基于云平台的深度学习电池参数识别与SOH估计OA

Accurate state of health(SOH)estimation of Li-ion battery is crucial for the safe and reliable operation of battery management system(BMS).Traditional moving window least squares methods suffer from insufficient accuracy and poor adaptability in complex dynamic environments.A deep learning-enhanced method for battery model parameter identification and SOH estimation based on a cloud computing platform is proposed.The method fully preserves the mathematical foundation of the second-order RC equivalent circuit model and integrates a deep learning architecture combining convolutional neural networks,long short-term memory(LSTM)networks and an attention mechanism to construct a cloud-based intelligently optimized parameter identification framework.The proposed method improves SOH prediction accuracy while maintaining the theoretical integrity of the moving window least squares algorithm,reducing the mean absolute percentage error(MAPE)from 1.15％with traditional methods to 0.31％.