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DOI: 10.4018/IJDA.2021070101
Volume 2 • Issue 2 • July-December 2021
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
*Corresponding Author
1
Liming Xie, North Dakota State University, USA
The experimental data of Lithium-ion battery has its specific sense. This paper is proposed to analyze
and forecast it by using autoregressive integrated moving average (ARIMA) and spectral analysis,
which has effective and statistical results. The method includes the identification of the data, estimation
and diagnostic checking, and forecasting the future values by Box and Jenkins. The analysis shows
that the time series models are related with the present value of a series to past values and past
prediction errors. After transferring the data by different function, improving autocorrelations are
significant. Forecasting the future values of the possible observations show significantly fluctuated
such as increasing or decreasing in specific ranges accordingly. In spectral analysis, the parameters
of the model were determined by performing spectral analysis of the experimental data to look
periodicities or cyclical patterns, and to check the existence of white noise in the data. The Bartlett’s
Kolmogorov-Smirnov statistic suggests the white noise of the data. The spectral analysis for the
series reveals non-11-second cycle of activity for dynamic stress test current, but strong 45-second
that highlights the position of the main peak in the spectral density; strong 21-second and 45-second
for the urbane dynamometer driver schedule current and voltage, respectively; but no significance
for dynamic stress test current.
Autocorrelation Function (ACF), Autoregressive Integrated Moving Average (ARIMA), Dynamic Stress Test
(DST), Lithium-ion Batteries (LIBs), Partial Autocorrelation Function (PACF), Urbane Dynamometer Driver
Schedule (UDDS)
A lithium-ion battery (LIB) is a type of rechargeable battery. It is main work to make lithium ion
from the negative electrode into the positive one, while discharge and back. LIBs use an intercalated
lithium compound as one electrode materials. There are three components of a lithium-ion battery:
the positive and negative electrodes and electrolyte. The carbon is made of the negative electrode.
A metal oxide is made of the positive one. In an organic solvent the electrolyte is a lithium salt. The
electrochemical work plays a key role on a lithium-ion battery, such as reversing between anode and
cathode based on the direction of current flow through the cell as a graph as the following Figure 1:
LIBs are applied expensively in industry for electronics and it is used in hybrid electronic vehicles
(HEVs), pure EVs, plug-in HEVs, smart grids as energy-storage devices, etc. It is a mainly enabling
technology in advanced transportation. In the future, Lithium-ion batteries will bring industry
revolution into new time. The data for this paper comes from experimental data of lithium-ion
Volume 2 • Issue 2 • July-December 2021
2
battery and ultracapacitor of the dynamic stress (DST) condition and the urban dynamometer driving
schedule (UDDS) condition at room temperature from 16:44 through 15:58, August 20, 2016. The
experiment recorded currents and voltages of the battery and ultracapacitor. 8081 observations for
lithium-ion battery and ultracapacitor hybrids were tested. It reflected measure voltage, currents that
loaded negative current as discharging and positive upon charging to analyze the dynamic changes of
the LIBs and ultracapacitors so that got further possible prediction of state-of-energy. The propose
is to analyze the performance and dynamic process of the LIBs and ultracapacitors and test DC
power supply and electrical load functions for charging and discharging. The type of the LIB was
IFP-1665130-10Ah (Wang et al., 2017).
To study the lithium-ion batteries deeply, many researchers and scholars conducted a lot of tries
or tests by means of statistics such as time series, lasso or other mathematics techniques. For example,
Eddahech et al analyzed lithium battery degrade phenomenon and thought that constant voltage charge
phase data help to determine battery state of health (Wang et al., 2017). Dr. Bor and his research
group developed and used mode and tools for time series data analysis on using a significant pool of
field trip data collected from 15 Hyundai Santa De electric sport utility vehicles dispatched to various
organizations on Oahu, Hawaii, during September 2001 and March 2002 (Eddahech et al., 2014). He
thought that a fuzzy-logic-based technique was a good algorithm for recognizing trip data. Dr. Peng
and his researchers used time series classification method to analyze the maintenance of batteries.
They built an accurate classification framework to collect the expert labels and made random forecast
(Liaw et al., 2002). In another article of this article’s author ARIMA mode and SARIMA were used
to script for 160 observations of data, the results indicated that the trends of cancer incidence rates
for 2015 to 2020 is downward by 200-550 per 100,000 population in the United States each year
(Xie, 2018). He et al got clustering algorithm such as L-means for lithium battery, self-organizing
neural network, pattern recognition and signal processing, etc. they obtained better effect (He et al.,
2018). Dr. Li et al made identification of the State of Health parameter of the life span for dynamic-
drive application system and selecting wavelet-based segmentation for time series data. Therefore,
time series of synchronized battery current and voltage data showed validated (He et al., 2018). Wu
et al estimated state of health of Li-ion battery, such as inputs of voltage curve properties used by
the group method of data handling polynomial neural network. They obtained the method is better
effect for the estimation of state of health (Li et al., 2015). Bi et al used to calibrate the regression
Figure 1. Lithium-ion battery rechargeable battery
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