Lithium-Ion Battery Modelling and Adaptive Extended Kalman Filter Implementation for BMS Application Software Development

Dewi Anggraeni, Budi Sudiarto, Farohaji Kurniawan, Purnomo Sidi Priambodo

Abstract


A custom lithium-Ion battery was built for the payload system on a single-engine two-seaters glider. The stages of software development in forming the Battery Management System as a way to provide security in the charging and discharging processes need some parameters to indicate the conditions of the battery. Therefore, in this study, the process of the State of Charge estimation which used in the Electrical Equivalent Circuit Methods and Adaptive Extended Kalman Filter was carried out in a 42 Ah Li-ion battery. As the results, a Mean Absolute Error, and a Root Mean Square Error in which the value of less than 1%. In the actual process, the real error value has never been discovered. The noise is given to determine the adaptive ability between the extended Kalman Filter and the Adaptive Extended Kalman Filter (AEKF) algorithms. In addition, the primary SoC is set at a certain value to see the estimation accuracy. The investigation shows that this method is possible to be applied in the development of the BMS software for payload systems.


Keywords


Li-ion Batteries

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References


C. Chasanah, D. Anggraeni, A. Wirawan, “Analysis of Battery Charging in the Development of BMS for Solar UAV Application”, 6th International Seminar of Aerospace Science and Technology, IOP Conf. Series: Journal of Physics: Conf. Series 2018, 1130, 012012, doi :10.1088/1742-6596/1130/1/012012

D. Anggraeni, A. Udhiarto, A. Wirawan, A. A. Ramadian, and N. R. Poespawati, “Development and Analysis Characteristic Solar Powered System on LAPAN Surveillance UAV 03 (LSU 03)”, AIP Conference Proceedings 2019, 2088, 030023, https://doi.org/10.1063/1.5095328.

F. S. Pranoto, A. Wirawan, D. A. Purnamasari, “Electrical Power Budgeting Analysis for LSA-02 UAV Technology Demonstrator”, IOP Conf. Series: Materials Science and Engineering 160(2016) 012088 doi:10.1088/1757-899X/160/1/012088

V. Pop, H.J. Bergveld, D. Danilov, P.P.L. Regtien, and P.H.L, Notten, “Battery Management Systems”, Springer Science+Business Media B.V 2008.

K. Ding, W. Li, S. Fang, L. Bing, L. Yiyang, Z. Yingying, “Design and research on a Power Distribution System for Airborne Radar”, The Journal of Engineering, 2019, 16, 2051-3305.

M.S.H. Lipu, M.A Hannan, A. Hussain, A. Ayob, M.H.M Saad, T.F.Karim, and How, D.N.T., “Data-drive State of Charge Estimation of Lithium Ion Batteries: Algorithms, Implementation, Factors, Limitations and Future Trends”, Journal of Cleaner production, 2020, 277, 124110 .

D.T. How, M.A. Hannan, M.S.H. Lipu; and P.J. Ker, “State of Charge Estimation for Lithium-Ion Batteries Using Model Based and Data-Driven Methods: A Review”, IEEE Access 2019, 7, 136116 – 136136, doi: 10.1109/ACCESS.2019.2942213.

M.U. Ali, A. Zafar, S. H. Nengroo, S. Hussain, M.J. Alvi, and H-J. Kim, :Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation”, Energies 2019,12, 446.

P. Shrivastava, T.K. Soon, M. Y. Idna Bin Indris, dan S. Mekhilef, “Overview of Model-Based Online State-of-Charge Estimation Using Kalman Filter Family for Lithium-Ion Batteries, Renewable and Sustainable Energy Reviews”, 2019, 113, 10933.

V. Esfahanian, A.A. Shahbazi, dan F. Torabi, “A Real-Time Battery Engine Simulation Tool (BEST) Based on Lumped Model and Reduced-Order Modes: Application to Lead-Acid Battery”, Journal of Energy Storage, 2019, Volume 24, 100780.

M.I. Wahyuddin, PS. Priambodo, H. Sudibyo, “State of Charge (SoC) Analysis and Modelling Battery Discharging Parameters”, 4th International Conference on Science and Technology (ICST) 2018, pp. 1-5. doi: 10.1109/ICSTC.2018.8528631.

C. Hou, S. Yang, J. Hu, H. Wang, and S. Xu, “A study of SOC Estimation Algorithm for Energy Storage Lithium Battery Pack Based on Information Fusion Technology”, International Conference on Power System Technology 2014, pp. 3157-3161, doi: 10.1109/POWERCON.2014.6993645.

L. Ran, W. Junfeng, W. Haiying, and L. Gechen, “Design on Battery Management System of LiFePO4 Power Battery Based on µC /OS-II”, 7th International Forum on Strategic Technology (IFOST) 2012, pp. 1-5, DOI: 10.1109/IFOST.2012.6357668

L.Linden, dan T.B. Reddy, “Handbook of Batteries”, McGraw Hill Handbook, Third Edition.

S. Zhang, X.Guo, and X. Zhang, “An Improved Adaptive Unscented Kalman Filtering for State of Charge Online Estimation of Lithium-Ion Battery”, Journal of Energy Storage 2020, 32, 101980.

B. Nemounehkhah, R. Faranda, K. Akkala, H. Hafezi, C. Parthasarathy, and H. Laaksonen, “Comparison and Evaluation of State of Charge Estimation Methods for a Verified Battery Model”, International Conference on Smart Energy Systems and Technologies (SEST), 2020, pp. 1-6, doi: 10.1109/SEST48500.2020.9203121.

