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摘要: This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 degrees C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.
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| 图1 Battery management system.
| 图2 RUL prediction results based on the moving-window method of cells tested at: (a) 1C and 25 °C; (b) 2C and 25 °C; (c) 1C and 40 °C; and (d) 2C and 40 °C.
| 引文信息: Xiong R , Zhang Y , Wang J , et al. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5):4110-4121.(下载链接) | 其他相关论文: 1. Rui, Xiong, et al. Towards a smarter battery management system: A critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018.(下载链接)
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