Automotive Data Analytics, Methods, Design of Experiments (DoE)

The book will expand on the topics discussed in the precursors entitled "DoE in Powertrain Development" with the related areas of "machine learning" and "big data". Now it its ninth outing, it will thus be a forum on which to critically engage with the future challenges of the digital revolution.
Real driving emissions (RDE), worldwide harmonized light-duty test procedures (WLTP) and the next round of CO2 guidelines all demand ongoing technical refinement of the drive train. The combination of changed environmental requirements, stricter limit values and new measurement techniques additionally require changes to existing processes and the development of new methods.

To reduce costs, many OEMs are scaling down the size of their engine ranges. A small number of standard engines are then installed in numerous vehicle models with minor hardware modifications. The result is an increased focus on the use of derivatives and the systematic validation of an application.

Contents:
Machine learning and artificial intelligence for engine calibration
- Big Data and Machine Learning Made Easy
- Automated Calibration Using Simulation and Robust Design Optimization Improving Shift and Launch Quality of Automatic Transmissions
- Development of a Simulation Platform for Validation and Optimisation of Real-World Emissions
- Implementation of data-based models using dedicated machine learning hardware (AMU) and ist impact on function development and the calibration processes
- The Global DoE Model Based Calibration and the Test Automation of the Gasoline Engine
- Optimization of ECU Map Sampling Point Values and Positions with Model-Based Calibration
- Dynamic Route-Based Design of Experiments (R-DoE)
- System for Real-time Evaluation of Real Drive Emission (RDE) Data
- System optimization for automated calibration of ECU functions
- Dynamic MBC Methodology for Transient Engine Combustion Optimization
- Implementing a real time exhaust gas temperature model for a