An Innovative Framework for Forecasting the State of Health of Lithium-Ion Batteries Based on an Improved Signal Decomposition Method

The declining trend of battery aging has strong nonlinearity and volatility, which poses great challenges to the prediction of battery’s state of health (SOH). In this research, an innovative framework is initially put forward for SOH prediction. First, partial incremental capacity analysis (PICA) is carried out to analyze the performance degradation within a specific voltage range. Subsequently, the height of the peak, the position of the peak, and the area beneath the peak of the IC curves are retrieved and used as health features (HFs). Moreover, improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed and utilized to decompose HFs to reduce the nonlinearity and fluctuations. Additionally, a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) is constructed for the prognosis of these sub-layers. Finally, the effectiveness and robustness of the proposed prognosis framework are validated using two battery datasets. The results of three groups of comparative experiments demonstrate that the maximum root mean squared error (RMSE) and mean absolute error (MAE) values reach merely 0.55% and 0.59%, respectively. This further demonstrates that the proposed FEEMD outperforms other benchmark models and can offer a reliable foundation for the health prognosis of lithium-ion batteries.