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Siti Fauziah Toha

Researcher at International Islamic University Malaysia

Publications -  91
Citations -  754

Siti Fauziah Toha is an academic researcher from International Islamic University Malaysia. The author has contributed to research in topics: Electric vehicle & Particle swarm optimization. The author has an hindex of 15, co-authored 83 publications receiving 620 citations. Previous affiliations of Siti Fauziah Toha include University of Sheffield & Islamic University.

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Fuzzy-based Temperature and Humidity Control for HV AC of Electric Vehicle

TL;DR: In this paper, the authors present the development of temperature and humidity control strategy for HVAC based on Fuzzy Logic Control (FLC) and simulation test results have been shown a satisfactory to control the temperature.
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Simplified Heat Generation Model for Lithium ion battery used in Electric Vehicle

TL;DR: In this article, a simple heat generation model was constructed in order to predict the thermal behavior of a battery pack, which can be viewed as an acceptable approximation for the variation of the battery pack temperature at a continuous discharge current from data provided by the manufacturer and literature.
Proceedings ArticleDOI

PID-Based Control of a Single-Link Flexible Manipulator in Vertical Motion with Genetic Optimisation

TL;DR: An investigation into dynamic simulation and controller optimization based on genetic algorithms (GAs) for a single-link flexible manipulator system in vertical plane motion and comparative performance assessment of the control approaches are presented.
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Optimal design of electric power assisted steering system(EPAS) using GA-PID method

TL;DR: In this article, the implementation of GA-PID algorithm to realize the potential of energy reduction as compared to conventional PID method is discussed and the results have shown GA-pID controller is bale to minimize energy consumption.
Proceedings ArticleDOI

MLP and Elman recurrent neural network modelling for the TRMS

TL;DR: A scrutinized investigation on system identification using artificial neural network (ANNs) to emphasis the potential benefits of this architecture for real system identification and to confirm the viability and effectiveness of the proposed methodology.