Experimental validation for Li-ion battery modeling using Extended Kalman Filters

Claude, F.; Becherif, Mohamed; Ramadan, Haïtham Saad Mohamed

doi:10.1016/j.ijhydene.2017.01.123

Cited by 37 publications

(12 citation statements)

References 38 publications

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“…The linear barrier of the KF method is surpassed with the Extended KF (EKF) [18], composed of first-order Taylor-series expansions that are used to linearize the non-linear time-invariant Li-ion model. For this purpose dual (DKF) or joint (JKF) Kalman filters may be used by either working in parallel and providing each other information, or by enlarging the model matrices in order to establish higher reliability with the penalty of proportionally increased computational time [118][119][120][121][122].…”

Section: Parameter Extraction With Kalman Filters Based Techniquesmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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Section: Parameter Extraction With Kalman Filters Based Techniquesmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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“…The KF methods have a closed-loop correction structure, which can solve the problem of initial SOC value error, and it does not need the large data set to train the battery model, so it has been widely used. For the nonlinear system, the extended KF(EKF) and unscented KF(UKF) are applied in reference [18][19][20][21][22][23][24][25], and the estimation results show the effectiveness of the algorithms. However, EKF uses the Taylor formula to expand the equation but ignores the higher-order term, which will lead to the inaccuracy of SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

Min

Jiao

et al. 2020

IEEE Access

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Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter(AUKF) and least-squares support vector machine(LSSVM) is proposed. This paper improves the accuracy of SOC estimation from four aspects. Firstly, the nonlinear relationship between SOC, current, and voltage is established by LSSVM. Secondly, a novel voltage estimation method based on improved LSSVM is proposed. Thirdly, the measurement equation of the novel AUKF is created by the improved LSSVM. Finally, the effectiveness of the proposed model is verified under different driving conditions. The comparison results show that the model can improve the accuracy of voltage and SOC estimation, and the SOC estimation error is controlled within 2%. INDEX TERMS state-of-charge(SOC); adaptive unscented Kalman filter(AUKF); terminal voltage; least squares support vector machine(LSSVM)

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“…EKF ensures accurate estimation of the SoC of the battery using the measured load current and terminal voltage. The accuracy of EKF based SoC estimation depends on the precision of the battery model and information of the system noise and covariance matrix [26][27][28]. However, the EKF has drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

Muratoglu

Alkaya

2019

Eng. Technol. Appl. Sci. Res.

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Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

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Experimental validation for Li-ion battery modeling using Extended Kalman Filters

Cited by 37 publications

References 38 publications

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

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