TY - JOUR
T1 - Artificial neural network–based performance modeling of a diesel engine within the whole operating region considering dynamic conditions
AU - Sun, Liang
AU - Wei, Wei
AU - Yan, Qingdong
AU - Jian, Hongchao
N1 - Publisher Copyright:
© IMechE 2018.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Engine performance under full working conditions, especially dynamic ones, is indispensable in many vehicle-level research fields. To acquire the engine performance parameters, a novel whole-region engine model, considering both steady and dynamic conditions, was developed based on limited test data in this work. This model used throttle position, engine speed, and its acceleration as the input variables to predict torque and brake-specific fuel consumption under all practical conditions within its operating envelope. The engine bench test was first conducted under typical operating conditions to collect test data for model development and validation. Then, the backpropagation neural network with designed structure was employed to perform data fitting for test conditions. After the analysis of parameter distribution tendency, the two-step interpolation method was used to generalize performance parameters under conditions apart from those test ones. The cross-condition prediction accuracy of developed engine model was validated by test data under various operating conditions. Also, the parameter prediction error of proposed modeling method was lower compared to that of existing neural network methods, which further proved its applicability to dynamic engine modeling issues.
AB - Engine performance under full working conditions, especially dynamic ones, is indispensable in many vehicle-level research fields. To acquire the engine performance parameters, a novel whole-region engine model, considering both steady and dynamic conditions, was developed based on limited test data in this work. This model used throttle position, engine speed, and its acceleration as the input variables to predict torque and brake-specific fuel consumption under all practical conditions within its operating envelope. The engine bench test was first conducted under typical operating conditions to collect test data for model development and validation. Then, the backpropagation neural network with designed structure was employed to perform data fitting for test conditions. After the analysis of parameter distribution tendency, the two-step interpolation method was used to generalize performance parameters under conditions apart from those test ones. The cross-condition prediction accuracy of developed engine model was validated by test data under various operating conditions. Also, the parameter prediction error of proposed modeling method was lower compared to that of existing neural network methods, which further proved its applicability to dynamic engine modeling issues.
KW - Engine performance modeling
KW - artificial neural network
KW - cross-condition prediction
KW - two-step interpolation
UR - http://www.scopus.com/inward/record.url?scp=85061041218&partnerID=8YFLogxK
U2 - 10.1177/0954407018812352
DO - 10.1177/0954407018812352
M3 - Article
AN - SCOPUS:85061041218
SN - 0954-4070
VL - 233
SP - 2970
EP - 2984
JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
IS - 11
ER -