Hybrid Electric Vehicle Emissions Monitoring and Estimation using Artificial Neural Networks: Technical Note

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Published: Dec 3, 2019
DOI: https://doi.org/10.4273/ijvss.11.3.06
Keywords:
Emission Factors, Hybrid Electric Vehicle, Discrete Fourier Transform, Artificial neural networks

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K. Sujatha
Dept. of EEE, Dr. MGR Educational and Research Institute, Chennai
V. Karthikeyan
Dept. of EEE, Saveetha School of Engg., Chennai
V. Balaji
Dept. of EEE, Saveetha School of Engg., Chennai
N.P.G. Bhavani
Dept. of EEE, Meenakshi College of Engg., Chennai
V. Srividhya
Dept. of EEE, Meenakshi College of Engg., Chennai
R. Krishnakumar
Sch. of Electrical Engg., Vels Inst. of Sci., Tech. and Adv. Studies, Chennai
R. Sridhar
Dept., of Mech. Engg., Vels Inst. of Sci., Tech. and Adv. Studies, Chennai

Abstract

Power is utilized as the prime fuel for hybrid and module electric vehicles in order to build the productivity of commercial vehicles. This paper forecasts the emission factors utilizing discrete Fourier transform, artificial neural networks and hybridization of back propagation algorithm. The DFT facilitates the extraction of the performance indicators which are otherwise called the features. The coefficients of the power spectrum denote the performance indicators. The ANN learns the pattern for emissions from HEVs using these performance indicators. This ANN based strategy offers an optimal control action to detect and reduce the exhaust gas emissions which are hazardous. These vehicles are provided with automated highway traffic Jam assist. Hence the forecast of these emissions offers increased efficiency of 90% to 100% thereby ensuring optimal operating condition for the hybrid vehicles.

Article Details

Issue
Vol. 11 No. 3 (2019)
Section
Articles
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