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• W2898894272 abstract "The fast diagnosis of micro-short circuit cells is crucial for the safety of battery packs. Based on the difference between the “median cell” and other cells in a battery pack, we propose a method that can identify micro-short circuit cells under dynamic conditions in real time. We model cell differences and analyze the model from the perspective of its low frequency variation characteristics. We find that approximate open-circuit voltage differences can be obtained when terminal voltage differences are passed through low-pass filters. Then approximate electric quantity differences can be obtained by utilizing the open-circuit voltage differences and the data smoothing function of low-pass filters. For onboard applications of diagnosis method, the recursive least square is adopted to estimate micro-short circuit currents and resistances utilizing the change of electric quantity differences. We verify and analyze the feasibility of the diagnosis method by using simulation data when the cells in a battery pack have temperature, state of charge, capacity, and internal resistance inconsistency, respectively. Finally, the effectiveness of the diagnosis method is further verified by the triggering experiments of micro-short circuits for real battery packs." @default.
• W2898894272 created "2018-11-09" @default.
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• W2898894272 date "2019-01-01" @default.
• W2898894272 modified "2023-10-11" @default.
• W2898894272 title "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters" @default.
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• W2898894272 doi "https://doi.org/10.1016/j.energy.2018.10.160" @default.
• W2898894272 hasPublicationYear "2019" @default.
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