Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction

  • Ye Chen 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Ziyi Chen Shenyang Agricultural University, Shenyang 110866, China
  • Mengmeng Zhu 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Yaohua Liao 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Fang Luo 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Xinru Li Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Keywords: Smart electricity meters, lithium battery, remaining useful life, particle filtering

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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Author Biographies

Ye Chen, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Ye Chen works at Electric Power Research Institute of Yunnan Power Grid Co. Ltd, She received master’s degree in Power systems and automation from Kunming University of Science and Technology in 2017, engaged in electric energy, electrical measurement and thermal engineering professional work. Members of the High-precision Electrical Parameter Laboratory, Spark Power Research Studio, Intelligent Perception Innovation Studio and Key Laboratory of CSG for Electric Power Measurement.

Ziyi Chen, Shenyang Agricultural University, Shenyang 110866, China

Ziyi Chen, born in Liaoning Province, China, in 2003. Undergraduate at Shenyang Agricultural University, mechanical design and manufacturing and automation, like electronic design and circuit related knowledge.

Mengmeng Zhu, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Mengmeng Zhu works at Electric Power Research Institute of Yunnan Power Grid Co. Ltd, senior engineer, the research direction is electric energy metering device technology research and power transformer field verification, AC/DC electronic transformer field key test technology application and distribution network fault detection and protection control work.

Yaohua Liao, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Yaohua Liao (1992), male, master, engineer, Yunnan Power Grid Co., Ltd. Electric Power Research Institute, engaged in electric energy, electrical measurement, thermal engineering and high voltage measurement professional work, good at solving measurement-related field problems. Members of the High-precision Electrical Parameter Laboratory, Spark Power Research, and Intelligent Perception Innovation Studio participated in the drafting of the Q/CSG 1209013.2-2019 and Q/CSG 1209013.7-2019 corporate standards.

Fang Luo, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Fang Luo (1995), female, works in Yunnan Yundian Tongfang Technology Co., LTD. She received her bachelor’s degree in Computer Science and Technology from Yunnan Minzu University in 2017. She is engaged in Web and APP front-end development, artificial intelligence consulting design and project management.

Xinru Li, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Xinru Li was born in Shandong, China, in 1997. She is currently pursuing the Master of Academic degree with the Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China. She is presently working in the fields of Fault prediction and health management.

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.

Published
2021-12-08
Section
Renewable Power and Energy Systems