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

Wanshi Hong; Indrasis Chakraborty; Hong Wang

doi:10.1109/ICAMechS.2019.8861667

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

Wanshi Hong, Indrasis Chakraborty, Hong Wang

Buildings and Transportation Science Div

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
Publisher	IEEE Computer Society
Pages	142-147
Number of pages	6
ISBN (Electronic)	9781728134802
DOIs	https://doi.org/10.1109/ICAMechS.2019.8861667
State	Published - Aug 2019
Event	2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019 - Kusatsu, Shiga, Japan Duration: Aug 26 2019 → Aug 28 2019

Publication series

Name	International Conference on Advanced Mechatronic Systems, ICAMechS
Volume	2019-August
ISSN (Print)	2325-0682
ISSN (Electronic)	2325-0690

Conference

Conference	2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
Country/Territory	Japan
City	Kusatsu, Shiga
Period	08/26/19 → 08/28/19

Funding

This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan )

Keywords

Hybrid Electric Vehicle
Neural Network
Parameter Optimization

Access to Document

10.1109/ICAMechS.2019.8861667

Cite this

Hong, W., Chakraborty, I., & Wang, H. (2019). Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. In Proceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019 (pp. 142-147). Article 8861667 (International Conference on Advanced Mechatronic Systems, ICAMechS; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/ICAMechS.2019.8861667

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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.",

keywords = "Hybrid Electric Vehicle, Neural Network, Parameter Optimization",

author = "Wanshi Hong and Indrasis Chakraborty and Hong Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019 ; Conference date: 26-08-2019 Through 28-08-2019",

year = "2019",

month = aug,

doi = "10.1109/ICAMechS.2019.8861667",

language = "English",

series = "International Conference on Advanced Mechatronic Systems, ICAMechS",

publisher = "IEEE Computer Society",

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}

Hong, W, Chakraborty, I & Wang, H 2019, Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. in Proceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019., 8861667, International Conference on Advanced Mechatronic Systems, ICAMechS, vol. 2019-August, IEEE Computer Society, pp. 142-147, 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019, Kusatsu, Shiga, Japan, 08/26/19. https://doi.org/10.1109/ICAMechS.2019.8861667

Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. / Hong, Wanshi; Chakraborty, Indrasis; Wang, Hong.
Proceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019. IEEE Computer Society, 2019. p. 142-147 8861667 (International Conference on Advanced Mechatronic Systems, ICAMechS; Vol. 2019-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Neural Network

KW - Parameter Optimization

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Hong W, Chakraborty I, Wang H. Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information. In Proceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019. IEEE Computer Society. 2019. p. 142-147. 8861667. (International Conference on Advanced Mechatronic Systems, ICAMechS). doi: 10.1109/ICAMechS.2019.8861667

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

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