A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery
A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery
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DOI：
10.1016/j.ress.2021.108082
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Ting Tang;Huimei Yuan
中科院分区：
文献类型：
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作者：
Ting Tang;Huimei Yuan
针对锂离子电池原始容量退化数据非线性、非平稳、预测精度低等问题，提出了一种新的剩余使用寿命预测方法。首先，采用全系综经验模态分解自适应噪声对原始数据进行完全自适应分解，防止容量再生部分的有效信息被剔除；其次，通过过零率和新的融合规则得到融合的高低频部分，减少了输入网络组件的数量，降低了运行成本。然后，利用深度神经网络对低频部分进行预测；高频部分采用自行设计的改进型res2net双向门控循环单元全连接（IRes2Net-BiGRU-FC）进行预测。最后，根据绝对误差最小准则选取的最优结果包含了两种高频融合规则各自的优点。利用NASA两组不同充放电条件下的数据进行模拟实验，并与其他方法进行对比。结果表明，无论是基于恒压恒流模式下的电池数据还是基于电流随机模式下的电池数据，我们的方法都是可行的。
Aiming at the problems of non-linearity, non-stationary and low prediction accuracy of the original capacity degradation data for lithium-ion battery, a novel remaining useful life prediction approach is proposed. First, complete ensemble empirical mode decomposition adaptive noise is employed to achieve complete adaptive decomposition of the original data to prevent effective information about the capacity regeneration part from being eliminated. Next, fused high and low frequency parts are obtained through zero-crossing rate and new fusion rules, which can reduce the number of input network components and lighten operating costs. Then, the low frequency part is predicted using deep neural network; the high frequency part is predicted by self-designed improved Res2Net-Bidirectional Gated Recurrent Unit-Fully Connected (IRes2Net-BiGRU-FC). Finally, optimal results selected according to the criterion of minimum absolute error contains respective advantages of two high frequency fusion rules. Two sets of data from NASA under different charging and discharge conditions are used for simulation experiments and compared with other methods. The results show that our approach is feasible regardless of whether it is based on the battery data obtained in the constant voltage and current mode or the current random mode.