An Improved Model for Parameters Identification of Lithium-ion Battery Based on Dual Kalman Filter

Zheng Liu; Xuanju Dang; Benqin Jing; Jianbo Ji

doi:10.6180/jase.201912_22(4).0002

An Improved Model for Parameters Identification of Lithium-ion Battery Based on Dual Kalman Filter

Zheng Liu^1,2, Xuanju Dang This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Benqin Jing^1,2 and Jianbo Ji²

¹School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guangxi Province, 541004, P.R. China
²School of Electronic and Automation, Guilin University of Aerospace Technology, Guangxi Province, 541004, P.R. China

Received: December 4, 2018
Accepted: September 6, 2019
Publication Date: December 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201912_22(4).0002

ABSTRACT

Reliable model parameters identification is the key evaluation index for battery management system (BMS) in electric vehicles (EVs). To ensure the sustainability of lithium-ion battery (LIB) under unknown measurement noise, an effective LIB model with updated parameters should be developed. To soften the impact of measurement noise from the transducer, a combined equivalent circuit model (ECM) that considers the current noise as a compensation factor is introduced into the LIB. To identify the model parameters recursively based on suppression of the parameters perturbations in the ECM, a dual extended kalman filter algorithm is applied. Finally, the Dynamic Stress Test sequence (DST) and the Federal Urban Driving Schedule (FUDS) are loaded on LIB to test the validity of the improved approach. The experiment results demonstrate the effectiveness of improved model and filtering method in terms of parameters identification.

Keywords: Lithium-ion Battery, Parameters Identification, Dual Kalman Filter, Equivalent-circuit Model

