A naive Bayes model for robust remaining useful life prediction of lithium-ion battery

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A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “Alternate applications of machine learning approaches for battery state estimation include particle swarm optimization [46], Gaussian process regression [47], recurrent neural fuzzy systems [48], genetic algorithm [49], sample entropy based approaches [50,51], the naive Bayes model [52,53] and other”
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery (2021, methodology): “[12], autoregressive modeling [13], Bayesian prediction [14]– [16], and Box–Cox transformation [17] have successfully been applied for battery future capacities and RUL prediction.”
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2022, unknown): “For example, to account for the effects of discharge current and ambient temperature, Ng et al. [13] proposed a naive Bayes model to predict the RUL.”
A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “Other data-driven methods, such as Naive Bayes [84], Bayesian learning [83,86], Bayesian Monte Carlo method [87] have also been applied for improved battery SOH estimation.”