State-of-Health Estimation of Lithium-Ion Batteries Based on Thermal Characteristics Mining and Multi-Gaussian Process Regression Strategy

Accurate prediction of the state of health (SOH) is essential to ensure the safety and reliability of battery operation. The thermal factor is an important indicator of SOH and many methods based on temperature are sensitive to measurement noise. To improve SOH estimation precision, a new health indicator (HI) directly extracted from the temperature curve is developed and an integrated multi-Gaussian process regression (MGPR) model is proposed. First, based on the trend analysis of the charging temperature curves with battery degradation, three features that can reflect thermal characteristics are extracted as the HI. Second, considering that the model generated by machine learning is influenced by the training dataset and the inherent inconsistencies in batteries, MGPR model is proposed to improve the model fitness. The training data is reformed and multiple GPR models are established. The multiple models are weighed by taking into account the prediction uncertainty to get the final SOH estimation result. Finally, two types of open-source data relative to different ambient temperatures and operating profiles are used to verify the performance. Experiment results show that the HI developed can characterize the battery degradation well and the MGPR model has high robustness and can obtain high-precision estimation results.