Hyun-Yong Park, Hee-Sung Lim, and Kyo-Beum Lee, "SOH Estimation of Batteries using Lithium-Ion Internal Parameters with Convolution Neural Network and Gated Recurrent," The Transactions of the Korean Institute of Electrical Engineers, vol. 72, no. 3, pp. > Paper

This paper proposes an estimation method for Lithium-Ion Batteries SOH by learning the batteries’ internal parameters using the Convolution Neural 

Network and the Gated Recurrent Unit. Various equivalent circuit models exist to represent the batteries’ internal parameters. Among these 

equivalent circuit models, the most representative model is the Randles model, and the data measured based on the Randles model is used as 

learning input data. The internal parameters of batteries change non-linearly depending on the operation condition and use time. So, nonlinear 

features are extracted using the CNN input as the batteries' parameters. The extracted features are used as an input of the GRU to learn the 

characteristics of change over time, and SOH is predicted through this. The learning dataset utilizes 17IND10 LibForSecUse of EMPIR, which 

validates the performance of the proposed model.