Real-Time Detection of Internal Short Circuits in Lithium-Ion Batteries using an Extend Kalman Filter A Novel Approach Combining Electrical and Thermal Measurements
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Published
Oct 8, 2024
Yiqi Jia
Lorenzo Brancato
Francesco Cadini
Marco Giglio
Abstract
Concerns over fuel scarcity and environmental degradation largely drive the increasing popularity of electric vehicles (EVs). Lithium-ion batteries (LIBs), known for their high energy and power densities, are the favored power source for EVs. Over the past few decades, research has been concentrated on ensuring these batteries operate efficiently, safely, and reliably. A key issue impacting the safety of Li-ion batteries is thermal runaway (TR), which can lead to hazardous battery fires. Internal short circuits (ISCs) are often the primary cause of these TR incidents, making the early detection of spontaneous ISC formation a pivotal diagnostic task. This research introduces an innovative ISC detection technique for cylindrical Li-ion battery cells. This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. This framework enables real-time estimation of the internal ISC state while maintaining computational efficiency. The proposed method is tested numerically considering a high-fidelity numerical plant cycled using charge-depleting tests that mimic a practical battery cell working cycle at various C rates and at different ambient temperatures to account for both load and environmental uncertainties. The results demonstrate the robustness and effectiveness of the method. In addition, the method has been proven to be computationally efficient, demonstrating the feasibility of its real-time implementation.
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Keywords
Lithium-Ion Battery, Thermal runaway, Internal short circuit, Battery management system, Extended Kalman filter
References
Amine, K., Kanno, R., & Tzeng, Y. (2014). Rechargeable lithium batteries and beyond: Progress, challenges, and future directions. MRS Bulletin, 39(5), 395–401. doi: 10.1557/mrs.2014.62
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2010). Battery internal short-circuit detecting device and method, battery pack, and electronic device system (No. US20100188054A1).
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2012). Battery internal short-circuit detection apparatus and method, and battery pack (No. US8334699B2).
Bernardi, D., Pawlikowski, E., & Newman, J. (1985). A general energy balance for battery systems. Journal of the electrochemical society, 132(1), 5. doi: 10.1149/1.2113792
Chang, W.-Y. (2013). The state of charge estimating methods for battery: A review. International Scholarly Research Notices, 2013. doi: https://doi.org/10.1155/2013/953792
Feng, X., He, X., Lu, L., & Ouyang, M. (2018). Analysis on the Fault Features for Internal Short Circuit Detection Using an Electrochemical-Thermal Coupled Model. Journal of The Electrochemical Society, 165(2), A155–A167. doi: 10.1149/2.0501802jes
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2018). Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Materials, 10, 246-267. doi: https://doi.org/10.1016/j.ensm.2017.05.013
Gao, W., Li, X., Ma, M., Fu, Y., Jiang, J., & Mi, C. (2021). Case study of an electric vehicle battery thermal runaway and online internal short-circuit detection. IEEE Transactions on Power Electronics, 36(3), 2452-2455. doi: 10.1109/TPEL.2020.3013191
Goodenough, J. B., & Kim, Y. (2010). Challenges for rechargeable Li batteries. Chemistry of Materials, 22(3), 587–603. doi: 10.1021/cm901452z
Grabow, J., Klink, J., Orazov, N., Benger, R., Hauer, I., & Beck, H. P. (2023). Triggering and Characterisation of Realistic Internal Short Circuits in Lithium-Ion Pouch Cells—A New Approach Using Precise Needle Penetration. Batteries, 9(10). doi: 10.3390/batteries9100496
