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

Macam S Dattathreya,Thomas Meitzler,Harpreet Singh

doi:10.1155/2012/687652

Macam S Dattathreya, Thomas Meitzler + Show 1 more

Open Access

https://doi.org/10.1155/2012/687652

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Abstract

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.

Highlights

A hybrid electric vehicle (HEV) propulsion system uses a high-voltage battery and an engine
In contrast with the existing singleton model in the literature, the proposed fuzzy deterministic noncontroller type (FDNCT) system uses no output membership function, instead it calculates the deterministic output value ki based on the implication of the rules in a novel way using FDNCT inference algorithm (FIA)
The authors simulated the performance of the FIA and singleton approaches using a computer software, namely, Matlab and Simulink, and a set of normalized input data for the engine and battery compartments

Summary

Introduction

A hybrid electric vehicle (HEV) propulsion system uses a high-voltage battery and an engine. In contrast with the existing singleton model in the literature, the proposed FDNCT system uses no output membership function, instead it calculates the deterministic output value ki based on the implication of the rules in a novel way using FDNCT inference algorithm (FIA). Let I1 be the value of the first input variable of the FDNCT system and μp and ap be the corresponding membership grade and output coefficient of the pth linguistic input membership function (fuzzy set), respectively, where p = 1, 2, . Let Il be the value of lth input variable of the FDNCT system where l = 2 to X inputs, μpl and apl be the corresponding membership grade and output coefficient of the pth linguistic input membership function (fuzzy set) of the lth input, respectively, where p = 1, 2, .

FIA Example

Simulation Results

Conclusion