电池健康状况估计的多模型集合学习:最新进展与展望

IF 13.1 1区 化学 Q1 Energy Journal of Energy Chemistry Pub Date : 2024-09-20 DOI:10.1016/j.jechem.2024.09.021
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引用次数: 0

摘要

随着锂离子电池市场的蓬勃发展,对更可靠的电池性能监测的需求也与日俱增。准确的健康状态(SOH)估计对于确保电池的运行性能至关重要。尽管现有研究报告了许多数据驱动的电池 SOH 估算方法,但这些方法在不同的应用场景中往往表现出不一致的性能。为解决这一问题并克服单个数据驱动模型的性能限制,整合多个模型进行 SOH 估算的方法受到了广泛关注。集合学习(EL)通常利用多个基础模型的优势来获得更稳健、更准确的输出结果。然而,由于缺乏对当前研究的清晰回顾,阻碍了 SOH 估算中集合方法的进一步发展。因此,本文全面评述了用于电池 SOH 估算的多模型集合学习方法。首先,根据组合策略将现有的集合方法系统地分为 6 类。细致分析了每一类集合学习方法的不同实现方式和内在联系,突出了它们的区别、创新和典型应用。随后,从基础模型、组合策略和出版趋势等方面对这些组合方法进行了全面比较。从 6 个维度进行的评估强调了基于堆叠的集合方法的卓越性能。随后,从加权集合和多样性的角度对这些集合方法进行了进一步检查,旨在启发提高集合性能的潜在方法。此外,还强调了解决基础模型选择、模型稳健性和不确定性测量以及集合模型在实际应用中的可解释性等挑战。最后,概述了未来的研究前景,特别指出深度学习集合有望推动电池 SOH 估算的集合方法。高级机器学习与集合学习的融合预计将产生有价值的研究途径。加快集合学习的研究有望在真实世界条件下实现更准确、更可靠的电池 SOH 估算。
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Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives
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 convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
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来源期刊
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
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