Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation
Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation
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DOI：
10.1016/j.energy.2020.119078
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发表时间：
2021-01-15
期刊：
影响因子：
9
通讯作者：
Pan, Haihong
中科院分区：
文献类型：
--
作者：
Chen, Lin;Wang, Huimin;Pan, Haihong
准确估计电池健康状态（SOH）对于保证电池储能系统的安全性和可靠性具有重要意义。由于电池复杂的退化机制，对于未知使用水平的不同类型电池的SOH估计的转移应用具有挑战性。为了解决这个问题，本研究提出了一种用于 SOH 估计的新型代谢极限学习机（MELM）框架。建立了基于极限学习机（ELM）的退化状态模型来描述复杂的电池退化机制，所建立的模型可以映射不同电池的退化特征和退化动态之间的关系。为了用少量数据实现不同使用水平下的SOH估计，引入代谢机制来更新退化状态模型的输入并反映最新的退化趋势。为了减少新陈代谢带来的误差，采用灰色模型来推断误差累积的趋势，并对估计结果进行修正。从不同方面综合验证了MELM框架的突出性能，结果表明MELM框架可以有效地实现对未知使用水平的不同类型电池的SOH估计。 (c) 2020 Elsevier Ltd. 保留所有权利。
Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels. (c) 2020 Elsevier Ltd. All rights reserved.