Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles

Autonomous driving has become a major goal of automobile manufacturers and an important driver for the vehicular technology. Hybrid electric vehicles (HEVs), which represent a trade-off between conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), have gained popularity...

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Published in:Proceedings - ACM IEEE Design Automation Conference pp. 1 - 6
Main Authors: Yanzhi Wang, Xue Lin, Pedram, Massoud, Naehyuck Chang
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2015
Subjects:
Batteries
Fuels
Hybrid electric vehicles
Ice
Power demand
Propulsion
ISSN:0738-100X
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Abstract Autonomous driving has become a major goal of automobile manufacturers and an important driver for the vehicular technology. Hybrid electric vehicles (HEVs), which represent a trade-off between conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), have gained popularity due to their high fuel economy, low pollution, and excellent compatibility with the current fossil fuel dispensing and electric charging infrastructures. To facilitate autonomous driving, an autonomous HEV controller is needed for determining the power split between the powertrain components (including an ICE and an electric motor) while simultaneously managing the power consumption of auxiliary systems (e.g, air-conditioning and lighting systems) such that the overall electromobility is enhanced. Certain (partial) prior knowledge of the future driving profile is useful information for the automatic HEV control. In this paper, methods for predicting driving profile characteristics to enhance HEV power control are first presented. Based on the prediction results and the observed HEV system state (e.g. velocity, battery state-of-charge, propulsion power demand), we propose a reinforcement learning method to determine the power source split between the ICE and electric motor while also controlling the power consumptions of the air-conditioning and lighting systems in the automobile. Experimental results demonstrate significant improvement in the overall HEV system efficiency.
AbstractList Autonomous driving has become a major goal of automobile manufacturers and an important driver for the vehicular technology. Hybrid electric vehicles (HEVs), which represent a trade-off between conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), have gained popularity due to their high fuel economy, low pollution, and excellent compatibility with the current fossil fuel dispensing and electric charging infrastructures. To facilitate autonomous driving, an autonomous HEV controller is needed for determining the power split between the powertrain components (including an ICE and an electric motor) while simultaneously managing the power consumption of auxiliary systems (e.g, air-conditioning and lighting systems) such that the overall electromobility is enhanced. Certain (partial) prior knowledge of the future driving profile is useful information for the automatic HEV control. In this paper, methods for predicting driving profile characteristics to enhance HEV power control are first presented. Based on the prediction results and the observed HEV system state (e.g. velocity, battery state-of-charge, propulsion power demand), we propose a reinforcement learning method to determine the power source split between the ICE and electric motor while also controlling the power consumptions of the air-conditioning and lighting systems in the automobile. Experimental results demonstrate significant improvement in the overall HEV system efficiency.
Author Yanzhi Wang
Xue Lin
Pedram, Massoud
Naehyuck Chang
Author_xml – sequence: 1
  surname: Yanzhi Wang
  fullname: Yanzhi Wang
  email: yanzhiwa@usc.edu
  organization: Univ. of Southern California, Los Angeles, SC, USA
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  surname: Xue Lin
  fullname: Xue Lin
  email: xuelin@usc.edu
  organization: Univ. of Southern California, Los Angeles, SC, USA
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  givenname: Massoud
  surname: Pedram
  fullname: Pedram, Massoud
  email: pedram@usc.edu
  organization: Univ. of Southern California, Los Angeles, SC, USA
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  surname: Naehyuck Chang
  fullname: Naehyuck Chang
  email: naehyuck@kaist.ac.kr
  organization: KAIST, Daejeon, South Korea
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Snippet Autonomous driving has become a major goal of automobile manufacturers and an important driver for the vehicular technology. Hybrid electric vehicles (HEVs),...
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SubjectTerms Batteries
Fuels
Hybrid electric vehicles
Ice
Power demand
Propulsion
Title Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles
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