A comparative study on time-frequency distribution techniques for battery parameters estimation system - Mohamad Basir, Muhammad Sufyan Safwan (2017)
Due to the degradation in battery lifetime directly impacts by load performance, reliability and safety operation of the battery cannot be guaranteed. In turn, safety precautions can be taken by monitoring battery performance from charging/discharging signals behaviour. Analyse the battery charging/discharging signals become challenging as the signal characteristic appears at very low frequency. Therefore, fast and accurate analysis in estimating battery parameters for real-time monitoring system should be proposed and developed. This research presents analysis of the battery charging/discharging signals using a spectral analysis technique, namely periodogram and time-frequency distributions (TFDs) which are 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, constant charging/discharging signals are presented, jointly, in time-frequency representation (TFR). From the TFR, battery signal characteristics are determined from the estimated parameters of instantaneous of total voltage (VTOT (t)), instantaneous of average voltage (VAVG (t)) and instantaneous of ripple factor voltage (VRF (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of VRF (t) using curve fitting tool is presented. In developing a real time automated battery parameters estimation system, best TFD is chosen in terms of accuracy of battery parameters, computational complexity in signal processing and memory size. Advantages in high accuracy for battery parameters estimation and low in memory size requirement makes S-transform technique is selected to be the best TFD. The accuracy of the system is verified with parameters estimation using ECM for each type of battery at a different capacity. The field testing results show that average mean absolute percentage error (MAPE) is around four percent. Thus, implementation of S-transform technique for real-time automated battery parameters estimation system is very appropriate for battery signal analysis.