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Using Fuzzy Preference Orderings in θ-Dominance with Application to Health Monitoring of Li-Ion Batteries.

, , , , and . J. Multiple Valued Log. Soft Comput., 32 (3-4): 343-367 (2019)

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Informed Weak Supervision for Battery Deterioration Level Labeling., , , and . IPMU (2), volume 1602 of Communications in Computer and Information Science, page 748-760. Springer, (2022)Online Estimation of the State of Health of a Rechargeable Battery Through Distal Learning of a Fuzzy Model., , , , and . SOCO, volume 950 of Advances in Intelligent Systems and Computing, page 68-77. Springer, (2019)Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks., , , and . Sensors, 18 (1): 9 (2018)Multiphase resonant converter with output current multiplier for battery charger applications., , , , and . IECON, page 5639-5644. IEEE, (2016)A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems., , , , and . J. Sensors, (2017)Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment., , , , and . Array, (December 2023)Using Fuzzy Preference Orderings in θ-Dominance with Application to Health Monitoring of Li-Ion Batteries., , , , and . J. Multiple Valued Log. Soft Comput., 32 (3-4): 343-367 (2019)Health assessment of LFP automotive batteries using a fractional-order neural network., , , and . Neurocomputing, (2020)Distal learning of the incremental capacity curve of a LiFePO4 battery., , , , , and . Log. J. IGPL, 30 (2): 301-313 (2022)Health Assessment of Automotive Batteries Through Computational Intelligence-Based Soft Sensors: An Empirical Study., , , and . SOCO-CISIS-ICEUTE, volume 649 of Advances in Intelligent Systems and Computing, page 47-56. Springer, (2017)