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Analyzing the Performance of AI-Based Battery SoC Estimatio..:
, In:
2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
,
Negri, Virginia
;
Mingotti, Alessandro
;
Tinarelli, Roberto
... - p. 1-5 , 2024
Link:
https://doi.org/10.1109/I2MTC60896.2024.10560993
RT T1 <cTitle>2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)</cTitle>: T1 <Title>Analyzing the Performance of AI-Based Battery SoC Estimation: A Metrological Point of View</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10560993&Exemplar=1&LAN=DE A1 Negri, Virginia A1 Mingotti, Alessandro A1 Tinarelli, Roberto A1 Peretto, Lorenzo A1 Apa, Ludovica A1 D'Alvia, Livio A1 Del Prete, Zaccaria A1 Rizzuto, Emanuele YR 2024 SN 2642-2077 K1 Maximum likelihood estimation K1 Accuracy K1 Uncertainty K1 Instruments K1 Measurement uncertainty K1 Machine learning K1 Battery charge measurement K1 SoC K1 uncertainty K1 artificial intelligence K1 batteries K1 Monte Carlo SP 1 OP 5 LK http://dx.doi.org/https://doi.org/10.1109/I2MTC60896.2024.10560993 DO https://doi.org/10.1109/I2MTC60896.2024.10560993 SF ELIB - SuUB Bremen
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