Enhanced Sample Entropy-based Health Management of Li-ion Battery for Electrified Vehicles

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Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, methodology): “As an extension of [43], the primary objective of this paper is to explore the improvement potential of the sample entropybased SOH gauge through advanced machine-learning tools.”
Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis (2018, methodology): “For the black box model-based techniques, artificial neural networks [18,19], support vector machine [20,21], sample entropy [22,23] and sparse bayesian modeling [24] are generally employed to estimate the capacity fade in batteries.”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[89] demonstrated that the sample entropy taken from the hybrid pulse power characterization data can be correlated with cell capacity loss and therefore used for online SOH estimation.”