文章基本信息

标题：Dynamic Indicated Torque Estimation for Turbocharged Diesel Engines Based on Back Propagation Neural Network
本地全文：下载
作者：Donghao Hao ; Changlu Zhao ; Ying Huang Gang Li 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：31
页码：720-725
DOI：10.1016/j.ifacol.2018.10.164
语种：English
出版社：Elsevier
摘要：AbstractAn indicated torque estimation model is presented for turbocharged diesel engines considering both steady-state and transient operating conditions. The proposed model consists of two submodels: a steady-state indicated torque model and a transient torque coefficient model. By combining the steady-state torque with the transient torque coefficient from the two proposed submodels, dynamic indicated torque is obtained. The transient torque coefficient is calculated by training a designed back-propagation neural network (BPNN) using transient test data obtained from the designed experiments based on a DEUTZ BF6M1015 turbocharged diesel engine bench. Only the engine speed, the cycle fuel quantity and the intake air pressure are needed for dynamic torque estimation. The generalization capacity and dynamic torque estimation accuracy of the torque estimation model are validated. The maximum error of the estimated torque is within 8% while the average error is within 2% in both fuel step change and slow change conditions.
关键词：KeywordsInternal combustion engineturbocharged diesel enginecalibrationvalidationBP neural networkartificial neural networkindicated torqueestimation