Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction
Abstract
Smart Electricity Meters (SEMs) are widely used in distributed generation system, and over 67% of its failure are caused by battery low-voltage. Therefore, it is necessary to study the degradation of battery voltage. This work explores the degradation mechanism of lithium battery and proposed to use voltage as degradation index to estimate the health status of the system. Four groups of batteries of the same type and batch are used for the test. The purpose is to use multiple sets of data to train the model parameters and enhance the robustness of the model. The Particle Filtering (PF) based approach is used in this study to estimate the degradation state such that the Remaining Useful Life (RUL) can be predicted. An accurate prediction can provide the proper maintenance/replacement schedule for the SEMs before the failure occurs.
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References
Abdel-Monem, M., Hegazy, O., Omar, N., Trad, K., Bossche, P., Van Den, and Mierlo, J. Van. 2017. Lithium-ion batteries: Comprehensive technical analysis of second-life batteries for smart grid applications. 2017 19th European Conference on Power Electronics and Applications, EPE 2017 ECCE Europe, 2017-Janua, 1–16.
Al-Dahidi, S., Di Maio, F., Baraldi, P., and Zio, E., 2017. A switching ensemble approach for remaining useful life estimation of electrolytic capacitors. In L. Walls, M. Revie, & T. Bedford (Eds.), Risk, Reliability and Safety: Innovating Theory and Practice, 2000–2005.
An, D., Choi, J. H., and Kim, N. H., 2013. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliability Engineering and System Safety, 115, 161–169.
Avadhanula, A. K. and Kulkarni, S. S., 2020. Comparative Study of Mathematical Models and Data Driven Models for Battery Performance Parameter Estimation. In India, 2020 Third International Conference on Advances in Electronics, Computers and Communications, (unpublished).
Dong, G., Han, W., and Wang, YJIToIE, 2020. Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles, pp. 99.
Duan, B., Zhang, Q., Geng, F., and Zhang, C., 2020. Remaining useful life prediction of lithium-ion battery based on extended Kalman particle filter. International Journal of Energy Research, 44(3), 1724–1734.
El Mejdoubi, A., Chaoui, H., Gualous, H., Van Den Bossche, P., Omar, N., and Van Mierlo, J., 2019. Lithium-ion batteries health prognosis considering aging conditions. IEEE Transactions on Power Electronics, 34(7), 6834–6844.
He, C., Liu, C., Wu, Y., and Wu, T., 2018. Estimation for SOC of Electric Vehical Lithium Battery Based on Artificial Immune Particle Filter. In China, 2018 3rd International Conference on Smart City and Systems Engineering, ICSCSE 2018, 675–678.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., and Zerhouni, N., 2016. Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing, 72–73, 2–31.
Kuriqi, A., Pinheiro, A. N., Sordoward, A., and Garrote, L., 2019. Influence of hydrologically based environmental flow methods on flow alteration and energy production in a run-of-river hydropower plant. Journal of Cleaner Production, 232, 1028–1042.
Levieux, L. I., Inthamoussou, F. A., and De Battista, H., 2019. Power dispatch assessment of a wind farm and a hydropower plant: A case study in Argentina. Energy Conversion and Management, 180, 391–400.
Ma, Y., Chen, Y., Zhang, F., and Chen, H., 2019. Remaining Useful Life Prediction of Power Battery Based on Extend H8 Particle Filter Algorithm. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 55(20), 36–43.
Muhammad, M., Ahmeid, M., Attidekou, P. S., Milojevic, Z., Lambert, S., and Das, P., 2019. Assessment of spent EV batteries for second-life application. In Singapore, 2019 IEEE 4th International Future Energy Electronics Conference, IFEEC 2019.
Omariba, Z. B., Zhang, L., and Sun, D., 2018. Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Battery Based on Particle Filter. In China, 2018 IEEE 3rd International Conference on Big Data Analysis, 412–416.
Song, Y., Liu, D., Hou, Y., Yu, J., and Peng, Y., 2018. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm. Chinese Journal of Aeronautics, 31(1), 31–40.
Weddington, J., Niu, G., Chen, R., Yan, W., and Zhang, B. J. N., 2021. Lithium-ion Battery Diagnostics and Prognostics Enhanced with Dempster-Shafer Decision Fusion, 458.
Xie, G., Li, X., Zhang, C. L., Hei, X. H., Qian, F. C., Hu, S. L., Cao, Y., and Cai, B. G., 2017. Data-Driven Approach for the Prediction of Remaining Useful We. 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE) (Xian, PEOPLES R CHINA), pp. 150–155.
Xiong, R., Zhang, Y., He, H., Zhou, X., and Pecht, M. G., 2017. A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries. IEEE Transactions on Industrial Electronics, 65(2), 1526–1538.
Zhang, D., Baraldi, P., Cadet, C., Yousfi-Steiner, N., Bérenguer, C., and Zio, E., 2019. An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells. Mechanical Systems and Signal Processing, 124, 479–501.