Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives  

作　　者：Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei 

机构地区：[1]State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,Shaanxi,China [2]Shaanxi Key Laboratory of Intelligent Robots,School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,Shaanxi,China [3]Department of Electrical Engineering and Automation,Luoyang Institute of Science and Technology,Luoyang 471023,Henan,China [4]School of Mechanical Engineering,Hangzhou Dianzi University,Hangzhou 310018,Zhejiang,China

出　　处：《Journal of Energy Chemistry》2025年第1期739-759,共21页能源化学(英文版)

基　　金：National Natural Science Foundation of China (52075420);Fundamental Research Funds for the Central Universities (xzy022023049);National Key Research and Development Program of China (2023YFB3408600)。

摘　　要：The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The co

关 键 词：Lithium-ion battery State-of-health estimation DATA-DRIVEN Machine learning Ensemble learning Ensemble diversity 

分 类 号：TM9[电气工程—电力电子与电力传动]