Improving SOH estimation for lithium-ion batteries using TimeGAN
S.Y. Seol, Jungeun Lee, Jaewoo Yoon, Byeong-Woo Kim
Abstract
Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.