Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model

doi:10.11930/j.issn.1004-9649.202208122

Abstract

Abstract: The remaining useful life (RUL) prediction and state of health (SOH) estimation of lithium-ion batteries are essential for energy storage safety. The important indicators often focus on battery capacity and internal resistance. However, in practical operation, the capacity measurement requires a complete charge/discharge cycle, and the measurement of internal resistance requires additional equipment. To this end, based on the voltage segment in the constant current discharge condition of the lithium-ion battery, the sharp voltage drop of the initial discharge segment is taken as a new healthy factor. Confronted with the possibility that data of the new healthy factor is polluted by noise, this paper rebuilds the factor data to reduce noise through a multi-order Bezier curve. Then an empirical degradation hybrid model is built with the number of cycles. On this basis, the SOH of the battery is defined by voltage segment, and a new SOH estimation model is proposed. The feasibility and effectiveness of the proposed degradation model and estimation model are verified by the aging data published by NASA and the experimental platform.

Key words: lithium-ion battery, remaining useful life, state of health, voltage segment data, hybrid model

YUE Jiahui, XIA Xiangyang, JIANG Daiyu, ZHOU Guandong, XU Zhiqiang, ZHANG Yuan, LV Chonggeng. Remaining Useful Life Prediction and State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model[J]. Electric Power, 2023, 56(7): 163-174.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202208122

https://www.electricpower.com.cn/EN/Y2023/V56/I7/163

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