An Early Remaining Useful Life Prediction Method for Lithium-ion Batteries using Optimization Algorithm-Assisted Gaussian Process Regression

Lithium-ion batteries are widely used as power devices for electric vehicles due to their high energy density and fast charging capability. However, battery life degradation adversely affects safety management and utilization maintenance. Therefore, research on battery life prediction can help early health diagnosis and residual value assessment to prevent system downtime. Aiming at the problems of poor early prediction ability of current methods, this paper proposes an early life prediction method for lithium-ion batteries using optimization algorithm-assisted Gaussian Process Regression. Firstly, six health features strongly correlated with end-of-life are extracted from the battery's first 100 cycles of charge/discharge data. Then, a battery life prediction model is established based on Gaussian Process Regression with composite kernel functions. The particle swarm optimization algorithm is employed to optimize the hyperparameters of the composite kernel functions, enhancing the adaptiveness of the proposed prediction method. The validation result shows that the prediction is highly accurate, and the mean absolute percentage error of early life prediction can reach 10.66% overall. The Gaussian Process Regression model based on hyperparameter optimization proposed in this paper has better prediction accuracy compared with the prediction results without hyperparameter tuning and with a single kernel function. The method proposed in this paper also outperforms other common machine learning algorithms in terms of prediction accuracy.