Emerging Trends in Emission Optimization

The exhaust emission control is a vital aspect of automotive development, aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies, that requires extensive vehicle testing with both the fresh and aged catalysts under a variety of operating conditions, is a time-consuming process. The frequent changes in emission legislation creates new challenges for achieving a faster time to market and effective utilization of resources. So, the use of automation and machine learning (ML) in the domain of emission control is the need of the hour to proactively tackle these challenges. This paper attempts to explore emerging trends in emission optimization, such as the automated measurement process and ML/AI based data analysis, that can improve the overall process efficiency in the calibration of emission control strategies. The automated measurement process is achieved using the automation software. The data generated by automated programs could be used directly in the data analysis with minimal or no need for data cleaning. The machine learning (ML) models could be trained by historical data, from relevant vehicle platforms, to predict the output. The integration of machine learning (ML) models with automated measurement process further enhances the process by enabling model-based calibration development. The use of automated measurement programs and machine learning (ML) models could ensure high accuracy of the emission calibration data. This methodology could significantly reduce the need for volumes of measurements required for data analysis and calibration. This could further help in optimized usage of testing facilities, ultimately saving time and resources. This methodology also supports faster calibration development cycles that would be required for adhering to frequent legislative changes and achieving faster time to market.