荷电状态
稳健性(进化)
计算机科学
估计员
健康状况
控制理论(社会学)
在线模型
电池(电)
工程类
人工智能
数学
功率(物理)
统计
物理
基因
化学
控制(管理)
量子力学
生物化学
作者
Zhongbao Wei,Linrun Feng,Zhongjie He,Wenyu Zhang,Kaiyuan Li
出处
期刊:Energies
[MDPI AG]
日期:2018-07-11
卷期号:11 (7): 1810-1810
被引量:29
摘要
The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.
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