Complex Vehicle Modeling: A Data Driven Approach
thesisposted on 31.01.2022, 15:31 authored by Alexander Christopher SchoenAlexander Christopher Schoen
This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks.
The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model.
The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created.
Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.
Degree TypeMaster of Science in Electrical and Computer Engineering
DepartmentElectrical and Computer Engineering
Advisor/Supervisor/Committee ChairZina Ben Miled
Additional Committee Member 2Euzeli Dos Santos
Additional Committee Member 3Brian King
- Automotive engineering not elsewhere classified
- Digital processor architectures
- Other information and computing sciences not elsewhere classified
- Environmental engineering not elsewhere classified
- Genetics not elsewhere classified
- Evolutionary computation
- Fuzzy computation
- Pattern recognition
- Data mining and knowledge discovery
- Modelling and simulation
Neural Network Predictionfuel consumption improvementsEnsemble AveragingComplex system modelingRefuse TruckDelivery TruckVehicle RoutingVehicle Routing ProblemSAE J1321synthetic data generationpower take-offAerodynamic SpeedCharacteristic AccelerationArtificial neural networkfeature importancespredictor importanceInfluence of WeightsPoint-wise Prediction ErrorMachine LearningAutomotive Engineering not elsewhere classifiedComputer EngineeringEnvironmental Engineering ModellingGenetic EngineeringNeural, Evolutionary and Fuzzy ComputationPattern Recognition and Data MiningSimulation and Modelling