Open AccessFuzzy‐based blended control for the energy management of a parallel plug‐in hybrid electric vehicle
Author(s) -
Denis Nicolas,
Dubois Maxime R.,
Desrochers Alain
Publication year - 2015
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0075
Subject(s) - fuzzy logic , electric vehicle , automotive industry , controller (irrigation) , energy management , automotive engineering , driving cycle , hybrid vehicle , control engineering , engineering , fuel efficiency , fuzzy control system , computer science , power (physics) , control theory (sociology) , control (management) , energy (signal processing) , artificial intelligence , agronomy , statistics , physics , mathematics , quantum mechanics , biology , aerospace engineering
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.