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Improved modeling of lithium-ion battery capacity degradation using an individual-state training method and recurrent softplus neural network
2020
IEEE Access
An individual-state training method using multiple battery cycling data and initial state training for individual batteries is proposed in this study. The training method is based on the Broyden-Fletcher-Goldfarb-Shanno quasi-Newton method, and is modified to adapt to different battery samples by training the initial states for individual batteries to improve the modeling precision. This is equivalent to training a different model for each battery with shared model parameters, which improves
doi:10.1109/access.2020.3048146
fatcat:xik4hq6itfcqres6noffslseeu