REFERENCES

[1] Shareef, H., M. Islam, and A. Mohamed (2016) A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles, Renewable & Sustainable Energy Reviews 64, 403_420. doi: 10.1016/j.rser.2016.06.033
[2] Saw, L., Y. Ye, and A. Tay (2016) Integration issues of lithium-ion battery into electric vehicles battery pack, Journal of Cleaner Production, 113, 1032_1045. doi: 10.1016/j.jclepro.2015.11.011
[3] Yong, J., V. Ramachandaramurthy, K. Tan, and N. Mithulananthan (2015) A review on the state-of-theart technologies of electric vehicle, its impacts and prospects, Renewable & Sustainable Energy Reviews 49, 365_385. doi: 10.1016/j.rser.2015.04.130
[4] Abada, S., G. Marlair, A. Lecocq, M. Petit, V. Sauvant-Moynot, and F. Huet (2016) Safety focused modeling of lithium-ion batteries: a review, Journal of Power Sources 306, 178_192. doi: 10.1016/j.jpowsour. 2015.11.100
[5] Jaguemont, J., L. Boulon, and Y. Dube (2016) Characterization and modeling of a hybrid-electric-vehicle lithium-ion battery pack at low temperatures, IEEE Transaction on Vehicular Technolpgy 65, 1_14. doi:10.1109/TVT.2015.2391053
[6] Basu, S., K. Hariharan, S. Kolake, T. Song, D. Sohn, and T. Yeo (2016) Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system, Applied Energy 181, 1_13. doi: 10.1016/j.apenergy.2016.08.049
[7] Chaoui, H., C. be-Ekeocha, A. Mejdoubi, A. Oukaour, H. Gualous, and N. Omar (2016) State of charge identification of LiFePO4 batteries with temperature variations using neural networks, IEEE 25th International Symposium on Industrial Electronics 286_291. doi: 10.1109/ISIE.2016.7744904
[8] Tian, Y., D. Li, J. Tian, and B. Xia (2017) State of charge identification of lithium-ion batteries using an optimal adaptive gain nonlinear observer, Electrochimica Acta 225, 225_234. doi: 10.1016/j.electacta.2016.12.119
[9] Rahman, M., S. Anwar, and A. Izadian (2016) Electrochemical model parameter identification of a lithiumion battery using particle swarm optimization method, Journal of Power Sources 307, 86_97. doi: 10.1016/j.jpowsour.2015.12.083
[10] Smith, K., C. Rahn, and C.Wang (2009) Model-based electrochemical identification and constraint management for pulse operation of lithium ion batteries, IEEE Transaction on Control Systems Technology 18(3), 654_663. doi: 10.1109/TCST.2009.2027023
[11] Wijewardana, S., R. Vepa, and M. Shaheed (2016) Dynamic battery cell model and state of charge identification, Journal of Power Sources 308, 109_120. doi: 10.1016/j.jpowsour.2016.01.072
[12] Duang, V., H. Bastawrous, K. Lim, K. See, P. Zhang, and S. Dou (2015) Online state of charge and model parameters identification of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares, Journal of Power Sources 296, 215_224. doi: 10.1016/j.jpowsour.2015.07.041
[13] Zou, Y., S. Li, B. Shao, and B. Wang (2016) Statespace model with non-integer order derivatives for lithium- ion battery, Applied Energy 161, 330_336. doi: 10.1016/j.apenergy.2015.10.025
[14] Lai, X., Y. Zheng, and T. Sun (2018) A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries, Electrochimica Acta 259, 566_577. doi: 10.1016/j.electacta.2017.10.153
[15] Xiong, R., H. He, F. Sun, and K. Zhao (2012) Online identification of peak power capability of Li-ion batteries in electric vehicles by a hardware-in-loop approach, Energies 5, 1455_1469. doi: 10.3390/en5051455
[16] Wang, Y., C. Zhang, and Z. Chen (2017) On-line battery state-of-charge identification based on an integrated estimator, Applied Energy 185, 2026_2032. doi:10.1016/j.apenergy.2015.09.015
[17] Wu, H., S. Yuan, X. Zhang, C. Yin, and X. Ma (2015) Model parameter identification approach based on incremental analysis for lithium-ion batteries without using open circuit voltage, Journal of Power Sources 287, 108_118. doi: 10.1016/j.jpowsour.2015.04.037
[18] Liu, C., W. Liu, L. Wang, G. Hu, L. Ma, and B. Ren (2016) A new method of modeling and state of charge
identification of the battery, Journal of Power Sources 320, 1_12. doi: 10.1016/j.jpowsour.2016.03.112
[19] Wang, B., S. Li, H. Peng, and Z. Liu (2015) Fractional-order modeling and parameter identification for lithium-ion batteries, Journal of Power Sources 293, 151_161. doi: 10.1016/j.jpowsour.2015.05.059
[20] Feng, T., L. Yang, X. Zhao, H. Zhang, and J. Qiang (2015) Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction, Journal of Power Sources 281, 192_203. doi: 10.1016/j.jpowsour.2015.01.154
[21] Pan, H., Z. Lü,W. Lin, J. Li, and L. Chen (2017) State of charge identification of lithium-ion batteries using a
grey extended Kalman filter and a novel open-circuit voltage model, Energy 138, 764_775. doi: 10.1016/j.energy.2017.07.099
[22] Yu, Q., R. Xiong, C. Lin,W. Shen, and J. Deng (2017) Lithium-ion battery parameters and state-of-charge joint identification based on H infinity and unscented Kalman filters, IEEE Transaction on Vehicular Technology 99, 1. doi: 10.1109/TVT.2017.2709326
[23] Zhang, C., W. Allafi, Q. Dinh, P. Ascencio, and J. Marco (2018) Online identification of battery equivalent circuit model parameters and state of charge using decoupled least squares technique, Energy 142, 678_688. doi: 10.1016/j.energy.2017.10.043
[24] He, Y., X. Liu, C. Zhang, and Z. Chen (2013) A new model for state-of-charge (SOC) identification for highpower Li-ion batteries, Applied Energy 101, 808_814. doi: 10.1016/j.apenergy.2012.08.031
[25] Zhang, C., K. Li, L. Pei, and C. Zhu (2015) An integrated approach for real-time model-based state-ofcharge identification of lithium-ion batteries, Journal of Power Sources 283, 24_36. doi: 10.1016/j.jpowsour.2015.02.099
[26] Pei, L., C. Zhu, T. Wang, R. Lu, and C. Chan (2014) Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles, Energy 66, 766_778. doi: 10.1016/j.energy.2014.02.009