Gramian angular field-based state-of-health estimation of lithium-ion batteries using two-dimensional convolutional neural network and bidirectional long short-term memory

Accurate assessment of the state of health (SOH) of Lithium-ion batteries (LIBs) is crucial for ensuring the stability and reliability of associated equipment. However, in practical applications, current feature extraction techniques face challenges due to process complexity and the difficulty of models in capturing the dynamic evolution of time series data. This study introduces an advanced method for predicting LIBs SOH by integrating two-dimensional (2D) convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with the Gramian angular field (GAF) technique. Health indicators (HIs) are derived from the incremental capacity and charging voltage curves, which are transformed into two-dimensional images using GAF. Bayesian optimization determines the optimal hyperparameters for the model, which uses image data of two health factors as inputs. The results demonstrate that the proposed dual-channel(CH2) image data input model, combined with voltage data during the charging phase, achieves superior performance, with lower errors and higher accuracy, evidenced by an average root mean square error (RMSE) of 0.0112 and an average mean absolute error (MAE) of 0.0087. The model's generalization capability and comparative analysis with existing methodologies affirm its practical significance.