Considering the self-adaptive segmentation of time series in interval prediction of remaining useful life for lithium-ion battery

In order to solve the limitations of numerical prediction in RUL prediction of lithium-ion battery, this paper studies the essence of interval prediction, and proposes a feasible strategy to achieve the RUL interval prediction of lithium-ion battery. In order to make the proposed interval prediction strategy applicable to degradation data sets with different change states, a self-adaptive time series segmentation algorithm is proposed. The algorithm can identify and distinguish smooth degradation stages and obvious fluctuation stages of the degradation series, so as to retain the fluctuation information of the original degradation data as much as possible in the subsequent processing, and is also adaptive for different battery degradation data sets. Firstly, the proposed self-adaptive segmentation algorithm is used to segment the original capacity degradation data, and then the segmentation results are processed by fuzzy information granulation into granule sequences with upper and lower limits. Finally, the least squares support vector machine is used to model the granule data to realize interval prediction. Four groups of battery data sets are used to verify the feasibility and effectiveness of the proposed self-adaptive segmentation algorithm, and the determination of an important parameter in this algorithm is also discussed.