Complex Vehicle Modeling: A Data Driven Approach

• Main Author: Schoen, Alexander C.
• Other Authors: Ben Miled, Zina
• Language: en
• Published: 2019
• Subjects: Neural Network Prediction; Fuel Consumption Improvement; Ensemble Learning; Refuse Truck; Complex System Modeling; Delivery Truck; Vehicle Routing; SAE J1321; Synthetic Data Generation; Aerodynamic Speed; Characteristic Acceleration; Feature Importance; Influence of Weights; Machine Learning; Point-wise Error; Artificial Neural Network
• Online Access: http://hdl.handle.net/1805/21466

Indiana University-Purdue University Indianapolis (IUPUI) === 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.