Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications

Ahmed Gaga; Hicham Benssassi; Fatima Errahimi; Najia Es-Sbai

doi:10.15866/ireaco.v10i4.11393

Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications

Ahmed Gaga^(1*), Hicham Benssassi⁽²⁾, Fatima Errahimi⁽³⁾, Najia Es-Sbai⁽⁴⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/ireaco.v10i4.11393

Abstract

Battery management system (BMS) is an electronic device responsible for all control and management operations of several battery parameters, especially SOH and SOC. The battery SOC acts like an indicator of the internal charge level of the battery, in order to avoid unpredicted system interruption and prevent the batteries from being over-charged or over-discharged. SOC estimation procedure is one of the most complex techniques caused by complex battery chemistry and its strong non linearity. In this paper we have chosen a Kalman filtering algorithm to estimate internal states of Lithium Ion battery and dynamically estimate the SOC by decreasing divergence due to parametric uncertainty of battery model, measurement and process noise by using an Unscented variant of this filter. To further enhance the UKF algorithm, an adaptive calculation of noise covariance is proposed to combine between a better convergence and robust results. Experimental results indicate that the adaptive unscented Kalman filter based algorithm has better performance Battery State of Charge estimation. A comparison with other estimations techniques shows that the proposed SOC estimation method is the best choice in term of accuracy and robustness.
Copyright © 2017 Praise Worthy Prize - All rights reserved.

Keywords

Unscented KALMAN Filter; Adaptive UKF; Battery Storage And Control; Thevenin RC Network; SOC; State Estimation; Plant Control; Modeling

Full Text:

PDF

References

D. Linden et T. Reddy, Handbook of Batteries, New York: McGraw-Hill, 2002.
http://dx.doi.org/10.2307/1957703

Piller S;Perrin M;Jossen A;, «Methods for state-of-charge determination and their applications,» Journal of Power Sources, vol. 96, pp. 113-120, 2001.
http://dx.doi.org/10.1016/s0378-7753(01)00560-2

J. Aylor et B. Johnson , «A battery state-of-charge indicator for electric wheelchairs,» IEEE Transactions on Industrial Electronics, vol. 39, pp. 398-409, 1992.

T. Liu, D. Chen et C. Fang, «Design and implementation of a battery charger with a state-of-charge estimator,» International Journal of Electronics, vol. 87, pp. 211-226, 2000.

S. Avril, G. Arnaud et A. Florentin, «Multi-objective optimization of batteries and hydrogen storage technologies for remote photovoltaic systems,» Energy, vol. 35, pp. 5300-5308, 2010.
http://dx.doi.org/10.1016/j.energy.2010.07.033

C. Chan et W. Shen, «The available capacity computation model based on artificial neural network for lead-acid batteries in electrical vehicles,» Journal of Power Sources, vol. 87, pp. 201-204, 2000.

W. Shen, C. Chan, E. Lo et K. Chau, «A new battery available capacity indicator for electric vehicles using neural network,» Energy Conversion and Management, vol. 43, pp. 817-826, 2002.

W. Shen, «State of available capacity estimation for lead-acid batteries in electric vehicles using neural network,» Energy Conversion and Management, vol. 48, pp. 433-442, 2007.
http://dx.doi.org/10.1016/j.enconman.2006.06.023

A. Slkind, C. Fennie, P. Singh et T. Atwater, «Determination of state-ofcharge and state-of-health of batteries by fuzzy logic methodology.,» Journal of Power Sources, vol. 80, pp. 293-300, 1999.

K. Chau, K. Wu et C. Chan, «A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system,» Energy Conversion and Management, vol. 45, pp. 1681-1692, 2004.
http://dx.doi.org/10.1016/j.enconman.2003.09.031

P. Singh , R. Vinjamu, X. Wang et D. Reisne, «Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators,» Journal of Power Sources, vol. 162, pp. 829-836, 2006.
http://dx.doi.org/10.1016/j.jpowsour.2005.04.039

Chiodo, E., Del Pizzo, A., Di Noia, L., Lauria, D., Modeling and Bayes Estimation of Battery Lifetime for Smart Grids Under an Inverse Gaussian Model, (2013) International Review of Electrical Engineering (IREE), 8 (4), pp. 1253-1266.

B. Bhangu, P. Bentley, D. Stone et C. Bingham, «Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybridelectric vehicles,» IEEE Transactions on Vehicular Technology, vol. 54, pp. 783-794, 2005.
http://dx.doi.org/10.1109/tvt.2004.842461

A. Vasebi, S. Bathaee et M. Partovibakhsh, «Predicting state of charge of lead acid batteries for hybrid electric vehicles by extended Kalman filter,» Energy Conversion and Management, vol. 49, pp. 75-82, 2008.
http://dx.doi.org/10.1016/j.enconman.2007.05.017

S. Julier et J. Uhlmann, «New extension of the Kalman filter to nonlinear systems,» International Symposium on Aerospace/Defense Sensing,Simulation, and Controls, pp. 182-193, 1997.
http://dx.doi.org/10.1117/12.280797

E. Wan et R. Van Der Merwe, «The unscented Kalman filter for nonlinear estimation,» IEEE Symposium on Adaptive Systems for Signal Processing,Communications and Control, p. 153-158, 2000.
http://dx.doi.org/10.1109/asspcc.2000.882463

R. Van Der Merwe, «Sigma-point Kalman filters for probabilistic inference in dynamic state-space models,» Dissertation for the Degree of Doctor of Philosophy. Oregon Health & Science University, Portland, USA, 2004.

G. Plett, «Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs-Part 1: introduction and state estimation, Part 2: simultaneous state and parameter estimation,» Journal of Power Sources, vol. 161, pp. 1356-1384, 2006.
http://dx.doi.org/10.1016/j.jpowsour.2006.06.004

S. Santhanagopalan et R. White, «State of charge estimation using an unscented filter for high power lithium ion cells,» International Journal of Energy Research, vol. 34, pp. 152-163, 2010.
http://dx.doi.org/10.1002/er.1655

J. Han, D. Kim et M. Sunwoo, «State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter,» Journal of Power Sources, vol. 188, pp. 606-612, 2009.
http://dx.doi.org/10.1016/j.jpowsour.2008.11.143

«An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack,» Energy Conversion and Management, vol. 50, pp. 3182-3186, 2009.
http://dx.doi.org/10.1016/j.enconman.2009.08.015

Boutte, A., Midoun, A., Identification of Lead-Acid Battery Parameters by Kalman Filter Using Various Battery Models, (2014) International Review of Automatic Control (IREACO), 7 (1), pp. 90-97.

P. Henri, «The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems,» Department of Civil and Environmental Engineering, Duke University, Caroline du Nord, 2016.

Bakhti, M., Idrissi, B., Highly Nonlinear Flexible Manipulator State Estimation Using the Extended and the Unscented Kalman Filters, (2016) International Review of Automatic Control (IREACO), 9 (3), pp. 151-160.
http://dx.doi.org/10.15866/ireaco.v9i3.9018

F. Sun, X. Hu, Y. Zou et S. Li, «Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles,» Energy, vol. 36, pp. 3531-3540, 2011.
http://dx.doi.org/10.1016/j.energy.2011.03.059

Refbacks

There are currently no refbacks.

Username
Password
Remember me