Piecewise model based intelligent prognostics for state of health prediction of rechargeable batteries with capacity regeneration phenomena
Piecewise model based intelligent prognostics for state of health prediction of rechargeable batteries with capacity regeneration phenomena
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DOI：
10.1016/j.measurement.2019.07.064
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发表时间：
2019-12
期刊：
影响因子：
5.6
通讯作者：
Dong Wang;Jin-Zhen Kong;Yang Zhao;K. Tsui
中科院分区：
文献类型：
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作者：
Dong Wang;Jin-Zhen Kong;Yang Zhao;K. Tsui
蓄电池健康状态预测是蓄电池健康管理系统中的一个重要课题，它可以推断蓄电池的剩余充放电循环次数，保证蓄电池的高可靠性。为了研究各种预测算法，在NASA艾姆斯预测卓越中心进行了电池寿命周期衰减实验。尽管过去几年提出了一些预测算法来预测RCDC，但大多数都忽略了电池容量再生现象（CRP）对RCDC预测的影响。电池CRP在非预期的充电-放电循环中引起一些突然的容量增加，如果不考虑这种CRP，则这可能进一步导致不准确的RCDC预测。本文提出了一种基于分段模型的智能预测方法，用于CRP蓄电池的RCDC预测。结果表明，该预测模型能够准确估计NASA电池容量衰减数据，自动检测CRP，准确预测RCDC。
State of health prediction of rechargeable batteries is an important topic in battery health management systems to infer remaining charge-discharge cycles (RCDC) and ensure high reliability of rechargeable batteries. To investigate various prognostic algorithms, battery life-cycle fade experiments were conducted in the NASA Ames Prognostic Center of Excellence. Even though some prognostic algorithms were proposed in the past years to predict RCDC, most of them omitted the influence of battery capacity regeneration phenomena (CRP) on RCDC prediction. Battery CRP cause some sudden capacity increments at unexpected charge-discharge cycles, which may further result in inaccurate RCDC prediction if such CRP are not considered. In this paper, piecewise model based intelligent prognostics for RCDC prediction of rechargeable batteries with CRP is proposed. Results show that the proposed prognostic model can precisely estimate NASA battery capacity fade data, automatically detect CRP and accurately predict RCDC.