| 引用本文: | 刘 斌,吉春霖,曹丽君,武欣雅,段云凤.基于自适应噪声完全集合经验模态分解与BiLSTM-Transformer的锂离子电池剩余使用寿命预测[J].电力系统保护与控制,2024,52(15):167-177.[点击复制] |
| LIU Bin,JI Chunlin,CAO Lijun,WU Xinya,DUAN Yunfeng.Prediction of remaining service life of lithium-ion batteries based on complete ensemble empiricalmode decomposition with adaptive noise and BiLSTM-Transformer[J].Power System Protection and Control,2024,52(15):167-177[点击复制] |
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| 摘要: |
| 锂离子电池剩余使用寿命(remaining useful life, RUL)是使用者十分关心的问题，其涉及电池的更换时间和安全。针对锂离子电池的电容量非线性变化趋势，提出了一种基于自适应噪声完全集合经验模态分解与双向长短期记忆网络-Transformer的锂离子电池剩余使用寿命预测方法。首先，利用自适应噪声完全集合经验模态分解方法对锂离子电池电容量数据进行分解。其次，使用串联的双向长短期记忆神经网络和Transformer网络对分解后得到的残差序列和本征模态分量序列进行建模预测。最后，将预测的若干本征模态分量序列和残差序列进行求和，并对求和之后的最终预测数据与原始数据进行RUL预测。采用NASA公开的电池数据集对所提方法进行验证，结果表明，所提方法的平均绝对误差、均方根误差、平均绝对百分比误差和绝对误差控制分别控制在0.0173、0.0231、1.2084%和3个循环周期以内，能够有效地提高锂离子电池RUL的预测精度。 |
| 关键词: 锂离子电池 剩余使用寿命预测 Transformer网络 双向长短期记忆网络 完全集合经验模态分解 |
| DOI：10.19783/j.cnki.pspc.231507 |
| 投稿时间：2023-11-27修订日期：2024-01-07 |
| 基金项目:国家自然科学基金项目资助(72071183)；山西省基础研究计划项目资助(202103021224274，202303021221144)；山西省回国留学人员科研项目资助(2022-163) |
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| Prediction of remaining service life of lithium-ion batteries based on complete ensemble empiricalmode decomposition with adaptive noise and BiLSTM-Transformer |
| LIU Bin1,JI Chunlin2,CAO Lijun1,WU Xinya1,DUAN Yunfeng3 |
| (1. School of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, China; 2. School of Computer
Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China; 3. School of
Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, China) |
| Abstract: |
| The remaining useful life (RUL) of lithium-ion batteries is a concern for users, as it relates to the timing of battery replacement and safety. Addressing the non-linear variation trend in the capacity of lithium-ion batteries, a method for predicting the RUL is proposed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bidirectional long short-term memory (BiLSTM)-Transformer. First, the lithium-ion battery capacity data is decomposed using CEEMDAN method. Subsequently, a concatenated model consisting of BiLSTM neural networks and a Transformer network is employed to model and predict the residual sequences obtained from the decomposition and the intrinsic mode component sequences. Finally, the predicted intrinsic mode component sequences and residual sequences are summed, and the RUL is forecast by comparing the final data after summation with the original data. The proposed method is validated using NASA’s publicly available battery dataset. Experimental results demonstrate that the mean absolute, root mean square, mean absolute percentage errors and absolute errors are controlled within 0.0173, 0.0231, 1.2084% and 3 cycles, respectively. The proposed approach effectively enhances the accuracy of RUL prediction for lithium-ion batteries. |
| Key words: lithium-ion battery remaining useful life Transformer network bidirectional long short term memory network complete ensemble empirical mode decomposition |
