scholarly journals State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H∞ Filtering

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1655
Author(s):  
Shuaishuai Zhang ◽  
Youhong Wan ◽  
Jie Ding ◽  
Yangyang Da
Keyword(s):  
Lithium Battery ◽  
Historical Data ◽  
Moving Average ◽  
Estimation Error ◽  
Gaussian White Noise ◽  
Gaussian Function ◽  
State Of Charge ◽  
Soc Estimation ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

When the classical H∞ algorithm (HIF) is applied to estimate the state of charge (SOC) of a lithium battery, the influence of historical data is usually ignored, resulting in an increase in the estimation error. In order to improve the accuracy of SOC estimation, this paper proposes an extended exponential weighted moving average H∞ algorithm (EE-HIF) in view of the influence of historical data. By designing the Gaussian function, the weighted distribution of the data at different times can effectively reduce the estimation error caused by the inaccuracy of the lithium battery model. In addition, when the system contains Gaussian white noise and alternating current input, the proposed method can achieve a faster convergence speed and better robustness. Simulation results show the advantages of the proposed algorithm, as compared to an HIF filtering algorithm and an exponentially weighted moving average H∞ algorithm (EWMA).

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

Online State of Charge Estimation for Battery of Electric Vehicle Using Sigma-Points Kalman Filters

2013 ◽  
Vol 427-429 ◽  
pp. 824-829
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Electric Vehicle ◽  
Kalman Filters ◽  
Hybrid Electric Vehicle ◽  
Lithium Battery ◽  
Cell Model ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Cell ◽  
Soc Estimation ◽  
Simulation And Experiment

An accurate state-of-charge (SOC) estimation of the hybrid electric vehicle (HEV) and electric vehicle (EV) battery pack is a difficult task to be performed online in a vehicle because of the noisy and low accurate measurements and the wide operating conditions in which the vehicle battery can operate. A Sigma-points Kalman Filters (SPKF) algorithm based on an improved Lithium battery cell model to estimate the SOC of a Lithium battery cell is proposed in this paper. The simulation and experiment results show the effectiveness and ease of implementation of the proposed technique.

scholarly journals Timely detection of Pertussis outbreaks in Iran: The comparison performance of Wavelet-based outbreak detector, Exponential weighted moving average, and Poisson regression-based methods

2020 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Seyed Mohsen Zahraei ◽  
Manoochehr Karami ◽  
Mehdi Yaseri ◽  
Mojtaba Lotfizad ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Moving Average ◽  
False Negative ◽  
False Negative Rate ◽  
Outbreak Detection ◽  
Haar Wavelets ◽  
Surveillance Systems ◽  
Likelihood Ratios ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract Background Early detection of outbreaks is very important for surveillance systems. Due to the importance of the subject and lack of similar studies in Iran, the aim of this study was to determine the performance of the Wavelet-Based Outbreak detection method)WOD(in detecting outbreaks and to compare its performance with Poisson regression-based model and Exponential weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Methods The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 and Haar wavelets), Poisson regression-based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks (literature-based and researcher-made outbreaks). The sensitivity, specificity, false alarm and false negative rate, positive and negative likelihood ratios, under ROC areas and median timeliness were used to assess the performance of the methods. Results In a literature-based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in Daubechies 10 (db10), with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher-made outbreaks, the EWMA (K=0.5) with sensitivity 0.92 (0.90-0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusions Performance of the WOD in the early alarming outbreaks was appropriate. However, it's better as the method was used along with other methods in public health surveillance systems.

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

scholarly journals Research on Volatility Pattern of BSE BANKEX Index & BSE SENSEX Index using Exponential Weighted moving Average Model

2020 ◽  
Vol 9 (4) ◽  
pp. 3195-3199
Keyword(s):  
Financial Services ◽  
Stock Exchange ◽  
Moving Average ◽  
Equity Market ◽  
Long Duration ◽  
Moving Average Model ◽  
Growth Prospects ◽  
High Level ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

India, a country with impressive growth prospects has stunned many developed nations. As far as performance of equity market concern, last 25 years among more than $1-trillion markets in the world, Indian equity market was best performer outpacing some of bigwigs such as US, Germany and Hong Kong. Last 25 years return in local money of SENSEX was so high in comparisons to others. Banking sectors have specific and an important role in the economic development of a India. With the reconstitution of BSE Sensex in last few years, the weightage of the Banking, Financial Services and Insurance (BFSI) sector. In the BSE 30 will touch its all-time high level to 40.1% which will be more than the combined weights of technology as consumer and auto. The weightage of financials in the Sensex has more than doubled from financial year 2009. In the long duration index weightage affect portfolio in major funds. The main objective of this research paper is to show the volatility patterns of Bombay Stock Exchange SENSEX and BSE BANKEX Index using Exponential weighted moving average (EWMA) model.

RISIKO PASAR: PERBANDINGAN MODEL EWMA DAN GARCH PADA NILAI TUKAR RUPIAH TERHADAP US DOLLAR

2010 ◽  
Vol 5 (2) ◽  
pp. 153
Author(s):  
Ari Christianti
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Risk Model ◽  
Moving Average ◽  
Var Model ◽  
Banking Supervision ◽  
Us Dollar ◽  
Exponential Weighted Moving Average ◽  
The Garch Model ◽  
Weighted Moving Average

Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision. Using daily exchange rate from January 2006-February 2008, will be compared measuring volatility between EWMA (Exponential Weighted Moving Average) and GARCH (Generalized Autoregressive Conditional Heterocedasticity). The results show that GARCH methods have considerably better power properties in measuring the volatility than the EWMA methods. However, the number of exceptions from the GARCH model, although much less than the EWMA model but the numbers were still above 5% and 1% (confidence level of 95% and 99%). The arguments for explained this finding is a pressure from stakeholders or the existence of an economic events that result in changes in exposure due to the different policies. As a result, the VaR model would be inaccurate to reality.Keywords: volatility, backtesting, EWMA, and GARCH

A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles

2021 ◽  
Author(s):  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Andrea Tonoli ◽  
Nicola Amati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Heavy Duty ◽  
Soc Estimation ◽  
Lead Acid Batteries ◽  
Heavy Duty Vehicles ◽  
Lead Acid

Abstract In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).

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