Heavy Duty Diesel Engine Modeling with Layered Artificial Neural Network Structures

2018 ◽  
Author(s):  
Anton D. Sediako ◽  
Jelena Andric ◽  
Jonas Sjoblom ◽  
Ethan Faghani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Network Structures ◽  
Heavy Duty ◽  
Engine Modeling ◽  
Heavy Duty Diesel Engine ◽  
Artificial Neural ◽  
Heavy Duty Diesel

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.

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.

Technical Note: Neural network modelling of the emissions and performance of a heavy-duty diesel engine

2000 ◽  
Vol 214 (2) ◽  
pp. 111-126 ◽  
Author(s):  
G. J. Thompson ◽  
C. M. Atkinson ◽  
N. N. Clark ◽  
T. W. Long ◽  
E Hanzevack
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Adaptive Learning ◽  
Internal Combustion Engines ◽  
Test Procedure ◽  
Exhaust Emissions ◽  
Neural Net ◽  
Heavy Duty ◽  
Heavy Duty Diesel Engine ◽  
Heavy Duty Diesel

Internal combustion engines are being required to comply with increasingly stringent government exhaust emissions regulations. Compression ignition (CI) piston engines will continue to be used in cost-sensitive fuel applications such as in heavy-duty buses and trucks, power generation, locomotives and off-highway applications, and will find application in hybrid electric vehicles. Close control of combustion in these engines will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. The engines of the future will require significantly more complex control than existing map-based control strategies, having many more degrees of freedom than those of today. Neural network (NN)-based engine modelling offers the potential for a multidimensional, adaptive, learning control system that does not require knowledge of the governing equations for engine performance or the combustion kinetics of emissions formation that a conventional map-based engine model requires. The application of a neural network to model the output torque and exhaust emissions from a modern heavy-duty diesel engine (Navistar T444E) is shown to be able to predict the continuous torque and exhaust emissions from a heavy-duty diesel engine for the Federal heavy-duty engine transient test procedure (FTP) cycle and two random cycles to within 5 per cent of their measured values after only 100 min of transient dynamometer training. Applications of such a neural net model include emissions virtual sensing, on-board diagnostics (OBD) and engine control strategy optimization.

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