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Advances in Fuzzy Systems
Volume 2012, Article ID 687652, 11 pages
Research Article

Detection and Elimination of a Potential Fire in Engine and Battery Compartments of Hybrid Electric Vehicles

1Tank Automotive Research, Development and Engineering Center, Warren, MI 48397, USA
2Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA

Received 3 May 2012; Accepted 13 October 2012

Academic Editor: Ashu M. G. Solo

Copyright © 2012 Macam S. Dattathreya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel fuzzy deterministic noncontroller type (FDNCT) system and an FDNCT inference algorithm (FIA). The FDNCT uses fuzzy inputs and produces a deterministic non-fuzzy output. The FDNCT is an extension and alternative for the existing fuzzy singleton inference algorithm. The research described in this paper applies FDNCT to build an architecture for an intelligent system to detect and to eliminate potential fires in the engine and battery compartments of a hybrid electric vehicle. The fuzzy inputs consist of sensor data from the engine and battery compartments, namely, temperature, moisture, and voltage and current of the battery. The system synthesizes the data and detects potential fires, takes actions for eliminating the hazard, and notifies the passengers about the potential fire using an audible alarm. This paper also presents the computer simulation results of the comparison between the FIA and singleton inference algorithms for detecting potential fires and determining the actions for eliminating them.