Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

被引:0
作者
Hong, Wanshi [1 ]
Chakraborty, Indrasis [2 ]
Wang, Hong [3 ]
机构
[1] Univ Virginia, Dept Elect & Comp Engn, Charlottesville, VA 22904 USA
[2] Pacific Northwest Natl Lab, Optimizat & Control Grp, Richland, WA 99354 USA
[3] Oak Ridge Natl Lab, POB 2009, Oak Ridge, TN 37830 USA
来源
2019 IEEE 90TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-FALL) | 2019年
关键词
Parameter optimization; V2X; Powertrain modeling; Neural Network;
D O I
10.1109/vtcfall.2019.8891239
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.
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页数:6
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