An Enhanced Model-Based Algorithm for Early Internal Short Circuit Detection in Lithium-Ion Batteries
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Sep 4, 2023
Yiqi Jia
Lorenzo Brancato
Marco Giglio
Francesco Cadini
Abstract
Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.
##plugins.themes.bootstrap3.article.details##
Keywords
Lithium-ion Battery, Model-based PHM, Battery fault detection, Internal short circuit
References
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2010). Battery internal short-circuit detecting device and method, battery pack, and electronic device system (Patent No. US20100188054A1).
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2012). Battery internal short-circuit detection apparatus and method, and battery pack (Patent No. US8334699B2).
Bernardi, D., Pawlikowski, E., & Newman, J. (1985). A General Energy Balance For Battery Systems. Journal of the Electrochemical Society, 132(1), 5–12. https://doi.org/10.1149/1.2113792
Bizeray, A. M., Zhao, S., Duncan, S. R., & Howey, D. A. (2015). Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter. Journal of Power Sources, 296, 400–412. https://doi.org/10.1016/j.jpowsour.2015.07.019
Comparison Chart - Orion BMS. (n.d.). Https://Www.Orionbms.Com/Comparison/.
Fang, W., Ramadass, P., & Zhang, Z. (2014). Study of internal short in a Li-ion cell-II. Numerical investigation using a 3D electrochemical-thermal model. Journal of Power Sources, 248, 1090–1098. https://doi.org/10.1016/j.jpowsour.2013.10.004
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. https://doi.org/10.1149/2.0501802jes
Feng, X., Pan, Y., He, X., Wang, L., & Ouyang, M. (2018). Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm. Journal of Energy Storage, 18, 26–39. https://doi.org/10.1016/j.est.2018.04.020
Goodenough, J. B., & Kim, Y. (2010). Challenges for rechargeable Li batteries. In Chemistry of Materials (Vol. 22, Issue 3, pp. 587–603). https://doi.org/10.1021/cm901452z
Hermann, W. A., & Kohn, S. I. (2013). Detection of over- current shorts in a battery pack using pattern recognition (Patent No. US8618775B2).
Hu, J., Wei, Z., & He, H. (2020). Improved internal short circuit detection method for Lithium-Ion battery with self-diagnosis characteristic. IECON Proceedings (Industrial Electronics Conference), 2020-October, 3741–3746. https://doi.org/10.1109/IECON43393.2020.9254885
Ikeuchi, A., Majima, Y., Nakano, I., & KASAl, K. (2014). Circuit and method for determining internal short- circuit, battery pack, and portable device (Patent No. US20140184235A1).
Keates, A. W., Otani, N., Nguyen, D. J., Matsumura, N., & Li, P. T. (2010). Short circuit detection for batteries (Patent No. US7795843B2).
Kim, G.-H., Smith, K., Ireland, J., & Pesaran, A. (2012). Fail- safe design for large capacity lithium-ion battery systems. Journal of Power Sources, 210, 243–253. https://doi.org/10.1016/j.jpowsour.2012.03.015
Kosky, P., Balmer, R., Keat, W., & Wise, G. (2013). Mechanical Engineering. In Exploring Engineering (pp. 259–281). Elsevier. https://doi.org/10.1016/B978- 0-12-415891-7.00012-1
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. In Energy Storage Materials (Vol. 35, pp. 470–499). Elsevier B.V. https://doi.org/10.1016/j.ensm.2020.11.026
Meng, J., Boukhnifer, M., & Diallo, D. (2019). A Comparative Study of Open-Circuit-Voltage Estimation Algorithms for Lithium-Ion Batteries in Battery Management Systems; A Comparative Study of Open-Circuit-Voltage Estimation Algorithms for Lithium-Ion Batteries in Battery Management Systems. https://doi.org/10.0/Linux-x86_64
Plett, G. L. (2015). Battery Management Systems Volume I Battery Modeling.
