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Application of Soft Computing in the Field of Internal Combustion Engines: A Review

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Abstract

It is well known that fossil fuels are depleting day by day, and with the increase in the number of vehicles the pollution has reached at an alarming stage. The need of the hour is to find an alternate fuel as well as to demote the exhaust emission and enhance the performance parameters of the internal combustion (I.C.) engine. Researches on I.C. engines are being conducted in order to come to a feasible solution. Since performing experiments on an I.C. engine is both time consuming and costly therefore many soft computing techniques are being adopted in this field. The term soft computing refers to find the solution of an inexact problem. Different soft computing techniques being used in this field are Artificial Neural Network, Fuzzy Based Approach, Adaptive Neuro Fuzzy Inference System, Gene Expression Programming, Genetic Algorithm and Particle Swarm Optimization. The motive of this work is to review the researches being carried out in the field of I.C. engine on different types of engines with various alternative fuels using these soft computing techniques.

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  1. Department of Mechanical Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

    Nitin Shrivastava & Zubair Mohd. Khan

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Shrivastava, N., Khan, Z.M. Application of Soft Computing in the Field of Internal Combustion Engines: A Review. Arch Computat Methods Eng 25, 707–726 (2018). https://doi.org/10.1007/s11831-017-9212-9

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