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- W4285222037 abstract "Precise gauging of the State of Charge (SoC) and State of Health of Lithium-Ion batteries is very basic for the ideal working of Battery Management Systems (BMS) locally available electric vehicles particularly with the end goal of range estimation, determination of time required for complete battery drain and Remaining Useful Life (RUL) forecasting. Current techniques for the assessment of SoC and SoH, for example, Coulomb Counting or Kalman Filtering either have low precision as on account of the former lacking accuracy and precision and the latter lacking adaptability and versatility. This paper presents an elective way to precisely gauge and anticipate the SoC and RUL of lithium-particle batteries utilizing Artificial Neural Networks to guarantee both exactness and versatility for ideal performance of Battery Management Systems (BMS)." @default.
- W4285222037 created "2022-07-14" @default.
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- W4285222037 date "2022-01-01" @default.
- W4285222037 modified "2023-09-30" @default.
- W4285222037 title "Data-Driven Prediction of State of Charge and Remaining Useful Life of Lithium-Ion Batteries Using Neural Networks" @default.
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- W4285222037 doi "https://doi.org/10.1007/978-981-19-0193-5_47" @default.
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