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

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 capacity values of the source and target domains and calculates the loss functions by comparing them to the actual capacity values. These loss functions, along with the computed MMD values, are combined to obtain the combined loss function of the model. Finally, the ACNN-Mogrifier LSTM-MMD is applied to the target domain data to formulate the lithium battery RUL prediction model. The effectiveness of the proposed method is validated using CACLE and NASA lithium battery datasets, The experimental results demonstrate that the predicted error of the RUL for the B5 battery is less than 6% for mean absolute percentage error (MAPE) and less than 1 for . Similarly, the RUL prediction error for the B6 battery is below 10% for MAPE and less than 1 for . This indicates higher accuracy compared to other prediction methods, along with improved robustness and practicality.