skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information

Abstract

A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach light-off temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.

Authors:
 [1]; ORCiD logo [2];  [3]
  1. Other
  2. BATTELLE (PACIFIC NW LAB)
  3. Oak Ridge National Laboratory
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1572780
Report Number(s):
PNNL-SA-145204
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Advanced Mechatronic Systems (ICAMechS 2019), August 26-28, 2019, Kusatsu, Shiga, Japan
Country of Publication:
United States
Language:
English
Subject:
V2V, Cooptimization

Citation Formats

Hong, Wanshi, Chakraborty, Indrasis, and Wang, Hong. Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. United States: N. p., 2019. Web. doi:10.1109/ICAMechS.2019.8861667.
Hong, Wanshi, Chakraborty, Indrasis, & Wang, Hong. Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. United States. doi:10.1109/ICAMechS.2019.8861667.
Hong, Wanshi, Chakraborty, Indrasis, and Wang, Hong. Thu . "Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information". United States. doi:10.1109/ICAMechS.2019.8861667. https://www.osti.gov/servlets/purl/1572780.
@article{osti_1572780,
title = {Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information},
author = {Hong, Wanshi and Chakraborty, Indrasis and Wang, Hong},
abstractNote = {A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach light-off temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.},
doi = {10.1109/ICAMechS.2019.8861667},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {10}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: