Hybrid Gray Wolf Optimization Method in Support Vector Regression Framework for Highly Precise Prediction of Remaining Useful Life of Lithium-ion Batteries

The prediction of remaining useful life (RUL) of lithium-ion batteries takes a critical effect in the battery management system, and precise prediction of RUL guarantees the secure and reliable functioning of batteries. For the difficult problem of selecting the parameter kernel of the training data set of the RUL prediction model constructed based on the support vector regression model, an intelligent gray wolf optimization algorithm is introduced for optimization, and owing to the premature stagnation and multiple susceptibility to local optimum problems of the gray wolf algorithm, a differential evolution strategy is introduced to propose a hybrid gray wolf optimization algorithm based on differential evolution to enhance the original gray wolf optimization. The variance and choice operators of differential evolution are designed to sustaining the diversity of stocks, and then their crossover operations and selection operators are made to carry out global search to enhance the prediction of the model and realize exact forecast of the remaining lifetime. Experiments on the NASA lithium-ion battery dataset demonstrate the effectiveness of the proposed RUL prediction method. Empirical tests demonstrate that the maximum average absolute value error of the prediction of the fusion algorithm on the battery dataset is limited to within 1%, which reflects the high accuracy prediction capability and strong robustness.