Artificial neural network based adaptive control for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.

• Record URL:
  • https://doi.org/10.1504/IJEHV.2019.099985
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17514088
• Supplemental Notes:
  • Copyright © 2018 Inderscience Enterprises Ltd.
• Authors:
  • Divakarla, Kavya P
  • Wirasingha, Sanjaka G
  • Emadi, Ali
  • Razavi, Saiedeh
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 127-151
• Serial:
  • International Journal of Electric and Hybrid Vehicles
  • Volume: 11
  • Issue Number: 2
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1751-4088
  • EISSN: 1751-4096
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=ijehv

Subject/Index Terms

• TRT Terms: Adaptive control; Dynamic programming; Electric vehicles; Fuel consumption; Neural networks; Optimization; Plug-in hybrid vehicles
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01713899
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 16 2019 3:51PM