基于CPO-MSC-BiLSTM-Attention模型的单特征锂电池SOH估计研究OA

A state of health(SOH)estimation method using only depth of discharge(DOD)as the input feature is proposed to address the insufficient accuracy of lithium-ion batteries SOH estimation under low-dimensional feature conditions.The crown porcupine optimization(CPO)algorithm is employed for global parameter optimization to improve prediction accuracy and stability.The model introduces multi-scale convolution(MSC)to extract multi-time-scale features,and bidirectional long short-term memory(BiLSTM)networks are used to enhance temporal modeling capability.An attention mechanism is employed for adaptive feature weighting.In addition,a Mixstyle layer is incorporated after the convolution layers to improve robustness under different operating conditions and data distributions.Experimental results show that the proposed model achieves high-accuracy SOH estimation using only the single DOD feature and outperforms comparative methods in generalization performance and robustness.This method provides an effective approach for battery SOH estimation under low-dimensional feature conditions.