Battery Capacity Estimation Analysis Based On Time-Frequency Distribution

• Main Author: Kasim, Rizanaliah
• Format: Thesis
• Language: English; English
• Published: UTeM 2017
• Subjects: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/23085/1/Battery%20Capacity%20Estimation%20Analysis%20Based%20On%20Time-Frequency%20Distribution%20-%20Rizanaliah%20Kasim%20-%2024%20Pages.pdf; http://eprints.utem.edu.my/id/eprint/23085/2/Battery%20Capacity%20Estimation%20Analysis%20Based%20On%20Time-Frequency%20Distribution.pdf; http://eprints.utem.edu.my/id/eprint/23085/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=107270

The rising crude oil prices and awareness of environmental issues led to increasing the development of energy storage system. Due to this reason, rechargeable batteries are beneficial options for energy storage. Improper handling of the battery during the high discharge rate and overcharging will cause the premature failure to the battery. Obtaining an accurate data of battery parameter is important because it will avoid unexpected system interruption and prevent permanent damage to the internal structure of the batteries. This research presents the charging and discharging battery signals analysis using periodogram and time-frequency distribution (TFD) which is spectrogram. The analysis focuses on four types of batteries which are lead acid, nickel-cadmium, nickel-metal hydride and lithium-ion. The nominal voltage for batteries are used 6V and 12V while the capacities is in the range of 5Ah to 50Ah, respectively. The raw data of batteries charging and discharging signals are collected via simulation using MATLAB 2013 for various voltages and battery capacities. Then, the signals are transformed into periodogram and spectrogram. Periodogram represents signal in frequency domain while spectrogram represents signal in time-frequency representation (TFR). The signal parameters that estimated from the spectrogram are instantaneous voltage root mean square (VRMS), instantaneous voltage direct current (VDC) and instantaneous voltage alternating current (VAC). The result shows the decreased voltage signal with an increased battery capacity. The highest voltage signal is at 5 Ah and the lower voltage signal at 50Ah. Besides, the battery capacities can be identified by using formula that have been defined by using the curve fitting tools from MATLAB. An equation is defined based on correlation between voltage alternating current (VAC) and battery capacities (Ah). An experimental test also conducted to capture the real data for battery signals. The outcome of this research shows the application of spectrogram clearly give the information of the performance characteristic of battery at various operating conditions.