Gu,W., &Wang, C. (2000). Thermal-electrochemical modeling of battery systems. Journal of The Electrochemical Society, 147(8), 2910. doi: 10.1149/1.1393625
Jia, Y., Brancato, L., Giglio, M., & Cadini, F. (2024). Temperature enhanced early detection of internal short circuits in lithium-ion batteries using an extended kalman filter. Journal of Power Sources, 591, 233874. doi: https://doi.org/10.1016/j.jpowsour.2023.233874
Kong, X., Zheng, Y., Ouyang, M., Li, X., Lu, L., Li, J., & Zhang, Z. (2017). Signal synchronization for massive data storage in modular battery management system with controller area network. Applied Energy, 197, 52-62. doi: https://doi.org/10.1016/j.apenergy.2017.04.002
Kong, X., Zheng, Y., Ouyang, M., Lu, L., Li, J., & Zhang, Z. (2018). Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs. Journal of Power Sources, 395, 358–368. doi: https://doi.org/10.1016/j.jpowsour.2018.05.097
Lai, X., Jin, C., Yi, W., Han, X., Feng, X., Zheng, Y., & Ouyang, M. (2021). Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives. Energy Storage Materials, 35, 470-499. doi: https://doi.org/10.1016/j.ensm.2020.11.026
Lin, X., Perez, H. E., Mohan, S., Siegel, J. B., Stefanopoulou, A. G., Ding, Y., & Castanier, M. P. (2014). A lumped-parameter electro-thermal model for cylindrical batteries. Journal of Power Sources, 257, 1-11. doi: https://doi.org/10.1016/j.jpowsour.2014.01.097
Lin, X., Perez, H. E., Siegel, J. B., Stefanopoulou, A. G., Li, Y., Anderson, R. D., . . . Castanier, M. P. (2012). Online parameterization of lumped thermal dynamics in cylindrical lithium ion batteries for core temperature estimation and health monitoring. IEEE Transactions on Control Systems Technology, 21(5), 1745–1755. doi: 10.1109/TCST.2012.2217143
Liu, G., Lu, L., Fu, H., Hua, J., Li, J., Ouyang, M., . . . Chen, P. (2014). A comparative study of equivalent circuit models and enhanced equivalent circuit models of lithium-ion batteries with different model structures. In 2014 ieee conference and expo transportation electrification asia-pacific (itec asia-pacific) (p. 1-6). doi: 10.1109/ITEC-AP.2014.6940946
Liu, X., Li, Y., Kang, Y., Zhao, G., Duan, B., & Zhang, C. (2024). An accurate co-estimation of core temperature and state of charge for lithium-ion batteries with electrothermal model. IEEE Journal of Emerging and Selected Topics in Power Electronics, 12(1), 231-241. doi: 10.1109/JESTPE.2023.3304754
Ma, R., Deng, Y., & Wang, X. (2023). Simplified electrochemical model assisted detection of the early-stage internal short circuit through battery aging. Journal of Energy Storage, 66(March). doi: 10.1016/j.est.2023.107478
Naha, A., Khandelwal, A., Agarwal, S., Tagade, P., Hariharan, K. S., Kaushik, A., . . . Oh, B. (2020). Internal short-circuit detection in Li-ion batteries using supervised machine learning. Scientific Reports, 10(1), 1–10. doi: 10.1038/s41598-020-58021-7
Nise, N. S. (2020). Control systems engineering. John Wiley & Sons.
Park, C., & Jaura, A. K. (2003). Dynamic thermal model of li-ion battery for predictive behavior in hybrid and fuel cell vehicles (Tech. Rep.). SAE Technical Paper.
Qin, P., Che, Y., Li, H., Cai, Y., & Jiang, M. (2022). Joint soc–sop estimation method for lithium-ion batteries based on electro-thermal model and multi-parameter constraints. Journal of Power Electronics, 22(3), 490–502.
Saqli, K., Bouchareb, H., M’sirdi, N. K., & Bentaie, M. O. (2023). Lithium-ion battery electro-thermal modelling and internal states co-estimation for electric vehicles. Journal of Energy Storage, 63, 107072.
Seo, M., Park, M., Song, Y., & Kim, S. W. (2020). Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges. IEEE Access, 8, 70947–70959. doi: 10.1109/ACCESS.2020.2987363
Simon, D. (2006). Optimal state estimation: Kalman, h infinity, and nonlinear approaches. John Wiley & Sons.
