On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[54] IC C/20 Peak position and height NMC Zhang et al.”
State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, background): “SOC is commonly defined as “the percentage of the maximum possible charge that is present inside a rechargeable battery”[24].”
Machine learning pipeline for battery state-of-health estimation (2021, methodology): “Additionally, both curves must cover a sufﬁcient voltage range for the method to work and, for obtaining a high curve ﬁdelity, it is restricted to low charge current rates(1/5 to 1/25 C-rate) [28], [29], [30].”
Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression (2021, unknown): “cycle number of battery aging test [30].”
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017, methodology): “techniques have been conventionally used for detailed cell analyses, such as understanding degradation mechanisms [16], [17]; however, recent studies have considered the use of portions of the IC/DV curve for online capacity estimation [18]–[22].”