Improving State-of-Health Estimation for Lithium-Ion Batteries Based on a Generative Adversarial Network and Partial Discharge Profiles

Abstract

The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge data for SOH estimation to address the unstable output of traditional estimation models when using partial discharge data under low-voltage conditions. This study first used the DoppelGANger network to generate artificially synthesized data. After the data augmentation process, we trained the temporal convolutional network to construct a data-driven SOH model. Finally, the performance of the SOH model output was evaluated using three indicators: RMSE, MAPE, and delta. The proposed method improved five kinds of low-voltage operating conditions in seven testing scenarios compared with traditional SOH estimation models. The experimental results provide a practical solution for data-driven SOH estimation.