is done with the help of a BV feed-forward neural network with back-propagation. In the model, which is in charge of controlling the system based on the training given by the BV network, Markov decision theory was adopted by scholars as a paradigm for posing and solving planning problems in the face of uncertainty and is in charge of the battery management system.The suggested technique uses online learning control methods to improve upon the efficiency of the traditional Q-learning method by more than 4.55% under the specified real-world driving circumstances.
Bibliography:	312893
ISSN:	1330-3651 1848-6339
DOI:	10.17559/TV-20230430000589