A Comparative Study On Time-Frequency Distribution Techniques For Battery Parameters Estimation System

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

A Comparative Study On Spectrogram And S-Transform For Batteries Parameters Estimation

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 (V RMS (t)), instantaneous direct current voltage (V DC (t)), and instantaneous alternating current voltage (V AC (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of V AC (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%

Lead Acid Battery Analysis using S-Transform

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

Compilation and Code Optimization for Data Analytics

The trade-offs between the use of modern high-level and low-level programming languages in constructing complex software artifacts are well known. High-level languages allow for greater programmer productivity: abstraction and genericity allow for the same functionality to be implemented with significantly less code compared to low-level languages. Modularity, object-orientation, functional programming, and powerful type systems allow programmers not only to create clean abstractions and protect them from leaking, but also to define code units that are reusable and easily composable, and software architectures that are adaptable and extensible. The abstraction, succinctness, and modularity of high-level code help to avoid software bugs and facilitate debugging and maintenance. The use of high-level languages comes at a performance cost: increased indirection due to abstraction, virtualization, and interpretation, and superfluous work, particularly in the form of tempory memory allocation and deallocation to support objects and encapsulation. As a result of this, the cost of high-level languages for performance-critical systems may seem prohibitive. The vision of abstraction without regret argues that it is possible to use high-level languages for building performance-critical systems that allow for both productivity and high performance, instead of trading off the former for the latter. In this thesis, we realize this vision for building different types of data analytics systems. Our means of achieving this is by employing compilation. The goal is to compile away expensive language features -- to compile high-level code down to efficient low-level code

Lead Acid Battery Analysis Using S-Transform

This paper proposes a new signal processing technique using time-frequency distribution (TFD),namely S-ransform (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

Automated Real-Time Vision Quality Inspection Monitoring System

The requirement of product quality inspection in industries for product standardized leads to a development of the quality inspection system.The problem is related to a manual inspection that is done by a human as an inspector.This paper presents an automated real-time vision quality inspection monitoring system as a problem solver to a manual inspection that is tedious and time-consuming task as well as reducing cost especially in small and medium enterprise industries (SME).For the proposed system,soft drink is used as the test product for quality inspection.The system uses computer-network to inspect two quality inspections which are color concentration and water level.The analysis includes pre-processing,color concentration using the histogram and quadratic distance and level inspection using coordinate vertical and horizontal reference levels.The similarities of both experimental and simulation results are obtained for both parameters which are 100% accuracy using 205 samples