Remaining Capacity Estimation for Lithium-Ion Batteries Based on Differential Temperature Curve and Hybrid Deep Learning Approach

Abstract

Efficient and accurate prediction of battery remaining capacity can guarantee the safety and reliability of electric vehicles (EVs). However, battery capacity is difficult to measure directly due to complex application scenarios and sophisticated internal physicochemical reactions. This study develops a hybrid deep learning approach for accurate remaining capacity estimation based on differential temperature (DT) curve. First, the cycle life data are acquired and analyzed. Then, DT curves are deduced based on the charging data and smoothed via Kalman filter (KF). Next, health features (HFs) that characterize the battery degradation are excavated from the DT curves. Finally, a hybrid deep learning model fusion convolutional neural network (CNN) and gated recurrent unit (GRU) recurrent neural network (RNN) is established to predict battery remaining capacity. Each deep neural network (NN) in the model is engaged to execute a particular part in the forecasting task to maximize its corresponding merits. The superiority of the proposed method in terms of accuracy is justified via comparison with other modern methods including long short-term memory (LSTM) RNN, GRU RNN and a hybrid model integrating CNN and LSTM RNN. Experimental results demonstrate that the effectiveness and applicability of the proposed method in enabling battery remaining capacity estimation.