This project aims to develop a model to predict vehicle emissions based on various engine parameters. The primary focus is on predicting NOx, PM 1, CO2 and pressure cylinder values. The project uses a regression model trained on a dataset containing multiple engine parameters such as engine speed, engine load, rail pressure, air supply, crank angle, intake pressure, back pressure, intake temperature, and more.
The dataset used for this project includes the following features:
- Engine speed (rpm)
- Engine load
- Rail pressure
- Air supply
- Crank angle
- Intake pressure
- Back pressure
- Intake temperature
- NOx (target variable)
- PM 1 (target variable)
- CO2 (target variable)
- PM 2 (it has highly inpredictable behavior so we will not target it)
- Pressure cylinder (target variable)
The dataset is formatted as a CSV file with each row representing a different set of engine parameters and corresponding emission values. We sarted with 100 Data and then trained it on up to 500.
- Find the Optimal model parameters for the given data
- Compare the performances of Models (MSE & R^2)
- Optimal Feature Selection
- Multiple Testing and performance monitoring
Rule 1 : Datapoint cost on the basis of their penalty
- No Violation : -1
- PM1 Violation : -2
- Pressure Violation : -11
Rule 2 : For a batch size of 80 credits we should get 60 datapoints
Rule 3 : Datapoints are rewarded as higher value they produce
- No Violation : +1
- PM1 Violation : +3
- Pressure Violation : +9
No Violation : 49 PM1 Violation : 10 Pressure Violation : 1
So we decided that:
Choosen Model : Stepwise Linear Regresion
Training points : 217 Datapoints
Number of steps : 1000
Features selected : 7/8
Then we satrted focusing on Maximizing the coverage of usable region
Mean Coverage of Parameters : 91.63
How is the prediction looking like :
