A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Computer Science Pub Date : 2023-12-16 DOI:10.1007/s11704-023-3277-4

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Abstract

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved. To address this challenge, this paper proposes a novel deep learning model, the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features. Additionally, we devise an ensemble predictor based on a Mixture-of-Experts (MoE) architecture to generate reliable RUL predictions. The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods, providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process. Our code and dataset are available at the website of github.

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用于预测锂离子电池剩余使用寿命的 MLP-Mixer 和专家混合模型

摘要准确预测锂离子电池的剩余使用寿命（RUL）对电池管理系统至关重要。通过利用电池容量时间序列数据，基于深度学习的方法已被证明能有效预测 RUL。然而，容量时间序列中的长距离序列依赖性和突变等特征的表示学习仍有待改进。为应对这一挑战，本文提出了一种新型深度学习模型--MLP-Mixer 和专家混合（MMMe）模型，用于 RUL 预测。MMMe 模型利用门控递归单元（Gated Recurrent Unit）和多头注意（Multi-Head Attention）机制对电池容量的序列数据进行编码，以捕捉时间特征，并利用重零 MLP-Mixer 模型捕捉高级特征。此外，我们还设计了一种基于专家混合（MoE）架构的集合预测器，以生成可靠的 RUL 预测。在公共数据集上的实验结果表明，我们提出的模型明显优于其他现有方法，能提供更可靠、更精确的 RUL 预测，同时还能准确跟踪容量衰减过程。我们的代码和数据集可在 github 网站上获取。

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.