Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
复制标题
基于长短期记忆与Elman神经网络混合模型的锂离子电池剩余寿命预测
DOI：
10.1016/j.est.2018.12.011
复制
发表时间：
2019-02-01
影响因子：
9.4
通讯作者：
Dong, Peng
中科院分区：
文献类型：
--
作者：
Li, Xiaoyu;Zhang, Lei;Dong, Peng
本文将经验模型分解算法与长短期记忆和Elman神经网络相结合，提出了一种新的混合Elman- lstm方法用于电池剩余使用寿命预测。采用经验模型分解算法将记录的电池容量与循环次数数据分解成若干子层。然后分别建立循环长短期记忆和Elman神经网络来预测高频子层和低频子层。收集了全面的电池测试数据集，并将其用于模型参数化和性能评估。对比结果表明，所提出的Elman-LSTM混合模型具有较好的性能，能较准确地预测电池剩余使用寿命。基于两个未见数据集的相对预测误差分别为3.3%和3.21%。
This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively.