A PWM Adaptive Sliding Mode Observer for Charge Control of Lithium Ion Battery

Main Article Content

Adeola Balogun
Chukwuemeka Sunday
Sunday Adetona
Sodiq Agoro
Frank Okafor

Abstract

An adaptive sliding mode control (SMC) based on PWM and an observer scheme for predicting the state of charge (SOC) of lithium-ion batteries is proposed. The control scheme is developed for a dc-dc buck converter used in regulated charge control of lithium-ion batteries. Unlike many estimation schemes where the converter’s output voltage is predetermined and the nonlinearities ignored, the proposed scheme estimates the buck converter’s output voltage, SOC, and nonlinearities in terms of errors in parameters. The stability of the proposed scheme is guaranteed by the Lyapunov method. The simulation was carried out in the Simulink in the MATLAB environment to transition from constant current charging mode to constant voltage charging mode. The results obtained from both modes in the Simulink in the MATLAB environment have shown that the dynamic control system of the SMC is asymptotically stable with excellent robust recovery features to sudden variations in input and un-modelled load.

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How to Cite

“A PWM Adaptive Sliding Mode Observer for Charge Control of Lithium Ion Battery” (2024) Journal of Engineering, 30(8), pp. 1–16. doi:10.31026/j.eng.2024.08.01.

References

Barcellona, S. and Piegari, L., 2017, Lithium ion battery models and parameter identification techniques. Energies, 10(12), p.2007. https://doi.org/10.3390/en10122007

Cardim R., Teixeira M. C. M., Assuno E., and Covacic M. R., 2009, Variable-structure control design of switched systems with an application to a dc–dc power converter, IEEE Trans. Ind. Electron., 56(9), pp. 3505–3513. https://doi.org/10.1109/TIE.2009.2026381

Cardoso B. J., Moreira A. F., Menezes B. R., and Cortizo P. C., 1992, Analysis of switching frequency reduction methods applied to sliding mode controlled dc–dc converters, Proc. IEEE Appl. Power Electron. Conf. Expo, pp. 403–410. https://doi.org/10.1109/APEC.1992.228382

Caumont, O., Le Moigne, P. P., and Lenain, C., 1998, An Optimized state of charge algorithm for lead acid batteries in electric vehicles, Proc. Electric Vehicle Symposium, Brussels, Belgium, Vol. EVS-15.

Chaoui H., Elmejdoubi A., and Gualous H., 2017, Online parameter identification of lithium-ion batteries with surface temperature cells, IEEE Transactions on Vehicular Technology, 66(3), pp. 2000–2009. https://doi.org/10.1109/TVT.2016.2583478

Chaoui H., Golbon N., Hmouz I., Souissi R., and Tahar S., 2015, Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries, IEEE Transactions on Industrial Electronics, 62(3), pp. 1610–1618. https://doi.org/10.1109/TIE.2014.2341576

Chen M., and Rincon-Mora G. A., 2006, Accurate electrical battery model capable of predicting run-time and I-V performance, IEEE Transaction on Energy Conversion, 21(2), pp. 504-511. https://doi.org/10.1109/TEC.2006.874229

Chen Z., Fu Y., and Mi C., 2013, State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering, IEEE Transactions on Vehicular Technology, 62(3), pp. 1020–1030. https://doi.org/10.1109/TVT.2012.2235474

Dai, H., Sun Z., and Wei, X., 2006, Online SOC estimation of high-power lithium-ion batteries used on HEV’s, Proc. IEEE Int. Conf. Vehicle Electron. Safety, pp. 342-347. https://doi.org/10.1109/ICVES.2006.371612

El Fadil H., Giri F., and Ouadi H., 2006, Adaptive sliding mode control of PWM boost dc–dc converters, Proc. IEEE Int. Conf. Control Appl., Munich, Germany, pp. 3151–3156. https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4777142

Gholizadeh M., and Salmasi F., 2014, Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model, IEEE Transactions on Industrial Electronics, 61(3), pp. 1335–1344. https://doi.org/10.1109/TIE.2013.2259779

Hansen T., and Wang, C.J., 2005, Support vector based battery state of charge estimator, Journal of Power Sources, 141(2), pp. 351–358. https://doi.org/10.1016/j.jpowsour.2004.09.020

He Y., and Luo F. L., 2006, Sliding-mode control for dc–dc converters with constant switching frequency, Inst. Elect. Eng. Proc. Control Theory Appl., 153(1), pp. 37–45. https://doi.org/10.1049/ip-cta:20050030