Sun, C.-C., Chou, C.-H., Shieh, D.-T., & Chu, C.-W. (2021, March 16). Battery safety identifying method and method for setting hazard levels of battery internal short circuit and warning system using the same. Google Patents. (US Patent 10,948,544)
Walcott, B., & Zak, S. (1987). State observation of nonlinear uncertain dynamical systems. IEEE Transactions on Automatic Control, 32(2), 166-170. doi: 10.1109/TAC.1987.1104530
Xiao, B., & Xiao, B. (2021). A novel approach for internal short circuit prediction of lithium-ion batteries by random forest. International Journal of Electrochemical Science, 16(4), 210463. doi: https://doi.org/10.20964/2021.04.21
Zhang, G., Wei, X., Tang, X., Zhu, J., Chen, S., & Dai, H. (2021). Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review. Renewable and Sustainable Energy Reviews, 141(January). doi: 10.1016/j.rser.2021.110790
Zhang, J., Zhou, Z., & Huang, X. (2018). The application of ekf in parameter identification of state-space model. In 2018 eighth international conference on instrumentation measurement, computer, communication and control (imccc) (p. 798-802). doi: 10.1109/IMCCC.2018.00171
Zhang, M. X., Du, J. Y., Liu, L. S., Siegel, J., Lu, L. G., He, X. M., & Ouyang, M. G. (2018). Internal short-circuit detection method for battery pack based on circuit topology. Science China Technological Sciences, 61(10), 1502–1511. doi: 10.1007/s11431-017-9299-3
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2010). Battery internal short-circuit detecting device and method, battery pack, and electronic device system (No. US20100188054A1).
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2012). Battery internal short-circuit detection apparatus and method, and battery pack (No. US8334699B2).
Bernardi, D., Pawlikowski, E., & Newman, J. (1985). A general energy balance for battery systems. Journal of the electrochemical society, 132(1), 5. doi: 10.1149/1.2113792
Chang, W.-Y. (2013). The state of charge estimating methods for battery: A review. International Scholarly Research Notices, 2013. doi: https://doi.org/10.1155/2013/953792
Feng, X., He, X., Lu, L., & Ouyang, M. (2018). Analysis on the Fault Features for Internal Short Circuit Detection Using an Electrochemical-Thermal Coupled Model. Journal of The Electrochemical Society, 165(2), A155–A167. doi: 10.1149/2.0501802jes
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2018). Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Materials, 10, 246-267. doi: https://doi.org/10.1016/j.ensm.2017.05.013
Gao, W., Li, X., Ma, M., Fu, Y., Jiang, J., & Mi, C. (2021). Case study of an electric vehicle battery thermal runaway and online internal short-circuit detection. IEEE Transactions on Power Electronics, 36(3), 2452-2455. doi: 10.1109/TPEL.2020.3013191
Goodenough, J. B., & Kim, Y. (2010). Challenges for rechargeable Li batteries. Chemistry of Materials, 22(3), 587–603. doi: 10.1021/cm901452z
Grabow, J., Klink, J., Orazov, N., Benger, R., Hauer, I., & Beck, H. P. (2023). Triggering and Characterisation of Realistic Internal Short Circuits in Lithium-Ion Pouch Cells—A New Approach Using Precise Needle Penetration. Batteries, 9(10). doi: 10.3390/batteries9100496
Gu,W., &Wang, C. (2000). Thermal-electrochemical modeling of battery systems. Journal of The Electrochemical Society, 147(8), 2910. doi: 10.1149/1.1393625
Jia, Y., Brancato, L., Giglio, M., & Cadini, F. (2024). Temperature enhanced early detection of internal short circuits in lithium-ion batteries using an extended kalman filter. Journal of Power Sources, 591, 233874. doi: https://doi.org/10.1016/j.jpowsour.2023.233874
Kong, X., Zheng, Y., Ouyang, M., Li, X., Lu, L., Li, J., & Zhang, Z. (2017). Signal synchronization for massive data storage in modular battery management system with controller area network. Applied Energy, 197, 52-62. doi: https://doi.org/10.1016/j.apenergy.2017.04.002
Kong, X., Zheng, Y., Ouyang, M., Lu, L., Li, J., & Zhang, Z. (2018). Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs. Journal of Power Sources, 395, 358–368. doi: https://doi.org/10.1016/j.jpowsour.2018.05.097
Lai, X., Jin, C., Yi, W., Han, X., Feng, X., Zheng, Y., & Ouyang, M. (2021). Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives. Energy Storage Materials, 35, 470-499. doi: https://doi.org/10.1016/j.ensm.2020.11.026
