Battery State of Charge Estimation Based on Improved Neural Network
DOI:
https://doi.org/10.54097/9ervp336Keywords:
State of charge, Aquila Optimization, Deep learningAbstract
Accurate prediction of battery state of charge (SOC) is vital for extending battery lifespan and ensuring operational safety in electric vehicles. However, traditional approaches exhibit constrained predictive accuracy and robustness under complex environments and variable operating conditions. Therefore, this study proposes the AO-BiGRU-CAM model, integrating Aquila Optimization (AO), a bidirectional Gated Recurrent Units (BiGRU), and a channel attention mechanism (CAM). It refines parameter search while enhancing focus on salient features to improve training efficiency and predictive accuracy. Under diverse temperatures and drive cycles, AO-BiGRU-CAM reduces errors by approximately 5%–10% in MSE, RMSLE, and MAPE compared with other models, demonstrating robust adaptability.
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