Rao, L., & Newman, J. (1997). Heat-generation rate and general energy balance for insertion battery systems. Journal of the Electrochemical Society, 144(8), 2697– 2704. https://doi.org/10.1149/1.1837884
Safdari, M., Ahmadi, R., & Sadeghzadeh, S. (2022). Numerical and experimental investigation on electric vehicles battery thermal management under New European Driving Cycle. Applied Energy, 315. https://doi.org/10.1016/j.apenergy.2022.119026
SPECIFICATION OF PRODUCT (Tentative) for Lithium- ion Rechargeable Cell Energy Business Division. (2008).
Tian, Y., Xia, B., Wang, M., Sun, W., & Xu, Z. (2014). Comparison study on two model-based adaptive algorithms for SOC estimation of lithium-ion batteries in electric vehicles. Energies, 7(12), 8446–8464. https://doi.org/10.3390/en7128446
Xiong, R., Li, L., & Tian, J. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. In Journal of Power Sources (Vol. 405, pp. 18–29). Elsevier B.V. https://doi.org/10.1016/j.jpowsour.2018.10.019
Yang, R., Xiong, R., & Shen, W. (2022). On-board diagnosis of soft short circuit fault in lithium-ion battery packs for electric vehicles using an extended Kalman filter. CSEE Journal of Power and Energy Systems, 8(1), 258–270. https://doi.org/10.17775/CSEEJPES.2020.03260
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. In Renewable and Sustainable Energy Reviews (Vol. 141). Elsevier Ltd. https://doi.org/10.1016/j.rser.2021.110790
Zhang, X., Klein, R., Subbaraman, A., Chumakov, S., Li, X., Christensen, J., Linder, C., & Kim, S. U. (2019). Evaluation of convective heat transfer coefficient and specific heat capacity of a lithium-ion battery using infrared camera and lumped capacitance method. Journal of Power Sources, 412, 552–558. https://doi.org/10.1016/j.jpowsour.2018.11.064
Zhao, W., Luo, G., & Wang, C.-Y. (2015). Modeling nail penetration process in large-format li-ion cells. Journal of the Electrochemical Society, 162(1), A207–A217. https://doi.org/10.1149/2.1071501jes
Asakura, J., Nakashima, T., Nakatsuji, T., & Fujikawa, M. (2012). Battery internal short-circuit detection apparatus and method, and battery pack (Patent No. US8334699B2).
Bernardi, D., Pawlikowski, E., & Newman, J. (1985). A General Energy Balance For Battery Systems. Journal of the Electrochemical Society, 132(1), 5–12. https://doi.org/10.1149/1.2113792
Bizeray, A. M., Zhao, S., Duncan, S. R., & Howey, D. A. (2015). Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter. Journal of Power Sources, 296, 400–412. https://doi.org/10.1016/j.jpowsour.2015.07.019
Comparison Chart - Orion BMS. (n.d.). Https://Www.Orionbms.Com/Comparison/.
Fang, W., Ramadass, P., & Zhang, Z. (2014). Study of internal short in a Li-ion cell-II. Numerical investigation using a 3D electrochemical-thermal model. Journal of Power Sources, 248, 1090–1098. https://doi.org/10.1016/j.jpowsour.2013.10.004
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. https://doi.org/10.1149/2.0501802jes
Feng, X., Pan, Y., He, X., Wang, L., & Ouyang, M. (2018). Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm. Journal of Energy Storage, 18, 26–39. https://doi.org/10.1016/j.est.2018.04.020
Goodenough, J. B., & Kim, Y. (2010). Challenges for rechargeable Li batteries. In Chemistry of Materials (Vol. 22, Issue 3, pp. 587–603). https://doi.org/10.1021/cm901452z
Hermann, W. A., & Kohn, S. I. (2013). Detection of over- current shorts in a battery pack using pattern recognition (Patent No. US8618775B2).
