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- W4380229712 abstract "Accurate state of health (SOH) knowledge is critical for reliable operations of lithium-ion batteries. However, short-term random charging operations of lithium-ion batteries are not conducive to reliable SOH estimation. To solve it, a charging voltage prediction and machine learning based estimation are employed to supply precise estimation. Firstly, the correlated feature variables in terms of SOH are determined by analyzing the raw charging voltage distribution. Then, the wide range charging voltage is predicted via the constructed polynomials and short-term measures. Next, the extreme learning machine algorithm is employed to achieve online SOH estimation. Finally, the feasibility of the proposed voltage estimation method is verified at different aging cycles and different charging intervals, and the reliability of SOH estimation is investigated in the full lifetime range and at different state of charge (SOC) charging intervals. The experimental results manifest that the proposed method can supply reliable SOH estimation with the error of less than 2.02% based on only the short-term random charging scenarios, furnishing reliable and safe operation guideline for lithium-ion battery systems." @default.
- W4380229712 created "2023-06-12" @default.
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- W4380229712 date "2023-09-01" @default.
- W4380229712 modified "2023-10-14" @default.
- W4380229712 title "Accurate state of health estimation for lithium-ion batteries under random charging scenarios" @default.
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- W4380229712 doi "https://doi.org/10.1016/j.energy.2023.128092" @default.
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