Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter
Tóm tắt
Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.
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Tài liệu tham khảo
Reddy, T. (2010). Linden’s Handbook of Batteries, McGraw-Hill Education. [4th ed.].
Tarascon, 2001, Issues and challenges facing rechargeable lithium batteries, Nature, 414, 359, 10.1038/35104644
Buchmann, I. (2016). Batteries in a Portable World: A Handbook on Rechargeable Batteries for Non-Engineers, Cadex Electronics Inc.. [4th ed.].
Lu, 2013, A review on the key issues for lithium-ion battery management in electric vehicles, J. Power Sources, 226, 272, 10.1016/j.jpowsour.2012.10.060
Cheng, 2011, Battery-Management System (BMS) and SOC Development for Electrical Vehicles, IEEE Trans. Veh. Technol., 60, 76, 10.1109/TVT.2010.2089647
Bergveld, H.J., Kruijt, W.S., and Notten, P.H.L. (2002). Battery Management Systems. Battery Management Systems: Design by Modelling, Springer.
Lewis, I., and Pierce, W. (2016). Battery Management System and Method. (9,440,544), U.S. Patent.
Chang, 2013, The state of charge estimating methods for battery: A review, ISRN Appl. Math., 2013, 953792, 10.1155/2013/953792
Waag, 2014, Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles, J. Power Sources, 258, 321, 10.1016/j.jpowsour.2014.02.064
Chaturvedi, 2010, Algorithms for Advanced Battery-Management Systems, IEEE Control Syst., 30, 49, 10.1109/MCS.2010.936293
Moura, S.J., Krstic, M., and Chaturvedi, N.A. (2012, January 17–19). Adaptive PDE observer for battery SOC/SOH estimation. Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference, Fort Lauderdale, FL, USA.
Awadallah, 2016, Accuracy improvement of {SOC} estimation in lithium-ion batteries, J. Energy Storage, 6, 95, 10.1016/j.est.2016.03.003
Dong, 2015, A method for state of energy estimation of lithium-ion batteries based on neural network model, Energy, 90, 879, 10.1016/j.energy.2015.07.120
Ng, 2009, Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries, Appl. Energy, 86, 1506, 10.1016/j.apenergy.2008.11.021
Baccouche, I., Mlayah, A., Jemmali, S., Manai, B., and Essoukri Ben Amara, N. (2015, January 16–19). Implementation of a Coulomb counting algorithm for SOC estimation of Li-Ion battery for multimedia applications. Proceedings of the 2015 IEEE 12th International Multi-Conference on Systems, Signals Devices (SSD15), Sakiet Ezzit Sfax, Tunisia.
Jeong, Y.M., Cho, Y.K., Ahn, J.H., Ryu, S.H., and Lee, B.K. (2014, January 14–18). Enhanced Coulomb counting method with adaptive SOC reset time for estimating OCV. Proceedings of the 2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA.
Nugroho, A., Rijanto, E., Wijaya, F.D., and Nugroho, P. (2015, January 5–7). Battery state of charge estimation by using a combination of Coulomb Counting and dynamic model with adjusted gain. Proceedings of the 2015 International Conference on Sustainable Energy Engineering and Application (ICSEEA), Bandung, Indonesia.
Tian, Y., Li, D., Tian, J., and Xia, B. (2016, January 4–6). A comparative study of state-of-charge estimation algorithms for lithium-ion batteries in wireless charging electric vehicles. Proceedings of the 2016 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW), Knoxville, TN, USA.
He, 2011, State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model, IEEE Trans. Veh. Technol., 60, 1461, 10.1109/TVT.2011.2132812
Zhang, C., Jiang, J., Zhang, L., Liu, S., Wang, L., and Loh, P.C. (2016). A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery. Energies, 9.
Wang, 2017, Correlation between the model accuracy and model-based {SOC} estimation, Electrochim. Acta, 228, 146, 10.1016/j.electacta.2017.01.057
Weng, 2014, A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring, J. Power Sources, 258, 228, 10.1016/j.jpowsour.2014.02.026
Nikolian, A., Firouz, Y., Gopalakrishnan, R., Timmermans, J.M., Omar, N., van den Bossche, P., and van Mierlo, J. (2016). Lithium ion batteries-Development of advanced electrical equivalent circuit models for nickel manganese cobalt lithium-ion. Energies, 9.
Ashwin, 2017, Electrochemical modeling of Li-ion battery pack with constant voltage cycling, J. Power Sources, 341, 327, 10.1016/j.jpowsour.2016.11.092
Sun, K., and Shu, Q. (2011, January 22–24). Overview of the types of battery models. Proceedings of the IEEE 2011 30th Chinese Control Conference (CCC), Yantai, China.
Ghossein, N.E., Salameh, J.P., Karami, N., Hassan, M.E., and Najjar, M.B. (May, January 29). Survey on electrical modeling methods applied on different battery types. Proceedings of the 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Beirut, Lebanon.