Hu, J., Wei, Z., & He, H. (2020). Improved internal short circuit detection method for Lithium-Ion battery with self-diagnosis characteristic. IECON Proceedings (Industrial Electronics Conference), 2020-October, 3741–3746. https://doi.org/10.1109/IECON43393.2020.9254885
Ikeuchi, A., Majima, Y., Nakano, I., & KASAl, K. (2014). Circuit and method for determining internal short- circuit, battery pack, and portable device (Patent No. US20140184235A1).
Keates, A. W., Otani, N., Nguyen, D. J., Matsumura, N., & Li, P. T. (2010). Short circuit detection for batteries (Patent No. US7795843B2).
Kim, G.-H., Smith, K., Ireland, J., & Pesaran, A. (2012). Fail- safe design for large capacity lithium-ion battery systems. Journal of Power Sources, 210, 243–253. https://doi.org/10.1016/j.jpowsour.2012.03.015
Kosky, P., Balmer, R., Keat, W., & Wise, G. (2013). Mechanical Engineering. In Exploring Engineering (pp. 259–281). Elsevier. https://doi.org/10.1016/B978- 0-12-415891-7.00012-1
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. In Energy Storage Materials (Vol. 35, pp. 470–499). Elsevier B.V. https://doi.org/10.1016/j.ensm.2020.11.026
Meng, J., Boukhnifer, M., & Diallo, D. (2019). A Comparative Study of Open-Circuit-Voltage Estimation Algorithms for Lithium-Ion Batteries in Battery Management Systems; A Comparative Study of Open-Circuit-Voltage Estimation Algorithms for Lithium-Ion Batteries in Battery Management Systems. https://doi.org/10.0/Linux-x86_64
Plett, G. L. (2015). Battery Management Systems Volume I Battery Modeling.
Rao, L., & Newman, J. (1997). Heat-generation rate and general energy balance for insertion battery systems. Journal of the Electrochemical Society, 144(8), 2697– 2704. https://doi.org/10.1149/1.1837884
Safdari, M., Ahmadi, R., & Sadeghzadeh, S. (2022). Numerical and experimental investigation on electric vehicles battery thermal management under New European Driving Cycle. Applied Energy, 315. https://doi.org/10.1016/j.apenergy.2022.119026
SPECIFICATION OF PRODUCT (Tentative) for Lithium- ion Rechargeable Cell Energy Business Division. (2008).
Tian, Y., Xia, B., Wang, M., Sun, W., & Xu, Z. (2014). Comparison study on two model-based adaptive algorithms for SOC estimation of lithium-ion batteries in electric vehicles. Energies, 7(12), 8446–8464. https://doi.org/10.3390/en7128446
Xiong, R., Li, L., & Tian, J. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. In Journal of Power Sources (Vol. 405, pp. 18–29). Elsevier B.V. https://doi.org/10.1016/j.jpowsour.2018.10.019
Yang, R., Xiong, R., & Shen, W. (2022). On-board diagnosis of soft short circuit fault in lithium-ion battery packs for electric vehicles using an extended Kalman filter. CSEE Journal of Power and Energy Systems, 8(1), 258–270. https://doi.org/10.17775/CSEEJPES.2020.03260
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. In Renewable and Sustainable Energy Reviews (Vol. 141). Elsevier Ltd. https://doi.org/10.1016/j.rser.2021.110790
Zhang, X., Klein, R., Subbaraman, A., Chumakov, S., Li, X., Christensen, J., Linder, C., & Kim, S. U. (2019). Evaluation of convective heat transfer coefficient and specific heat capacity of a lithium-ion battery using infrared camera and lumped capacitance method. Journal of Power Sources, 412, 552–558. https://doi.org/10.1016/j.jpowsour.2018.11.064
Zhao, W., Luo, G., & Wang, C.-Y. (2015). Modeling nail penetration process in large-format li-ion cells. Journal of the Electrochemical Society, 162(1), A207–A217. https://doi.org/10.1149/2.1071501jes
Section
Regular Session Papers
This work is licensed under a Creative Commons Attribution 3.0 Unported License.