DOI : https://doi.org/10.18517/ijaseit.7.5.2289

Lead Acid Battery Analysis using S-Transform

Muhammad Sufyan Safwan Mohamad Basir (1), Abdul Rahim Abdullah (2), Nur Asmiza Selamat (3), Haslinda Musa (4), Rahifa Ranom (5)
(1) Center for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malacca, 76100, Malaysia
(2) Center for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malacca, 76100, Malaysia
(3) Center for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malacca, 76100, Malaysia
(4) Centre of Technopreneurship Development (C-TeD), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
(5) Center for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malacca, 76100, Malaysia
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How to cite (IJASEIT) :
Mohamad Basir, Muhammad Sufyan Safwan, et al. “Lead Acid Battery Analysis Using S-Transform”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, Oct. 2017, pp. 1832-9, doi:10.18517/ijaseit.7.5.2289.
Citation Format :
This paper proposes a new signal processing technique using time-frequency distribution (TFD), namely S-transform (ST) for battery parameters estimation. Compared to other TFDs such as short time Fourier transform (STFT) and wavelet transform (WT), ST technique offers more promising results in a low frequency application analysis, especially battery. The results of the ST are the parameters of instantaneous means square voltage (VRMS (t)), instantaneous direct current voltage (VDC (t)) and instantaneous alternating current voltage (VAC (t)) extracted from the time-frequency representation (TFR). Simulation through MATLAB has been conducted using equivalent circuit model (ECM), using 12 V lead acid (LA) battery with capacities from 1.0 Ah to 10.0 Ah. For the battery model, charging/discharging signal has been used to estimate the battery parameters from the ST technique to determine battery characteristics. From the signal characteristics of battery capacity versus VAC (t) obtained, new equation is proposed based on the curve fitting tool. The advantage of this technique embraces a better capability in estimating battery parameters at low frequency component, resulting in better frequency and time resolutions compared to previous TFDs.

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