Lin, X., Perez, H. E., Mohan, S., Siegel, J. B., Stefanopoulou, A. G., Ding, Y., & Castanier, M. P. (2014). A lumped-parameter electro-thermal model for cylindrical batteries. Journal of Power Sources, 257, 1-11. doi: https://doi.org/10.1016/j.jpowsour.2014.01.097
Lin, X., Perez, H. E., Siegel, J. B., Stefanopoulou, A. G., Li, Y., Anderson, R. D., . . . Castanier, M. P. (2012). Online parameterization of lumped thermal dynamics in cylindrical lithium ion batteries for core temperature estimation and health monitoring. IEEE Transactions on Control Systems Technology, 21(5), 1745–1755. doi: 10.1109/TCST.2012.2217143
Liu, G., Lu, L., Fu, H., Hua, J., Li, J., Ouyang, M., . . . Chen, P. (2014). A comparative study of equivalent circuit models and enhanced equivalent circuit models of lithium-ion batteries with different model structures. In 2014 ieee conference and expo transportation electrification asia-pacific (itec asia-pacific) (p. 1-6). doi: 10.1109/ITEC-AP.2014.6940946
Liu, X., Li, Y., Kang, Y., Zhao, G., Duan, B., & Zhang, C. (2024). An accurate co-estimation of core temperature and state of charge for lithium-ion batteries with electrothermal model. IEEE Journal of Emerging and Selected Topics in Power Electronics, 12(1), 231-241. doi: 10.1109/JESTPE.2023.3304754
Ma, R., Deng, Y., & Wang, X. (2023). Simplified electrochemical model assisted detection of the early-stage internal short circuit through battery aging. Journal of Energy Storage, 66(March). doi: 10.1016/j.est.2023.107478
Naha, A., Khandelwal, A., Agarwal, S., Tagade, P., Hariharan, K. S., Kaushik, A., . . . Oh, B. (2020). Internal short-circuit detection in Li-ion batteries using supervised machine learning. Scientific Reports, 10(1), 1–10. doi: 10.1038/s41598-020-58021-7
Nise, N. S. (2020). Control systems engineering. John Wiley & Sons.
Park, C., & Jaura, A. K. (2003). Dynamic thermal model of li-ion battery for predictive behavior in hybrid and fuel cell vehicles (Tech. Rep.). SAE Technical Paper.
Qin, P., Che, Y., Li, H., Cai, Y., & Jiang, M. (2022). Joint soc–sop estimation method for lithium-ion batteries based on electro-thermal model and multi-parameter constraints. Journal of Power Electronics, 22(3), 490–502.
Saqli, K., Bouchareb, H., M’sirdi, N. K., & Bentaie, M. O. (2023). Lithium-ion battery electro-thermal modelling and internal states co-estimation for electric vehicles. Journal of Energy Storage, 63, 107072.
Seo, M., Park, M., Song, Y., & Kim, S. W. (2020). Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges. IEEE Access, 8, 70947–70959. doi: 10.1109/ACCESS.2020.2987363
Simon, D. (2006). Optimal state estimation: Kalman, h infinity, and nonlinear approaches. John Wiley & Sons.
Sun, C.-C., Chou, C.-H., Shieh, D.-T., & Chu, C.-W. (2021, March 16). Battery safety identifying method and method for setting hazard levels of battery internal short circuit and warning system using the same. Google Patents. (US Patent 10,948,544)
Walcott, B., & Zak, S. (1987). State observation of nonlinear uncertain dynamical systems. IEEE Transactions on Automatic Control, 32(2), 166-170. doi: 10.1109/TAC.1987.1104530
Xiao, B., & Xiao, B. (2021). A novel approach for internal short circuit prediction of lithium-ion batteries by random forest. International Journal of Electrochemical Science, 16(4), 210463. doi: https://doi.org/10.20964/2021.04.21
Zhang, G., Wei, X., Tang, X., Zhu, J., Chen, S., & Dai, H. (2021). Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review. Renewable and Sustainable Energy Reviews, 141(January). doi: 10.1016/j.rser.2021.110790
Zhang, J., Zhou, Z., & Huang, X. (2018). The application of ekf in parameter identification of state-space model. In 2018 eighth international conference on instrumentation measurement, computer, communication and control (imccc) (p. 798-802). doi: 10.1109/IMCCC.2018.00171
Zhang, M. X., Du, J. Y., Liu, L. S., Siegel, J., Lu, L. G., He, X. M., & Ouyang, M. G. (2018). Internal short-circuit detection method for battery pack based on circuit topology. Science China Technological Sciences, 61(10), 1502–1511. doi: 10.1007/s11431-017-9299-3
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Technical Papers