Kim, I., 2010, A Technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer, IEEE Transactions on Power Electronics, 25, (4), pp. 1013 - 1022. https://doi.org/10.1109/TPEL.2009.2034966

Mahdavi, J., Emadi A., and Toliyat H. A., 1997, Application of state-space averaging method to sliding mode control of PWM dc/dc converters, Proc. IEEE Ind. Appl. Soc. Annu. Meeting, 2, pp. 820–827. https://doi.org/10.1109/IAS.1997.628957

Mattavelli P., Rossetto L., and Spiazzi G., 1997, Small-signal analysis of dc–dc converters with sliding mode control, IEEE Trans. Power Electron, 12(1), pp. 96–102. https://doi.org /10.1109/63.554174

Navarro-Lopez E. M., Cortes D., and Castro C., 2009, Design of practical sliding-mode controllers with constant switching frequency for power converters, Elect. Powers Syst. Res., 79(5). pp. 796–802. https://doi.org/10.1016/j.epsr.2008.10.018

Obeid, H., Petrone, R., Chaoui, H., and Gualous, H. 2022, Higher order sliding-mode observers for state-of-charge and state-of-health estimation of lithium-ion batteries, IEEE Transactions on Vehicular Technology, 72(4), pp. 4482-4492. https://doi.org/10.1109/TVT.2022.3226686

Oucheriah S., and L. Guo L., 2013, PWM- based adaptive sliding-mode control for dc-dc boost power converter, IEEE Transactions on Industrial Electronics, 60(8), pp. 3291-3294. https://doi.org/10.1109/TIE.2012.2203769

Rahimi-Eichi H., Baronti F., and Chow M.Y., 2014, Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells, IEEE Transactions on Industrial Electronics, 61(4), pp. 2053–2061. https://doi.org/10.1109/TIE.2013.2263774

Shahriari, M., and Farrokhi, M., 2013, Online state-of-health estimation of VRLA batteries using state of charge, IEEE Transactions on Indus- trial Electronics, 60(1), pp. 191–202. https://doi.org/10.1109/TIE.2012.2186771

Sira-Ramirez H., Ortega R, and Garcia-Esteban M, 1998, Adaptive passivity based control of average dc-to-dc power converter models, Int. J. Adapt. Control Signal Process, 12(1), pp. 63–80.

Tan S. T., Lai Y. M., and Tse C. K., 2006 A unified approach to the design of PWM-based sliding-mode controllers for basic dc–dc converters in continuous conduction mode, IEEE Trans. Circuits Syst., 53(8), pp. 1816–1827. https://doi.org/10.1109/TCSI.2006.879052

Tan S. T., Lai Y. M., and Tse C. K., 2008, General design issues of sliding-mode controllers in dc–dc converters, IEEE Trans. Ind. Electron., 55(3), pp. 1160–1174. https://doi.org/10.1109/TIE.2007.909058

Tan S. T., Lai Y. M., Tse C. K., Martinez-Salamero L., and Wu C. K., 2007, A fast-response sliding-mode controller for boost-type converters with a wide range of operating conditions, IEEE Trans. Ind. Electron., 54(6), pp. 3276–3286. https://doi.org/10.1109/TIE.2007.905969

Vidal-Idiarte E., Carrejo C. E., Calvente J., and Martinez-Salamero L., 2011, Two-loop digital sliding mode control of dc–dc power converters based on predictive interpolation, IEEE Trans. Ind. Electron., 58(6), pp. 2491–2501. https://doi.org/10.1109/TIE.2010.2069071

Wai R.J., and Shih L.C., 2011, Design of voltage tracking control for dc–dc boost converter via total sliding-mode technique, IEEE Trans. Ind. Electron., 58(6), pp. 2502–2511. https://doi.org/10.1109/TIE.2010.2066539

Wang Y., Fang H., Sahinoglu Z., Wada T., and Hara S., 2015, adaptive estimation of the state of charge for lithium-ion batteries: nonlinear geometric observer approach, IEEE Transactions on Control Systems Technology, 23(3), pp. 948–962. https://doi.org/10.1109/TCST.2014.2356503

Watrin N., Roche R., Ostermann H., Blunier B., and Miraoui A., 2012, Multiphysical lithium-based battery model for use in state-of-charge determination, IEEE Transactions on Vehicular Technology, 61(8), pp. 3420–3429. https://doi.org/10.1109/TVT.2012.2205169

Wei, Z., Zhao, D., He, H., Cao, W. and Dong, G., 2020, A noise-tolerant model parameterization method for lithium-ion battery management system, Applied Energy, 268, p.114932. https://doi.org/10.1016/j.apenergy.2020.114932

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