Z. Wang, S. Zeng, J. Guo, T. Qin, “Remaining Capacity Estimation of Lithium-Ion Batteries Based on the Constant Voltage Charging Profile”, PLoS ONE 2018, 13(7): e0200169. https://doi.org/10.1371/journal.pone.0200169

J. Meng, D. Stroe, M. Ricco, G. Luo, and R. Teodorescu, “A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery with Dynamic Linear Model”, in IEEE Transactions on Industrial Electronics 2019, vol. 66, no. 10, pp. 7717-7727, doi: 10.1109/TIE.2018.2880668.

F. Poloei, A. Bakhshai, and Y. Liu, “A Novel Online Adaptive Fast Simple State of Charge Estimation for Lithium-Ion Batteries”, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA) 2017, pp. 914-918, doi: 10.1109/ICRERA.2017.8191193.

J. Luo, J. Peng, H. He, “Lithium-Ion Battery SOC Estimation Study Based on Cubature Kalman Filter”, Energy Procedia 2019, 158, 3421-3426, https://doi.org/10.1016/j.egypro.2019.01.933.

J. Turner, “3 Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells” (2013-01-1544), in Progress in Modelling and Simulation of Batteries, SAE 2016, pp.19-28

S. Cheng, Y. Zhang, X. Cheng, and X. Zhang, “State Estimation Using Physics-Based Equivalent Circuit Models of a Li-Ion Cell and Kalman Filter”, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 2017, pp. 5280-5286, doi: 10.1109/IECON.2017.8216914.

Matlab, Understanding Kalman Filters, Part 5: Nonlinear State Estimators, Available in: https://www.youtube.com/watch?v=Vefia3JMeHE&t=1s

R. Xiong, H. He, F. Sun, X. Liu, Z. Liu, “Model-Based State of Charge and Peak Power Capability Joint Estimation of Lithium-Ion Battery in Plug-In Hybrid Electric Vehicles”, Journal of Power Sources 2013, 229, Pages 159-169, https://doi.org/10.1016/j.jpowsour.2012.12.003.

Z. Cheng, J. Lv, Y. Liu, and Z. Yan, “Estimation of State of Charge for Lithium-Ion Battery Based on Finite Difference Extended Kalman Filter”, Journal of Applied Mathematics, Hindawi Publishing Corporation 2014, Article ID 348537, 10 pages, http://dx.doi.org/10.1155/2014/348537

P. Nian, Z. Shuzhi, Z. Xiongwen, “Co-Estimation for Capacity and State of Charge for Lithium-Ion Batteries Using Improved Adaptive Extended Kalman Filter”, Journal of Energy Storage 2021, 40, 102559, https://doi.org/10.1016/j.est.2021.102559 Received 3 December 2020.

Z. He, Z. Yang, X. Cui, and E. Li, “A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter”, IEEE Transactions On Vehicular Technology 2020, 69, NO. 12, pp. 14618-14630, doi: 10.1109/TVT.2020.3032201.

D. Yang, G. Qi, and X. Li, “State-of-Charge Estimation of LiFePO4/C Battery Based on Extended Kalman Filter”, IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) 2013, pp. 1-5, doi: 10.1109/APPEEC.2013.6837188.

Electric Vehicle Battery Test Procedure Manual. Revision 2, Published January 1996

Y.-C. Lin, and K.-J. Chung, “Lifetime Prognosis of Lithium-Ion Batteries Through Novel Accelerated Degradation Measurements and a Combined Gamma Process and Monte Carlo Method”, Appl. Sci. 2019, 9, 559, https://doi.org/10.3390/app9030559

G. Baure, and M. Dubarry , “Synthetic vs. Real Driving Cycles: A Comparison of Electric Vehicle Battery Degradation, Batteries 2019, 5(2), 42, https://doi.org/10.3390/batteries5020042

A. Fotouhi, K. Propp, and D. J. Auger, “Electric Vehicle Battery Model Identification and State of Charge Estimation in Real World Driving Cycles”, 7th Computer Science and Electronic Engineering Conference (CEEC) 2015, pp. 243-248, doi: 10.1109/CEEC.2015.7332732.

S. Peng, C. Chen, H. Shi, and Z. Yao, “State of Charge Estimation of Battery Energy Storage Systems Based on Adaptive Unscented Kalman Filter With a Noise Statistics Estimator”, in IEEE Access 2017, 5, pp. 13202-13212, doi: 10.1109/ACCESS.2017.2725301.

S. Kou, X. Gong, Q. Zhu, and G. Wang, “Parameter Identification of Battery Model Based on Forgetting Factor Recursive Least Square Method”, IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) 2018, pp. 1712-1715, doi: 10.1109/ITOEC.2018.8740487

Simin Peng, Xuelai Zhu, Yinjiao Xing, Hongbing Shi, Xu Cai, and Michael Pecht, “An Adaptive State of Charge Estimation Approach for Lithium-Ion Series-Connected Battery System”, Journal of Power Sources 2018, 392, Pages 48-59, https://doi.org/10.1016/j.jpowsour.2018.04.101

W. Li, Y. Yang, D. Wang, and S. Yin, “The Multi-Innovation Extended Kalman Filter Algorithm for Battery SOC Estimation”. Ionics 2020, 26, 6145–6156.

M. S. Grewal, and A. P. Andrews, “Kalman Filtering”, A. John Wiley & Sons, Inc., 2008.

Wang, D., Xu, X., and Hou, L., An Improved Adaptive Kalman Filter for Under Water SINS/DVL System, Mathematical Problems in Engineering, vol. 2020, Article ID 5456961, 14 pages, 2020. https://doi.org/10.1155/2020/5456961

He Z., Yang Z, Cui X. and Li E., “A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter”, in IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14618-14630, Dec. 2020, doi: 10.1109/TVT.2020.3032201.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i1.12882.g8693

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