An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator. (15th October 2022)

• Record Type: Journal Article
• Authors: Cao, Mengda; Zhang, Tao; Liu, Yajie; Zhang, Yajun; Wang, Yu; Li, Kaiwen
• Is Part Of: Energy. Volume 257(2022)
• Publication Date: 2022-10-15
• Subjects: Ensemble learning -- Lithium-ion battery -- Capacity estimation -- Incremental capacity analysis (ICA) -- Induced ordered weighted geometric averaging (IOWGA) operator; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124725
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Capacity estimation is crucial for assessing the health statuses of lithium-ion batteries to develop better battery usage and maintenance strategies. However, it is difficult to satisfy multiple application situations in which each individual prognostic method has its own particular preconditions and application limitations. Thus, in this paper, an ensemble prognostic framework is proposed to integrate several individual prognostic methods to achieve better capacity estimation accuracy. In the proposed framework, a measurement- and calculation-based combined feature extraction method is first applied to the battery charging phase to better capture the extracted features that are related to the health statuses of lithium-ion batteries. Then, a novel validation dataset-based induced ordered weighted geometric averaging (V-IOWGA) operator is proposed to realize the time-varying weight allocation of each individual prognostic method to solve issue by which the performances of different prognostic algorithms vary in different phases. The advantages of the proposed ensemble model are verified on lithium-ion battery datasets from NASA PCoE and the University of Maryland CALCE Laboratory, and its prediction accuracy is better than that of other individual prognostic models. In addition, comparison experiments involving three types of ensemble models validate the superiority of the proposed model.