An adaptive wavelet and CNN-LSTM hybrid neural network improved battery state of charge estimation accuracy under complex noise conditions, achieving R2 values above 0.95.
The proposed hybrid deep learning model effectively suppresses noise and accurately estimates lithium-ion battery state of charge.
Estimación del efecto: R2 > 0.95
Accurate state of charge (SOC) estimation is essential for lithium‐ion battery management systems. However, the accuracy of SOC estimation can be significantly degraded by unknown noise under complex operating conditions. According to this issue, an SOC estimation method that integrates an adaptive wavelet and a convolutional neural network (CNN)–long short‐term memory (LSTM) hybrid neural network is proposed. First, we have designed a locally adaptive signal‐to‐noise ratio‐based dynamic wavelet threshold algorithm. By optimizing the decomposition level and adopting an improved semi‐soft threshold function, this algorithm can effectively suppress noise while preserving the signal features. Furthermore, a CNN–LSTM hybrid model is constructed. The CNN is used to extract the local features of the battery data, and the LSTM is employed to model the long‐term dependencies of the battery data, thereby enhancing the model's robustness against noise. The experimental results show that this method can suppress the influence of various types of noise in multiple working conditions. The errors in this method are lower than those of traditional methods, and the R2 values are all above 0.95, indicating a high degree of fitting. This study provides a reliable solution for estimating the battery's SOC in complex noise environments.
Liu et al. (Wed,) conducted a other in Lithium-ion battery state of charge estimation. Adaptive wavelet and CNN-LSTM hybrid neural network vs. Traditional methods was evaluated on State of charge (SOC) estimation accuracy (R2 > 0.95). An adaptive wavelet and CNN-LSTM hybrid neural network improved battery state of charge estimation accuracy under complex noise conditions, achieving R2 values above 0.95.