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Pinagapani Arun Kumar

Bio: Pinagapani Arun Kumar is an academic researcher from PSG College of Technology. The author has contributed to research in topics: System identification & Proton exchange membrane fuel cell. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Book ChapterDOI
01 Jan 2018
TL;DR: A mathematical model of a real-time PEMFC is obtained and its quality is assessed using various validation techniques and validation procedures like recursive least square algorithm, ARX and ARMAX were employed to assess the model.
Abstract: A model is an input–output mapping that suitably explains the behavior of a system. Model helps to analyze the functionality of the system and to design suitable controllers. System identification builds model from experimental data obtained by exciting the process with an input and observing its response at regular interval (Wibowo et al. in System identification of an interacting series process for real-time model predictive control, American Control Conference, pp. 4384–4389, 2009). Fuel cells (FC) systems are a potentially good clean energy conversion technology, and they have wide range of power generation applications. Classification of fuel cells is based on the fuel and the electrolyte type used. The proton exchange membrane fuel cells (PEMFC) are portable devices with superior performance and longer life. They act as a good source for ground vehicle applications. They also possess high power density and fast start-up time. In this work, mathematical model of a real-time PEMFC is obtained and its quality is assessed using various validation techniques. The model is obtained using system identification tool in MATLAB, and validation procedures like recursive least square algorithm, ARX and ARMAX were employed to assess the model. Controllers such as PI and PID were employed in order to achieve the desired load current by controlling the hydrogen flow rate. The values of the gain constant, integral time and derivative time were obtained using Cohen-Coon method. PI and PID control schemes were implemented using SIMULINK in MATLAB environment, and the system response was observed.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: An improved version of seagull optimization algorithm for optimal parameter identification of the PEMFC stacks is presented and results show the algorithm’s superiority in terms of the solutions quality and the convergence speed.

138 citations

Journal ArticleDOI
TL;DR: An optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for increasing their efficiency in the next designs of the proton exchange membrane fuel cell.

132 citations

Journal ArticleDOI
TL;DR: A new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose and it is declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PemFC model.

31 citations

Journal ArticleDOI
TL;DR: This study presents a multi-objective optimization method to provide an optimal design parameters for the HT-PEMFC based micro-CHP during a 14,000 h lifetime by considering the effect of degradation to optimize the net electrical efficiency and the electrical power generation.

29 citations