Omar, 2014, Optimization of an advanced battery model parameter minimization tool and development of a novel electrical model for lithium-ion batteries, Int. Trans. Electr. Energy Syst., 24, 1747, 10.1002/etep.1815
Hua, 2015, Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles, Energies, 8, 3556, 10.3390/en8053556
Roscher, 2011, OCV hysteresis in Li-ion batteries including two-phase transition materials, Int. J. Electrochem., 2011, 984320, 10.4061/2011/984320
Kim, J., Seo, G.S., Chun, C., Cho, B.H., and Lee, S. (2012, January 4–8). OCV hysteresis effect-based SOC estimation in extended Kalman filter algorithm for a LiFePO4/C cell. Proceedings of the 2012 IEEE International Electric Vehicle Conference, Greenville, SC, USA.
Xing, 2014, State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures, Appl. Energy, 113, 106, 10.1016/j.apenergy.2013.07.008
Zhang, 2015, An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries, J. Power Sources, 283, 24, 10.1016/j.jpowsour.2015.02.099
Tran, N.T., Khan, A.B., and Choi, W. (2017). State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation. Energies, 10.
Hu, 2012, Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries, J. Power Sources, 217, 209, 10.1016/j.jpowsour.2012.06.005
Plett, 2004, Extended Kalman filtering for battery management systems of LiPB-based {HEV} battery packs: Part 2. Modeling and identification, J. Power Sources, 134, 262, 10.1016/j.jpowsour.2004.02.032
Widanage, 2016, Design and use of multisine signals for Li-ion battery equivalent circuit modeling. Part 2: Model estimation, J. Power Sources, 324, 61, 10.1016/j.jpowsour.2016.05.014
Luo, Z., Li, Y., and Lou, Y. (2015, January 8–10). An adaptive Kalman filter to estimate state-of-charge of lithium-ion batteries. Proceedings of the 2015 IEEE International Conference on Information and Automation, Lijiang, China.
Baccouche, I., Jemmali, S., Manai, B., Chaibi, R., and Essoukri Ben Amara, N. (2016, January 22–24). Hardware implementation of an algorithm based on Kalman filtrer for monitoring low capacity Li-ion batteries. Proceedings of the 2016 7th International Renewable Energy Congress (IREC), Hammamet, Tunisia.
Szumanowski, 2008, Battery Management System Based on Battery Nonlinear Dynamics Modeling, IEEE Trans. Veh. Technol., 57, 1425, 10.1109/TVT.2007.912176
Chen, 2006, Accurate electrical battery model capable of predicting runtime and I-V performance, IEEE Trans. Energy Conversion, 21, 504, 10.1109/TEC.2006.874229
Hu, 2011, Electro-thermal battery model identification for automotive applications, J. Power Sources, 196, 449, 10.1016/j.jpowsour.2010.06.037
Neumann, D., and Lichte, S. (2011, January 10–11). A multidimensional battery discharge model with thermal feedback applied to a lithium-ion battery pack. Proceedings of the NDIA Ground Vehicle Systems Engineering and Technology Symposium, Dearborn, MI, USA.
Pattipati, 2014, Open circuit voltage characterization of lithium-ion batteries, J. Power Sources, 269, 317, 10.1016/j.jpowsour.2014.06.152
Goutam, 2015, Comparative Study of Surface Temperature Behavior of Commercial Li-Ion Pouch Cells of Different Chemistries and Capacities by Infrared Thermography, Energies, 8, 8175, 10.3390/en8088175
Cicconi, 2017, Thermal analysis and simulation of a Li-ion battery pack for a lightweight commercial {EV}, Appl. Energy, 192, 159, 10.1016/j.apenergy.2017.02.008
Huria, T., Ceraolo, M., Gazzarri, J., and Jackey, R. (2013). Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells, SAE International. SAE Technical Paper 2013-01-1544.
Tjandra, R., Tseng, K.J., Thanagasundram, S., and Jossen, A. (2015, January 18–22). State of charge estimation considering OCV hysteresis in lithium iron phosphate battery for UPS applications. Proceedings of the 2015 IEEE International Telecommunications Energy Conference (INTELEC), Osaka, Japan.
Cho, I., and Kim, D. (2006). Method of Setting Initial Value of SOC of Battery Using OCV Hysteresis Depending on Temperatures. (11/370,403), U.S. Patent App.
Yuan, 2013, State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model, Energies, 6, 444, 10.3390/en6010444
Zhang, 2015, Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery, J. Power Sources, 289, 50, 10.1016/j.jpowsour.2015.04.148
Plett, 2006, Sigma-point Kalman filtering for battery management systems of LiPB-based {HEV} battery packs: Part 2: Simultaneous state and parameter estimation, J. Power Sources, 161, 1369, 10.1016/j.jpowsour.2006.06.004
Yu, 2015, State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization, Energies, 8, 7854, 10.3390/en8087854