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Secure Determinant Codes for Distributed Storage Systems
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AI
DOI：10.1109/TIE.2022.3187596.png)
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摘要
En 中文
Complex operating conditions of lithium-ion batteries in practical applications bring challenges to accurate diagnosis of battery aging. This article proposes a practical method to estimate both battery capacity and electrode aging through an open-circuit voltage (OCV) reconstruction process. First, the recursive least square algorithm is implemented in a real battery management system (BMS) to identify OCV in real-time. Then, an electrode OCV model is employed to reconstruct the OCV curve based on the raw identified data from BMS. Thus, the parameters of the electrode OCV model are obtained, with which the battery capacity and electrode aging can be determined. During OCV reconstruction, a two-step estimation method with weighted objective functions is utilized to address the impact of fragmented and poorly identified OCV in BMS. Meanwhile, the Peukert coefficient is employed to improve the adaptability to different temperatures. The proposed method is validated with four aged batteries at different temperatures of 5 degrees C, 25 degrees C, and 45 degrees C, respectively. Experimental results indicate that the proposed method can achieve accurate estimations of battery capacity and electrode aging state at various temperatures.
Keyword:
Batteries
Aging
Electrodes
Estimation
Degradation
Temperature measurement
Discharges (electric)
Capacity estimation
electrode aging
lithium-ion battery
open-circuit voltage (OCV) reconstruction
期刊
IF:
7.2
论文数:
1.8W
被引数:
9.8W
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