A Case Study of Emission Optimization using Machine Learning Bosch Global Software Technologies

Format:

Book

Conference/Event

Author/Creator:

Dhayanidhi, Hukumdeen, author.

Contributor:

A, Akash

Balasubramanian, Karthick

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

The Exhaust Emission Control is a vital part of automotive development aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies is a highly time-consuming process, which requires extensive vehicle testing under a variety of operating conditions. The frequent updates in emission legislation requires a high-efficiency process to achieve a faster time-to-market. The use of Machine Learning (ML) in the domain of emission calibration is the need of the hour to proactively improve the process efficiency and achieve a faster time-to-market. This paper attempts to explores emerging trend of Machine Learning (ML) based data analysis that have improved the overall process efficiency of emission control calibration. The data generated by automated programs could be used directly in data analysis with minimal or no need for data cleaning. The Machine Learning (ML) models could be trained by historical data from relevant engine platforms to predict the output. The integration of Machine Learning (ML) models with automated measurement processes further enhances the process by enabling model-based calibration development. The use of automated 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. A 70% overall savings in time and resources could be expected with the use of automation and machine learning models. This methodology also supports faster calibration development cycles that would be required for adhering to frequent legislative changes and achieving faster time-to-market

Notes:

Vendor supplied data

Publisher Number:

2026-26-0229

Access Restriction:

Restricted for use by site license