State of health estimation method for lithium-ion batteries using incremental capacity and long short-term memory network

Abstract

Knowing the battery state of health (SOH) is essential to ensure safety and reliability in the operation of electric vehicles (EVs). However, it is still difficult to accurately estimate the SOH values of batteries owing to the complexity of their aging mechanisms. In this context, an SOH estimation method using incremental capacity (IC) and long short-term memory (LSTM) network was investigated in this study. We proposed an improved IC curve acquisition method based on reference voltage, which can retain the important features of the IC curve and reduce computational efforts. Based on the correlation between the incremental capacity and SOH of the battery, health feature variables were extracted from the IC curves. Moreover, considering the time-series characteristics and long-term dependency of battery degradation, we adopted the LSTM network to develop the SOH estimation model. The accuracy and reliability of the proposed model were verified. The results showed that the proposed IC curve acquisition method reduced the calculation time by 11.49 % compared to that using the Gaussian filter. Compared with the estimation results obtained using the support vector machine (SVM) and artificial neural network (ANN), the estimation results of the proposed IC curve acquisition method combined with the LSTM network had the minimum mean absolute percentage error (MAPE). The MAPE of the estimation results was <2 % for all the different battery samples.