Abstract
Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error.
Highlights
The fuel efficiency of heavy-duty trucks can be beneficial for the automotive and transportation industry and for a country’s economy and the global environment [1,2]
As per Environmental Protection Agency (EPA) reports, 28% of total greenhouse gas emissions come from transportation [5]
The United States Environmental Protection Agency (US EPA) has introduced Corporate Average Fuel Economy (CAFÉ) standards enforcing automotive manufacturers to be compliant with standards to regulate fuel consumption [6,7]
Summary
The fuel efficiency of heavy-duty trucks can be beneficial for the automotive and transportation industry and for a country’s economy and the global environment [1,2]. The era of big data and artificial intelligence has enabled the modeling of huge volumes of data for companies to reduce emissions and fuel consumption Machine learning techniques such as support vector machine (SVM) [19], random forest (RF) [20], and artificial neural networks (ANN) [21,22] are widely applied to turn data into meaningful insights and solve complex problems. These techniques have been applied to estimate emissions and fuel consumption in motor vehicles [23], trucks [24], ships [25], and aircraft [26]. Cubic Capacity, Quantity of cylinders, Quantity of valves, Maximum Power, Maximum Torque, Compression Rate, Kerb Weight of Vehicle, Type of Engine, Fuel Injection, Type of charge, Gearbox, Drivetrain
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