Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

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Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “The empirical model of the capacity degradation is fitted by the RVM for capacity estimation [85].”
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error (2014, neutral): “, can be found in [19–25].”
Gaussian process regression for forecasting battery state of health (2017, methodology): “cycle data extracted from [13] (see Appendix A, and Fig.”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[144] used RVM to derive the relevance vectors to represent the battery capacity fade, and predict capacity degradation values for the future cycles of relevance vectors.”
Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, background): “[27] obtained the relevance vectors from the degradation of battery capacity and the number of cycles.”