The motivation for this project at Honda is to develop strategies for BS6 norms and create tests for fuel economy and emission constraints. In this project, data is being collected in large volumes through telematics from field vehicles of different makes and operating in multiple geographical and climatic conditions. These varying operating conditions and driving patterns lead to varying vehicle performance, drive efficiency, and emissions. Furthermore, it leads to a vast difference in calibration needs since performance changes with the different riding and operating conditions of the field vehicle.

The challenges in this project were mainly the volume of the data that had to be churned to come up with any valid analysis, leading to a big data problem. The analysis had to be performed for data exploration, and feature engineering was aimed at achieving an understanding of the fuel economy profile for different geographical areas, variations based on temperature, and geographical terrain, as well as generating drive cycles capturing real-world driving scenarios. The team at Honda also needed to scale up to reduce the computational time due to the huge amount of data.

In this presentation, you will learn how the key challenges described above were addressed using MATLAB^® and toolboxes.