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

Conference · Sun Sep 01 00:00:00 EDT 2019

DOI:https://doi.org/10.1109/VTCFall.2019.8891239· OSTI ID:1566975

Hong, Wanshi  ^[1]; Chakraborty, Indrasis ^[2]; Wang, Hong ^[1]

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1. ORNL
2. Pacific Northwest National Laboratory (PNNL)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1566975
• Resource Relation: Conference: 2nd IEEE Connected and Automated Vehicles Symposium (IEEE CAVS 2019) - Honolulu, Hawaii, United States of America - 9/22/2019 12:00:00 PM-9/23/2019 12:00:00 PM
• Country of Publication: United States
• Language: English