Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me

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Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, methodology): “[36] proposed a double-index lithium battery degradation model and used the Dempster–Shafer theory (DST) to initialize the model parameters and the Bayesian Monte Carlo (BMC) method to update the model parameters, which are used to predict the battery RUL.”
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life (2021, methodology): “A double exponential model was proposed to approximate the degraded trajectory of the LIB capacity [13] and initialized model parameters with Dempster–Shafer evidence theory, combining with PF.”
Stochastic modelling and analysis of degradation for highly reliable products (2015, methodology): “Compared with Kalman filters, the use of particle filters is more flexible for any non-linear functions [105,106].”
Gaussian process regression for forecasting battery state of health (2017, methodology): “Batteries 5, 6, 7, and 18 (in the numbering of the online repository) were chosen to be analysed in the present work, since these have the most data-points; and because they have previously been chosen for analysis in earlier works [8,10], and hence the present selection facilitates a comparison wit”