Battery Model Parameters Estimation with the Sigma Point Kalman Filter

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

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Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

Mathematical Problems in Engineering ◽

10.1155/2019/4961045 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 6

Author(s):

Kai Zhang ◽

Jian Ma ◽

Xuan Zhao ◽

Xiaodong Liu ◽

Yixi Zhang

Keyword(s):

Parameters Estimation ◽

State Of Charge ◽

Superior Performance ◽

Model Parameters ◽

Local Optimum ◽

Soc Estimation ◽

Chaotic Mapping ◽

Battery Model ◽

Ant Lion Optimizer ◽

Ant Lion

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

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Battery Model Parameters Estimation Using Simulated Annealing

TEMA (São Carlos) ◽

10.5540/tema.2017.018.01.0127 ◽

2017 ◽

Vol 18 (1) ◽

pp. 127 ◽

Cited By ~ 4

Author(s):

Marcia De Fatima Brondani ◽

Airam Teresa Zago Romcy Sausen ◽

Paulo Sérgio Sausen ◽

Manuel Osório Binelo

Keyword(s):

Mathematical Modeling ◽

Experimental Data ◽

Simulated Annealing ◽

Visual Analysis ◽

Lithium Ion ◽

Parameters Estimation ◽

Model Parameters ◽

Algorithm Efficiency ◽

Battery Model ◽

Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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UAS based Li-ion battery model parameters estimation

Control Engineering Practice ◽

10.1016/j.conengprac.2017.06.012 ◽

2017 ◽

Vol 66 ◽

pp. 126-145 ◽

Cited By ~ 12

Author(s):

D. Ali ◽

S. Mukhopadhyay ◽

H. Rehman ◽

A. Khurram

Keyword(s):

Parameters Estimation ◽

Model Parameters ◽

Li Ion Battery ◽

Battery Model ◽

Li Ion

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Electrochemical Model and Sigma Point Kalman Filter based Online Oriented Battery Model

IEEE Access ◽

10.1109/access.2021.3095620 ◽

2021 ◽

pp. 1-1

Author(s):

E. Miguel ◽

Gregory L. Plett ◽

M. Scott Trimboli ◽

I. Lopetegi ◽

L. Oca ◽

...

Keyword(s):

Kalman Filter ◽

Sigma Point ◽

Battery Model ◽

Electrochemical Model

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Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

Hydrology Research ◽

10.2166/nh.2021.272 ◽

2021 ◽

Author(s):

Mengtian Lu ◽

Sicheng Lu ◽

Weihong Liao ◽

Xiaohui Lei ◽

Zhaokai Yin ◽

...

Keyword(s):

Parameter Estimation ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Assimilation Efficiency ◽

Estimation Method ◽

Initial Distribution ◽

Parameters Estimation ◽

Model Parameters ◽

Multiple Parameters ◽

Basin Surface

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

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A novel adaptive technique for Li-ion battery model parameters estimation

2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS) ◽

10.1109/naecon.2016.7856769 ◽

2016 ◽

Author(s):

Daniyal Ali ◽

Shayok Mukhopadhyay ◽

Habibur Rehman

Keyword(s):

Parameters Estimation ◽

Model Parameters ◽

Li Ion Battery ◽

Battery Model ◽

Adaptive Technique ◽

Li Ion

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Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.2211 ◽

2011 ◽

Vol 403-408 ◽

pp. 2211-2215 ◽

Cited By ~ 5

Author(s):

Ke Xin Wei ◽

Qiao Yan Chen

Keyword(s):

Kalman Filter ◽

State Of Charge ◽

Model Parameters ◽

Adaptive Kalman Filter ◽

Single Model ◽

Soc Estimation ◽

Battery Model ◽

Kalman Filter Algorithm ◽

Estimation Precision ◽

State Of Charge Estimation

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

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An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter

Mathematics ◽

10.3390/math9060636 ◽

2021 ◽

Vol 9 (6) ◽

pp. 636

Author(s):

Rabih Ghostine ◽

Mohamad Gharamti ◽

Sally Hassrouny ◽

Ibrahim Hoteit

Keyword(s):

Saudi Arabia ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Numerical Models ◽

Parameters Estimation ◽

Disease Spread ◽

Model Parameters ◽

Equilibrium Existence ◽

Seir Model ◽

Proposed Model

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model’s forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic.

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