A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

/pdf/a-review-on-second-life-of-li-ion-batteries-prospects-1dlculjn.pdf

A review on second-life of Li-ion batteries: prospects, challenges, and issues

This study proposes an end-to-end prognostic framework for state-of-health (SOH) estimation and remaining useful life (RUL) prediction. In such a framework, a hybrid neural network (NN), i.e., the concatenation of one-dimensional convolutional NN and active-state-tracking long–short-term memory NN, is designed to capture the hierarchical features between several variables affecting battery degeneration, as well as the temporal dependencies embedded in those features. The prior distribution over hyperparameters, specified to the popular NNs applied in SOH or RUL tasks, is built through the Kolmogorov–Smirnov test. Such prior distribution is regarded as a surrogate to investigate the degeneration data’s impact on modeling such NNs. Based on such a surrogate, a Bayesian optimization algorithm is proposed to build SOH and RUL models, selecting the most promising configuration automatically in the sequential evolution progress of hyperparameters. Compared with the existing NNs, the experiments indicate that our method hits a lower average RMSE 0.0072 and global average RMSE 0.0269 for SOH and RUL tasks. Code and models are available at https://github.com/Lipenghua-CQ/CNN-ASTLSTM. 

Ming-Feng Ge

Papers

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries