Research on Battery Life Prediction Method Based on Gaussian Process Regression

Battery life prediction technology is one of the core elements of battery prediction and health management technology, and it is also a recognized research hotspot and difficult area in the field of battery life prediction. Most of the existing battery life prediction models are based on a single prediction index, with low prediction accuracy and poor generalization performance of the model. In this paper, a battery life prediction model based on Gaussian process regression with maximized marginal log-likelihood improved kernel function is constructed by collecting lithium-ion battery data from real vehicles to achieve accurate prediction of the remaining life of lithium batteries. Firstly, the operating parameters and environmental parameters of the lithium-ion battery are extracted according to the factors affecting the remaining life of the battery, and the optimal set of features is screened as the model input based on the random forest algorithm, and secondly, the optimal parameters are found by maximizing the marginal log-likelihood, and this method of optimizing the Gaussian process kernel function enables it to dynamically adjust the Gaussian process kernel function, which can be smoother and give better results without cross-validation. This method of optimizing the Gaussian process kernel function enables it to dynamically adjust the Gaussian process kernel function, which can be smoother and give better regularization results without cross-validation. The results show that the combination of the linear combination of kernel functions and hyperparameter optimization achieves a better prediction than the initial GPR model and proves its effectiveness.