WO/HBO Internship: CO2 and fuel consumption analysis

As an intern, you will further strengthen the correlation between CO2 and BSFC results from our certification and development test cells. This increases the value of development data early in the product development process. Your tasks are:

During the internship you will learn about emissions legislation, test cell measurement technology, uncertainty analysis and statistical modelling with real industrial data. You will work in a development environment for heavy-duty engines.

• You compare the calculation methods and map where their assumptions differ. Examples include fuel composition, humidity correction, CO2 from DEF (AdBlue) dosing and analyser drift;
• You use test data and uncertainty analysis, such as Monte Carlo simulation, to find out which factors contribute most to the differences;
• You build a prediction model that estimates the expected certification value, including an uncertainty interval, from development test conditions;
• You advise which measurement or procedural improvements would best strengthen the correlation between the two cell types;
• You deliver a report, a validated calculation tool (Python/MATLAB) and a final presentation.

• You compare the calculation methods and map where their assumptions differ. Examples include fuel composition, humidity correction, CO2 from DEF (AdBlue) dosing and analyser drift;
• You use test data and uncertainty analysis, such as Monte Carlo simulation, to find out which factors contribute most to the differences;
• You build a prediction model that estimates the expected certification value, including an uncertainty interval, from development test conditions;
• You advise which measurement or procedural improvements would best strengthen the correlation between the two cell types;
• You deliver a report, a validated calculation tool (Python/MATLAB) and a final presentation.

• You compare the calculation methods and map where their assumptions differ. Examples include fuel composition, humidity correction, CO2 from DEF (AdBlue) dosing and analyser drift;
• You use test data and uncertainty analysis, such as Monte Carlo simulation, to find out which factors contribute most to the differences;
• You build a prediction model that estimates the expected certification value, including an uncertainty interval, from development test conditions;
• You advise which measurement or procedural improvements would best strengthen the correlation between the two cell types;
• You deliver a report, a validated calculation tool (Python/MATLAB) and a final presentation.

• You compare the calculation methods and map where their assumptions differ. Examples include fuel composition, humidity correction, CO2 from DEF (AdBlue) dosing and analyser drift;
• You use test data and uncertainty analysis, such as Monte Carlo simulation, to find out which factors contribute most to the differences;
• You build a prediction model that estimates the expected certification value, including an uncertainty interval, from development test conditions;
• You advise which measurement or procedural improvements would best strengthen the correlation between the two cell types;
• You deliver a report, a validated calculation tool (Python/MATLAB) and a final presentation.