Abstract
The cascade utilization of retired lithium batteries to build an energy storage system is an effective means to achieve my country's dual-carbon goal, but safety issues restrict large-scale promotion and application. Accurately assessing the operational risk of cascade batteries in an energy storage system can ensure the safe operation of the system. This paper defines the risk of retired power batteries in the energy storage system, and establishes the risk with the remaining useful life (RUL), state of charge (SOC)and temperature rise rate of the echelon battery as the evaluation factors. Evaluate the model. In this paper, the BP (back propagation) neural network algorithm is used to estimate the RUL of the echelon battery, and the nonlinear model of the echelon battery is used to estimate the SOC and the temperature rise rate. Combined with the AHP (analytic hierarchy process) method and K-means mean aggregation. The class method estimates the subjective and objective weights of the evaluation indicators. This method can complete the risk assessment and determine the warning threshold value, and finally realize the real-time operation risk estimation during the operation of the echelon battery. The calculation example shows that the method can realize the operation risk assessment of the cascade battery energy storage system, improve the safety of the system, and promote the large-scale popularization and application of the cascade battery energy storage system.
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References
Li, J., Li, Y., Lv, C., et al.: Key technology and research status of cascaded utilization in decommissioned power battery. Autom. Electric Power Syst. 44(13), 172–183 (2022)
Huang, J., Li, J., Li, Z., et al.: A state of health rapid assessment method for decommissioned lithium-ion batteries. Power Syst. Protect. Control 49(12), 25–32 (2022)
Li, J., Li, Y., Lv, C., et al.: Key technology of retired batteries’ screening and clustering under target of carbon neutrality. Power Syst. Technol. 46(2), 429–441 (2022)
Yu, L., Zhang, H., Tian, P., et al.: A battery safety evaluation method for reuse of retired power battery in energy storage system. Acta Energiae Solaris Sinica 43(5), 447–453 (2022)
Yu, L., Zhang, H., Tian, P., et al.: Multi-level on-line safety assessment of reconfigurable energy storage system using secondary batteries risk warning postitioning method. Acta Energiae Solaris Sinica 43(5), 462–467 (2022)
Ning, Y., Zuo, X., Wei, L., et al.: Economic dispatch application of power system with energy storage systems. IEEE Trans. Appl. Supercond. 26(7), 1–5 (2016)
Wang, P., Fan, L., Cheng, Z.: A joint state of health and remaining useful life estimation approach for lithium-ion batteries based on health factor parameter. Proc. CSEE 42(4), 1523–1533 (2022)
Chen, Y., James, W.: Heat transfer phenomena in lithium/polymer-electrolyte batteries for electric vehicle application. J. Electrochem. SOC 140(7), 1833–1838 (1993)
Ci, S., Lin, N., Wu, D.: Reconfigurable battery techniques and systems: a survey. IEEE Access 4, 1175–1189 (2016)
Ci, S., Zhou, Y., Wang, H., et al.: Modeling and operation control of digital energy storage system based on reconfigurable battery network—Base station energy storage application. J. Glob. Energy Interconnect. 4(5), 427–435 (2021)
Liu, D., Zhou, J., Liao, H., et al.: A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics. IEEE Trans. Syst. Man Cybern.: Syst. 45(6), 915–928 (2015)
Liu, Y., Li, S., Zhang, L., et al.: Characteristics and application pospects of second use batteries for energy storage. Sci. Technol. Manag. Res. 37(1), 59–65 (2017)
Feng, X.: Thermal runaway initiation and propagation of lithium-ion traction battery for electric vehicle: test, modeling and perception. Tsinghua university (2016)
Wu, Y., Tian, P., Xiao, X., et al.: Security risk assessment of reconfigurable secondary battery energy storage system based on precursor information. Acta Energiae Solaris Sinica 43(4), 36–45 (2022)
Wang, R., Hou, Q., Shi, R.: Remaining useful life prediction method of lithium battery based on variational mode decomposition and integrated deep mode. Chin. J. Sci. Instrum. 42(4), 111–120 (2021)
Zhang, J., Wang, P., Cheng, Z.: A joint estimation framework of SOC-SOH-RUL for lithium batteries based on charging voltage segment and hybrid method. 46(3), 1063–1072 (2022)
Li, J., Li, Y., Huang, B.: Research on consistency evaluation and control strategy of a retired power battery. Power Syst. Protect. Control 49(12), 1–7 (2021)
Li, J., Li, Y., Chen, G.: Research on feature extraction and SOH evaluation methods for retired power battery. Proc. CSEE 42(4), 1332–1347 (2022)
Hornk, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)
Xiao, X., Tian, P., Yu, L., et al.: Status and prospect of safety studies of cascade power battery energy storage system. J. Electr. Eng. 1, 208–224 (2022)
Acknowledgments
This work was supported by the S&T Major Project of Inner Mongolia Autonomous Region in China (2020ZD0018).
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Cao, Y., Wu, Y., Tian, P., Xiao, X., Yu, L. (2023). Risk Assessment of Retired Power Battery Energy Storage System. In: Sun, F., Yang, Q., Dahlquist, E., Xiong, R. (eds) The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022). ICEIV 2022. Lecture Notes in Electrical Engineering, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-99-1027-4_74
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DOI: https://doi.org/10.1007/978-981-99-1027-4_74
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