Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD

作者：Zihan Li, Li Ai, Fang Bai, Hongfu Zuo, Ying Zhang · 发表于：Measurement Science and Technology · 年份：2023 · DOI：10.1088/1361-6501/ad006d · 被引用次数：26 · 研究领域：Advanced Battery Technologies Research、Reliability and Maintenance Optimization、Advancements in Battery Materials

Abstract Predicting the remaining useful life (RUL) of lithium batteries is crucial for predicting battery failure and health management. Accurately estimating the RUL allows for timely maintenance and replacement of batteries that pose safety risks. To enhance the safety and reliability of lithium battery operations, this paper proposes a lithium battery life prediction model, attention mechanism-convolutional neural network (ACNN)-Mogrifier long and short-term memory network (LSTM)-maximum mean discrepancy (MMD), based on ACNN, Mogrifier LSTM, and MMD Feature Transfer Learning. Firstly, the capacity degradation data from historical life experiments of lithium batteries in both source and target domains are extracted. The whale optimization algorithm (WOA) is employed to optimize the parameters of variational modal decomposition, enabling the decomposition of the historical capacity degradation data into multiple intrinsic mode functions (IMFs) components. Secondly, highly correlated IMF components are identified using the Pearson correlation coefficient (Pearson) to reconstruct the capacity sequence, which characterizes the capacity degradation information of the lithium batteries. These reconstructed sequences are inputs to the ACNN model to extract features from the capacity degradation data. The extracted features are then utilized to compute MMD values, quantifying the distribution differences between the two domains. The Mogrifier LSTM neural network estimates the capa...