Lithium-ion battery remaining useful life estimation based on ensemble learning with ls-svm algorithm

This chapter proposes the integrated use of parameter-selection-based ensemble learning (PSBEL) and least square support vector machine (LS-SVM) algorithm for lithium-ion battery remaining useful life (RUL) estimation. Technically, given a set of monitoring parameters, some groups of parameters are randomly selected to construct LS-SVM submodels. On the basis of these submodels, ensemble learning is utilized to achieve a final result, which overcomes the difficulty of accurately determining the model parameters and significantly improves the precision and stability of RUL estimation. The proposed approach provides practitioners with confidence intervals and probability distributions of RUL estimates for uncertainty management. The validity and applicability of the PSBEL with LS-SVM for RUL estimation are demonstrated using capacity-changing data of lithium-ion batteries during discharging cycles. To analyze the impact of different submodels on the precision of final RUL prediction and to improve the uncertainty representation, more advanced ensemble learning techniques should be considered.