Time series forecasting of battery state of charge using real-world driving data: an SCSSA optimized CNN-LSTM-Attention model
A purely data-driven end-to-end SOC prediction framework based on sliding-window technology that adopts an SCSSA-optimized convolutional neural networks-long short-term memory-attention hybrid model that integrates a CNN for local feature extraction, an LSTM for modeling temporal dependencies, and an attention mechanis...