Time series forecasting of battery state of charge using real-world driving data: an SCSSA optimized CNN-LSTM-Attention model
TL;DR
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 mechanism for adaptive feature weighting.
Abstract
High-precision state-of-charge (SOC) prediction is critical for electric vehicle (EV) safety and performance. To address the high computational complexity of existing data-driven methods, which rely on long historical sequences, this paper proposes a purely data-driven end-to-end SOC prediction framework based on sliding-window technology. The framework adopts an SCSSA-optimized convolutional neural networks (CNN)-long short-term memory (LSTM)-attention hybrid model that integrates a CNN for local feature extraction, an LSTM for modeling temporal dependencies, and an attention mechanism for adaptive feature weighting, with an improved sparrow search algorithm for global hyper-parameter optimization. Experiments are conducted using 29 months of operational data from 20 EVs. Results show that the proposed method achieves 14.8%, 8.9%, and 17.6% improvements in mean absolute error, root mean square error, and mean absolute percentage error, respectively, compared with the best benchmark model, with R2 consistently above 0.96. The method demonstrates excellent robustness across seasonal variations and diverse charging patterns, laying a solid technical foundation for SOC prediction in battery management systems.