scholarly journals Effects of artificial neural network speed-based inputs on heavy-duty vehicle emissions prediction

2004 ◽  
Author(s):  
Nastaran Hashemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vehicle Emissions ◽  
Heavy Duty ◽  
Heavy Duty Vehicle ◽  
Artificial Neural ◽  
Network Speed

Artificial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California

2007 ◽  
Vol 8 (4) ◽  
pp. 321-336 ◽  
Author(s):  
N Hashemi ◽  
N. N. Clark
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oxides Of Nitrogen ◽  
Heavy Duty ◽  
Predictive Tool ◽  
Diesel Vehicles ◽  
Artificial Neural ◽  
Heavy Duty Diesel ◽  
Artificial Neural Network Ann ◽  
The One

An artificial neural network (ANN) was trained on chassis dynamometer data and used to predict the oxides of nitrogen (NO x), carbon dioxide (CO2), hydrocarbons (HC), and carbon monoxide (CO) emitted from heavy-duty diesel vehicles. Axle speed, torque, their derivatives in different time steps, and two novel variables that defined speed variability over 150 seconds were defined as the inputs for the ANN. The novel variables were used to assist in predicting off-cycle emissions. Each species was considered individually as an output of the ANN. The ANN was trained on the Highway cycle and applied to the City/Suburban Heavy Vehicle Route (CSHVR) and Urban Dynamometer Driving Schedule (UDDS) with four different sets of inputs to predict the emissions for these vehicles. The research showed acceptable prediction results for the ANN, even for the one trained with only eight inputs of speed, torque, their first and second derivatives at one second, and two variables related to the speed pattern over the last 150 seconds. However, off-cycle operation (leading to high NO x emissions) was still difficult to model. The results showed an average accuracy of 0.97 for CO2, 0.89 for NO x, 0.70 for CO, and 0.48 for HC over the course of the CSHVR, Highway, and UDDS.

Artificial Neural Network Speed Profile Model for Construction Work Zones on High-Speed Highways

2007 ◽  
Vol 133 (3) ◽  
pp. 198-204 ◽  
Author(s):  
Douglas R. Taylor ◽  
Saravanan Muthiah ◽  
Bohdan T. Kulakowski ◽  
Kevin M. Mahoney ◽  
Richard J. Porter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Speed ◽  
Speed Profile ◽  
Construction Work ◽  
Work Zones ◽  
Profile Model ◽  
Artificial Neural ◽  
Network Speed

scholarly journals A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 279 ◽  
Author(s):  
Yi Dong ◽  
Jianmin Liu ◽  
Yanbin Liu ◽  
Xinyong Qiao ◽  
Xiaoming Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Heavy Duty ◽  
Performance Indexes ◽  
Heavy Duty Diesel Engine ◽  
Artificial Neural ◽  
Heavy Duty Diesel ◽  
And Performance

In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.

scholarly journals Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8592
Author(s):  
Sasanka Katreddi ◽  
Arvind Thiruvengadam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Fuel Consumption ◽  
Mean Square Error ◽  
Mean Square ◽  
Heavy Duty ◽  
Artificial Neural ◽  
Road Testing ◽  
Heavy Duty Vehicles

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.

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