Abstract
This research presents the analysis of battery charging and discharging signals using spectrogram, and S-transform techniques. The analysed batteries are lead acid (LA), nickel-metal hydride (Ni-MH), and lithium-ion (Li-ion). From the equivalent circuit model (ECM) simulated using MATLAB, the constant charging and discharging signals are presented, jointly, in time-frequency representation (TFR). From the TFR, the battery signal characteristics are determined from the estimated parameters of instantaneous means square voltage (VRMS (t)), instantaneous direct current voltage (VDC (t)), and instantaneous alternating current voltage (VAC (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of VAC (t) using curve fitting tool is presented. In developing a real-time automated battery parameters estimation system, the best time-frequency distribution (TFD) is chosen in terms of accuracy of the battery parameters, computational complexity in signal processing, and memory size. The advantages in high accuracy for battery parameters estimation, and low in memory size requirement makes the S-transform technique is selected to be the best TFD. Then, field testing is conducted for different cases, and the results show that the average mean absolute percentage error (MAPE) calculated is around 4%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
