Li-ion battery prognostic and health management through an indirect hybrid model
Li-ion battery prognostic and health management through an indirect hybrid model
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DOI：
10.1016/j.est.2021.102990
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发表时间：
2021-10
影响因子：
9.4
通讯作者：
Lyu Zhiqiang;Geng-feng Wang;Renjing Gao
中科院分区：
文献类型：
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作者：
Lyu Zhiqiang;Geng-feng Wang;Renjing Gao
由于复杂的电池老化，不可测量的在线电池容量妨碍了准确的健康状态（SOH）估计和剩余使用寿命（RUL）预测。为解决这一问题，提出了一种电池在线预测与健康管理（PHM）的间接混合模型。首先，基于部分充电数据提取两个电池老化特征，定量评价电池容量；其次，利用灰狼优化器（GWO）对多核相关向量机（MKRVM）进行优化，确定不同核函数的权值和核参数，建立电池老化特征与容量的映射关系；第三，利用互补集成经验模态分解（CEEMD）将可测量和历史老化特征分解为高频和低频信号，然后分别利用长短期记忆神经网络（LSTMNN）和前馈神经网络（FNN）对不同频率的信号进行建模和预测。最后，利用实时老化特征进行SOH估计，并通过基于gwo的MKRVM将预测的老化特征用于RUL预测。此外，通过开放获取的电池老化数据集验证了所提方法的性能。结果表明，间接混合模型对电池PHM具有较高的灵活性和较强的鲁棒性。
The immeasurable online battery capacity hampers the accurate State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction because of complex battery ageing. To resolve the issues, an indirect hybrid model for online battery Prognostics and Health Management (PHM) is put forward. First, based on partial charging dada, two battery ageing features are extracted for the quantitative evaluation of battery capacity. Second, a Multi-Kernel Relevance Vector Machine (MKRVM) is optimized by Grey Wolf Optimizer (GWO) to determine the weights and kernel parameters of different kernel functions, then establish a mapping between battery ageing features and capacity. Third, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) decomposes the measurable and historical ageing features into high- and low-frequency signals, then, the signals with different frequencies are modelled and predicted by Long Short-Term Memory Neural Network (LSTMNN) and Feedforward Neural Network (FNN), respectively. Finally, the real-time ageing features are utilized for SOH estimation and the predicted ageing features are used for RUL prediction through the GWO-based MKRVM. Furthermore, the performance of the proposed method is validated by an open-access battery ageing dataset. The results demonstrate that the indirect hybrid model has high flexibility and strong robustness on battery PHM.