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access icon free Fuzzy-based blended control for the energy management of a parallel plug-in hybrid electric vehicle

  • Author(s): Nicolas Denis 1 ; Maxime R. Dubois 2 ; Alain Desrochers 3
    • View affiliations
    • Affiliations: 1: Smart Vehicle Research Center Laboratory, Toyota Technological Institute, 2-12-1, Hisakata, Tenpaku Ward, Nagoya, Japan;
      2: Electrical and Computer Engineering Department, Sherbrooke University, 2500 Boulevard Université, Sherbrooke, Canada;
      3: Mechanical Engineering Department, Sherbrooke University, 2500 Boulevard Université, Sherbrooke, Canada
  • Source: Volume 9, Issue 1, February 2015, p. 30 – 37
    DOI:  10.1049/iet-its.2014.0075 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 10/04/2014, Accepted 01/07/2014, Revised 16/06/2014, Published 10/12/2014

The growing interest in reducing fuel consumption and gas emissions provides an incentive for the automotive industry to innovate in the field of hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). The two embedded power sources in these vehicles require an intelligent controller in order to make the best decision on the power distribution. Actually these controllers, often called energy management systems, are very important and greatly influence the achievable fuel economy. Compared with an HEV, a PHEV allows battery discharge over a complete trip. As a consequence the optimal control of a PHEV implies a stronger dependence on the total driving cycle. Many authors have studied the possibility of fuzzy-based systems for both HEV and PHEV as they have proved to be robust, reliable and simple. However, classical fuzzy rule-based strategies demonstrate a lack of optimality because their design is focused on the actual vehicle state rather than the driving conditions. This study proposes a blended control strategy based on fuzzy logic for a PHEV. The proposed controller is fed with driving condition information in order to increase the controller effectiveness in every situation. The efficiency of the proposed controller is demonstrated through simulations.

Inspec keywords: hybrid electric vehicles; fuzzy control; control system synthesis; energy management systems; fuel economy; optimal control; intelligent control

Other keywords: intelligent controller; embedded power sources; driving condition information; parallel plug-in hybrid electric vehicle; PHEV; optimal control; fuel economy; energy management systems; power distribution; automotive industry; fuzzy-based blended control; fuel consumption reduction; gas emission reduction; vehicle state; total driving cycle; fuzzy logic

Subjects: Optimal control; Fuzzy control; Transportation system control; Control of electric power systems; Transportation; Power system management, operation and economics; Control system analysis and synthesis methods

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