A charging-feature-based estimation model for state of health of lithium-ion batteries

Within battery management systems, the state of health of lithium-ion batteries is a key and vital enabler to ensure battery safety and efficiency. However, the accurate state of health estimation is still a critical but challenging task, and the complex electrochemical attributes underlying the degradation processes of lithium-ion batteries are not directly available. In response to this challenge, this study proposes a charging-feature-based model to realize state of health estimation by Gaussian process regression. In this approach, two features are extracted only from the monitoring parameters obtained from charging current and voltage curves. These extracted features have been demonstrated to be correlated with the state of health. Subsequently, a regression model with a 2-dimensional linear mean function and a new double-covariance function is developed to improve estimation performance. Consequently, the proposed model effectively tracks both global and local degradation trends synchronously. Finally, the reliability and accuracy of the proposed model are verified using two different batteries datasets. The results illustrate that the proposed model is capable of realizing accurate batteries' state of health estimation, thereby outperforming other counterparts in uncertainty representation and estimation errors, whether under static profiles or dynamic profiles.