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

Y. Muratoglu, A. Alkaya

Abstract


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.


Keywords


combined dynamic modelling; li-ion battery; passive balance control; SoC based equalization; SoC estimation; unscented Kalman filter

Full Text:

PDF

References


M. A. Hannan, M. S. H. Lipu, A. Hussain, A. Mohamed, “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations”, Renewable and Sustainable Energy Reviews, Vol. 78, pp. 834–854, 2017

V. H. M. Nguyen, C. V. Vo, L. D. L. Nguyen, B. T. T. Phan, “Green scenarios for power generation in Vietnam by 2030”, Engineering, Technology & Applied Science Research, Vol. 9, No. 2, pp. 4019-4026, 2019

E. V. Palconit, M. L. S. Abundo, “Transitioning to green maritime transportation in Philippines: Mapping of potential sites for electric ferry operations”, Engineering, Technology & Applied Science Research, Vol. 9, No. 1, pp. 3770-3773, 2019

G. E. Blomgren, “The development and future of lithium ion batteries”, Journal of the Electrochemical Society, Vol. 164, No. 1, pp. A5019-A5025, 2017

X. Hu, C. Zou, C. Zhang, Y. Li, “Technological developments in batteries: A survey of principal roles, types, and management needs”, IEEE Power and Energy Magazine, Vol. 15, No. 5, pp. 20-31, 2017

P. Shen, M. Ouyang, L. Lu, J. Li, X. Feng, “The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles”, IEEE Transactions on Vehicular Technology, Vol. 67, No. 1, pp. 92-103, 2018

X. Wang, J. Xu, Y. Zhao, “Wavelet based denoising for the estimation of the state of charge for lithium-ion batteries”, Energies, Vol. 11, No. 5, pp. 1144, 2018

L. Lu, X. Han, J. Li, J. Hua, M. Ouyang, “A review on the key issues for lithium-ion battery management in electric vehicles”, Journal of Power Sources, Vol. 226, pp. 272-288, 2013

A. Fotouhi, D. J. Auger, K. Propp, S. Longo, M. Wild, “A review on electric vehicle battery modelling: from lithium-ion toward lithium–sulphur”, Renewable and Sustainable Energy Reviews, Vol. 56, pp. 1008-1021, 2016

X. Lai, Y. Zheng, T. Sun, “A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries”, Electrochimica Acta, Vol. 259, pp. 566-577, 2018

C. Zhang, W. Allafi, Q. Dinh, P. Ascencio, J. Marco, “Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique”, Energy, Vol. 142, pp. 678-688, 2018

R. Xiong, J. Cao, Q. Yu, H. He, F. Sun, “Critical review on the battery state of charge estimation methods for electric vehicles”, IEEE Access, Vol. 6, pp. 1832-1843, 2017

R. Zhang, B. Xia, B. Li, L. Cao, Y. Lai, W. Zheng, “State of the art of lithium-ion battery SOC estimation for electrical vehicles”, Energies, Vol. 11, No. 7, pp. 1820, 2018

Y. Zheng, M. Ouyang, X. Han, L. Lu, J. Li, “Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles”, Journal of Power Sources, Vol. 377, pp. 161-188, 2018

W. Y. Chang, “The state of charge estimating methods for battery: A review”, ISRN Applied Mathematics, Vol. 2013, Article ID 953792, 2013

N. C. Eli-Chukwu, “Applications of artificial intelligence in agriculture: A review”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4377-4383, 2019

K. S. Ng, Y. F. Huang, C. S. Moo, Y. C. Hsieh, “An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries”, 31st International Telecommunications Energy Conference, Incheon, South Korea, October 18-22, 2009

S. Wang, C. Fernandez, L. Shang, Z. Li, H. Yuan, “An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs”, Transactions of the Institute of Measurement and Control, Vol. 40, No. 6, pp. 1892-1910, 2017

