Battery Capacity Estimation Analysis Based On Time-Frequency Distribution

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

Lithium-ion Battery Parameter Analysis Using Spectrogram

Nowadays, energy storage improves the reliability and efficiency of electric utility system. The most common device used for storing electrical energy is battery. Obtaining an accurate data of battery parameter is important because it will be avoid unexpected system interruption and prevent permanent damage to the internal structure of the batteries. The objective of this study is to apply time-frequency distribution (TFD) which is spectrogram technique in analysis of voltage charging and discharging signal for lithium-ion battery. Spectrogram represents the battery signal in time frequency representation (TFR) which is appropriate to analyze the signal before displaying the instantaneous RMS voltage (Vrms), direct current voltage (VDC) and alternating current voltage (VAC) parameter value. This paper focuses on lithium-ion (Li-ion) battery with nominal voltage of 6 and 12V and various storage capacities from 5 to 50Ah. The battery model is implemented in MATLAB/SIMULINK. From the results, the Li-ion battery parameter could be identified using spectrogram

Battery Parameters Identification Analysis using Periodogram

Batteries are essential components of most electrical devices and one of the most important parameters in batteries is storage capacity. It represents the maximum amount of energy that can be extracted from the battery under certain specified condition. This paper presents the analysis of charging and discharging battery signal using periodogram. The periodogram converts waveform data from the time domain into the frequency domain and represents the distribution of the signal power over frequency. This analysis focuses on four types of batteries which are leadacid (LA), lithium-ion (Li-ion), nickel-cadmium (Ni-Cd) and nickel-metal-hydride (Ni-MH). This paper used battery model from MATLAB/SIMULINK software and the nominal voltage of each battery is 6 and 12V while the capacity is 10 and 20Ah, respectively. The analysis is done and the result shows that varying capacity produce different power at a frequency and voltage at DC component

Lead Acid Battery Analysis using Spectrogram

Battery is an alternative option that can be substituted for future energy demand. Numerous type of battery is used in industries to propel portable power and its makes the task of selecting the right battery type is crucial. These papers discuss the implementation of linear timefrequency distribution (TFD) in analysing lead acid battery signals. The time-frequency analysis technique selected is spectrogram. Based on, the time-frequency representations (TFR) obtain, the signal parameter such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated. The parameter is essential in identifying signal characteristics. This analysis is focussing on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah, respectively. The results show that spectrogram technique is capable to estimate and identify the signal characteristics of Lead Acid battery

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