Fuzzy‐based blended control for the energy management of a parallel plug‐in hybrid electric vehicle. Issue 1 (1st February 2015)

• Record Type: Journal Article
• Authors: Denis, Nicolas; Dubois, Maxime R.; Desrochers, Alain
• Is Part Of: IET intelligent transport systems. Volume 9:Issue 1(2015)
• Page Start: 30
• Page End: 37
• Publication Date: 2015-02-01
• Subjects: fuzzy control -- energy management systems -- hybrid electric vehicles -- intelligent control -- fuel economy -- optimal control -- control system synthesis; driving condition information -- fuzzy logic -- vehicle state -- total driving cycle -- optimal control -- fuel economy -- energy management systems -- power distribution -- intelligent controller -- embedded power sources -- automotive industry -- gas emission reduction -- fuel consumption reduction -- PHEV -- parallel plug‐in hybrid electric vehicle -- fuzzy‐based blended control; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2014.0075
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.