Research of System Identification for Ni/MH Battery State of Charge Based on a Short Sequence and Multi-Sample Process

2012 ◽  
Vol 586 ◽  
pp. 322-327 ◽  
Author(s):  
Jian Xiong Long ◽  
Lin Ming Yu ◽  
Shi Chen
Keyword(s):  
Time Series ◽  
System Identification ◽  
Reference Value ◽  
Identification Problem ◽  
State Of Charge ◽  
Short Sequence ◽  
Data Set ◽  
Systematic Analysis ◽  
Battery State Of Charge ◽  
Limited Length

In the battery state of charge of a systematic analysis, the observed data has a property that is the direction of time t (referred to as vertical) for a limited length, and number of samples obtained by N (called horizontal) for the infinite data set , it is called as a short sequence and multi-sample time series. By studying the characteristic of this time series, a new system identification method has been proposed, and the system identifiability for this process has been demonstrated. Through practice simulations, a satisfactory application results have been obtained. This feature of the time series identification problem is the same in other areas have a certain reference value.

Revisiting the deformation transients before the 2011 Tohoku-Oki Megathrust Earthquake with GPS

2020 ◽  
Author(s):  
Anne Socquet ◽  
Lou Marill ◽  
David Marsan ◽  
Baptiste Rousset ◽  
Mathilde Radiguet ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Gravity Anomalies ◽  
Seismogenic Zone ◽  
Double Difference ◽  
Ocean Bottom ◽  
Slow Slip ◽  
Short Term ◽  
Data Set ◽  
Systematic Analysis

<p>The precursory activity leading up to the Tohoku-Oki earthquake of 2011 has been suggested to feature both long- and short-term episodes of decoupling and suggests a particularly complex slow slip history. The analysis of the F3 solution of the Japanese GPS network suggested that an accelerated slip occurred in the deeper part of the seismogenic zone during the 10 years preceding the earthquake (Heki & Mitsui, EPSL 2013; Mavrommatis et al., GRL 2014; Yokota & Koketsu, Nat. Com. 2015). During the two months preceding the earthquake, no anomaly in the GPS position time series has been revealed so far, although several anomalous geophysical signals have been reported (an extended foreshock crisis near the future hypocenter (Kato et al., Science 2012), a synchronized increase of intermediate-depth background seismicity (Bouchon et al., Nat Geosc. 2016), a signal in ocean-bottom pressure gauges and on-land strainmeter time series (Ito et al., Tectonoph. 2013), and large scale gravity anomalies that suggest deep-seated slab deformation processes (Panet et al., Nat. Geosc. 2018 ; Wang & Burgmann, GRL 2019)).</p><p>We present novel results based on an independent analysis of the Japanese GPS data set. We perform a full reprocessing of the raw data with a double-difference approach, a systematic analysis of the obtained time-series, including noise characterization and network filtering, and make a robust assessment of long- and short-term tectonic aseismic transients preceding the Tohoku-Oki earthquake. An accelerated slip on the lower part of the seismogenic zone over the last decade is confirmed, not only below the epicenter of Tohoku-Oki earthquake but also further south, offshore Boso peninsula, which is a worrying sign of an on-going slow decoupling east of Tokyo. At shorter time-scale, first results seem compatible with a slow slip close to the epicenter initiating ~ 2 months before the mainshock.</p>

Interpolated Mapping System Identification From Time Series Data

1993 ◽  
Author(s):  
Faruk H. Bursal ◽  
Benson H. Tongue
Keyword(s):  
Time Series ◽  
System Identification ◽  
Time Series Data ◽  
Initial Conditions ◽  
Series Data ◽  
Time Interval ◽  
Identification Algorithm ◽  
Regular Grid ◽  
Time Step ◽  
Data Set

Abstract In this paper, a system identification algorithm based on Interpolated Mapping (IM) that was introduced in a previous paper is generalized to the case of data stemming from arbitrary time series. The motivation for the new algorithm is the need to identify nonlinear dynamics in continuous time from discrete-time data. This approach has great generality and is applicable to problems arising in many areas of science and engineering. In the original formulation, a map defined on a regular grid in the state space of a dynamical system was assumed to be given. For the formulation to become practically viable, however, the requirement of initial conditions being taken from such a regular grid needs to be dropped. In particular, one would like to use time series data, where the time interval between samples is identified with the mapping time step T. This paper is concerned with the resulting complications. Various options for extending the formulation are examined, and a choice is made in favor of a pre-processing algorithm for estimating the FS map based on local fits to the data set. The suggested algorithm also has smoothing properties that are desirable from the standpoint of noise reduction.

Battery Charge And Source Estimation As A Product Component

2020 ◽  
Author(s):  
Gabriel De Almeida Souza ◽  
José Jean-Paul Zanlucchi de Souza Tavares
Keyword(s):  
Time Series ◽  
Low Cost ◽  
Computational Cost ◽  
External Source ◽  
State Of Charge ◽  
Hardware Complexity ◽  
Handheld Device ◽  
Data Consumption ◽  
Battery State Of Charge ◽  
Conventional Solution

This paper's goal is to present a low cost, non-conventional solution for battery state of charge estimation and external electrical input presence/absence for a commercial mobile, handheld device whose battery state of charge control is critical. This solution is based on treating and filtering a time series in real-time software, using the battery pack characteristic discharge curve and time series statistical features. The time series is composed of data that is sampled embedded in hardware, communicating directly with the machine's BIOS. The system processes this data and outputs a value that indirectly relates to state of charge, needing further processing to insure accuracy. The data stream is treated in a process that directly relates the output time series with state of charge through a transfer function, effectively treating intermediary conversions as black boxes to simplify analysis and implementation. This process can also detect if an external source is connected/disconnected by exploiting pre-detected features in the time series. This approachadvantages are its low cost and simplicity, reducing hardware complexity and expenses; small dimensional footprint; mostly software-based; and centralization into the main hardware as low computational cost daemons, simplifying data consumption.

Fractional system identification for lead acid battery state of charge estimation

2006 ◽  
Vol 86 (10) ◽  
pp. 2645-2657 ◽  
Author(s):  
Jocelyn Sabatier ◽  
Mohamed Aoun ◽  
Alain Oustaloup ◽  
Gilles Grégoire ◽  
Franck Ragot ◽  
...  
Keyword(s):  
System Identification ◽  
State Of Charge ◽  
Lead Acid Battery ◽  
Battery State Of Charge ◽  
Fractional System ◽  
State Of Charge Estimation ◽  
Lead Acid

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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