C. Zhang, J. Jiang, L. Zhang, S. Liu, L. Wang, P. C. Loh, “A generalized SOC-OCV model for lithium-ion batteries and the SOC estimation for LNMCO battery”, Energies, Vol. 9, No. 11, Article ID 900, 2016

L. Lavigne, J. Sabatier, J. M. Francisco, F. Guillemard, A. Noury, “Lithium-ion open circuit voltage (OCV) curve modelling and its ageing adjustment”, Journal of Power Sources, Vol. 324, pp. 694-703, 2016

M. Charkhgard, M. Farrokhi, “State-of-charge estimation for lithium-ion batteries using neural networks and EKF”, IEEE Transactions on Industrial Electronics, Vol. 57, No. 12, pp. 4178-4187, 2010

L. Xu, J. Wang, Q. Chen, “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model”, Energy Conversion and Management, Vol. 53, No. 1, pp. 33-39, 2012

G. Burgers, P. J. V. Leeuwen, G. Evensen, “Analysis scheme in the ensemble Kalman filter”, Monthly Weather Review, Vol. 126, No. 6, pp. 1719-1724, 1998

O. Aydogdu, M. L. Levent, “Kalman state estimation and LQR assisted adaptive control of a variable loaded servo system”, Engineering, Technology & Applied Science Research, Vol. 9, No. 3, pp. 4125-4130, 2019

K. Fujii, Extended Kalman filter, The ACFA-Sim-J Group, 2013

F. Claude, M. Becherif, H. S. Ramadan, “Experimental validation for li-ion battery modeling using extended Kalman filters”, International Journal of Hydrogen Energy, Vol. 42, No. 40, pp. 25509-25517, 2017

S. Jung, H. Jeong, “Extended Kalman filter-based state of charge and state of power estimation algorithm for unmanned aerial vehicle li-po battery packs”, Energies, Vol. 10, No. 8, pp. 1237, 2017

M. Mathew, S. Janhunen, M. Rashid, F. Long, M. Fowler, “Comparative analysis of lithium-ion battery resistance estimation techniques for battery management systems”, Energies, Vol. 11, No. 6, pp. 1490, 2018

E. A. Wan, R. V. D. Merwe, “The unscented Kalman filter for nonlinear estimation”, Adaptive Systems for Signal Processing, Communications, and Control Symposium, Alberta, Canada, October 4, 2000

Y. He, X. Liu, C. Zhang, Z. H. Chen, “A new model for state-of-charge (SOC) estimation for high-power li-ion batteries”, Applied Energy, Vol. 101, pp. 808-814, 2013

W. He, N. Williard, C. Chen, M. Pecht, “State of charge estimation for electric vehicle batteries using unscented Kalman filtering”, Microelectronics Reliability, Vol. 53, No. 6, pp. 840-847, 2013

H. He, H. Qin, X. Sun, Y. Shui, “Comparison study on the battery SoC estimation with EKF and UKF algorithms”, Energies, Vol. 6, No. 10, pp. 5088-5100, 2013

S. Peng, C. Chen, H. Shi, Z. Yao, “State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator”, IEEE Access, Vol. 5, pp. 13202-13212, 2017

Y. Ma, P. Duan, Y. Sun, H. Chen, “Equalization of lithium-ion battery pack based on fuzzy logic control in electric vehicle”, IEEE Transactions on Industrial Electronics, Vol. 65, No. 8, pp. 6762-6771, 2018

D. D. Quinn, T. T. Hartley, “Design of novel charge balancing networks in battery packs”, Journal of Power Sources, Vol. 240, pp. 26-32, 2013

Y. Zheng, L. Lu, X. Han, J. Li, M. Ouyang, “LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation”, Journal of Power Sources, Vol. 226, pp. 33-41, 2013

Y. Li, C. Wang, J. Gong, “A combination Kalman filter approach for state of charge estimation of lithium-ion battery considering model uncertainty”, Energy, Vol. 109, pp. 933-946, 2016




eISSN: 1792-8036     pISSN: 2241-4487