In response to the current issue of low prediction performance in the remaining service life of electric vehicle lithium batteries, a hybrid deep learning model for predicting the remaining service life of electric vehicle lithium batteries is proposed. The model employs Empirical Mode Decomposition (EMD) to decompose battery data, forming high-frequency and low-frequency components of the battery capacity sequence. It utilizes Multilayer Long Short-Term Memory (MLSTM) and Elman neural networks to learn high-frequency and low-frequency battery capacity characteristics, extracting high-level representations of battery capacity. It combines high-frequency and low-frequency prediction results through stacking rules to achieve high-precision prediction of the battery's remaining service life. Experimental results show that the loss generated by the proposed hybrid deep learning detection model in the training set is approximately 7.87%. Compared with Support Vector Machine (SVM), Logistic Regression (LR), Recurrent Neural Network (RNN), and LSTM models, the proposed hybrid deep learning model demonstrates superior comprehensive performance indicators, with an Mean Absolute Percentage Error (MAPE) of only 1.438%. The experiments validate the effectiveness and practicality of the proposed model.