fig5

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Figure 5. Sequential approaches for estimating SOC of batteries. Key strategies include RNN-based modeling (LSTM, GRU, BiLSTM, BiGRU), input variable optimization (battery stress, average voltage, physical variables), feature extraction (random forest, PCA), and model optimization (noise immunity, Encoder-Decoder structures, transfer learning). SOC: State of charge; LSTM: long short-term memory; GRU: gated recurrent unit; BiLSTM: bidirectional long short-term memory networks; PCA: pearson correlation; RNN: recurrent neural network.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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