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• W3081632319 abstract "In this article, we propose a method for extracting, modeling, and predicting the resistance of Lithium-ion batteries directly from the battery dynamic mission profile. While the extraction of the mainly relied on data manipulation and bookkeeping, the modeling and subsequent prediction of the resistance used a log-linear model. It is shown that the estimated log-linear model can be used to create a posterior probability distribution of the age of the battery, given an internal resistance measurement and the state-of-charge (SOC) value at which it was measured. This distribution was used calculate the expected age of the battery, and the expected age was compared to weekly check-ups. At an SOC of 80% a mean absolute error (MAE), between the weekly check-ups and the expected age, of 5.83 weeks [706 full equivalent cycles (FEC)] was achieved. Furthermore, it is shown that by introducing a decision threshold the MAE could be reduced as far as 2.65 weeks (321 FEC). Finally, a method is introduced for handling cases where the SOC was not known exactly." @default.
• W3081632319 created "2020-09-08" @default.
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• W3081632319 date "2020-11-01" @default.
• W3081632319 modified "2023-10-06" @default.
• W3081632319 title "Log-Linear Model for Predicting the Lithium-ion Battery Age Based on Resistance Extraction from Dynamic Aging Profiles" @default.
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• W3081632319 doi "https://doi.org/10.1109/tia.2020.3020529" @default.
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