Application of statistical approaches in IC engine calibration to enhance the performance and emission Characteristics: A methodological review

It is a well-known fact that the automotive industries have predominantly ruled the global industrial and commercial sectors. However, an increase in the demand for fossil fuels and stringent emission norms have paved a way for alternative fuels to power IC engines. To adapt the alternative fuels in IC engines, certain optimization protocols are followed by the automotive industry to save the time and cost involved in production, design and engine operating conditions. However, due to the broad nature of this subject in automotive applications, the optimization of engine operating conditions and their respective parameters are deeply delved into in this article. In order to guide the novice engine researchers, this article intents to critically elucidate the optimization techniques used by researchers for improving the performance of spark ignition and compression ignition engines systems and its various sub-systems. Initially, this article begins with experimental design plan approach for screening and optimization. Then elucidate about the single point optimization of Taguchi approach and multi-objective optimization of RSM approach. Further, provides the information of predictive optimization approach of Genetic algorithm in engine optimization and artificial neural networks for recognize the engine behaviour pattern. Moreover, this article also explains the certain methods to analyze the accuracy and model-fit significance for certain methodologies using an experimental case study dedicated solely for this article. From the review, it can be concluded that the statistical technique efficiently assists in exploring the in-depth engine function relation between the input variables.