Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization

IF 1.8 Q4 ENERGY & FUELS
AIMS Energy Pub Date : 2023-01-01 DOI:10.3934/energy.2023043
Xiaoyu Zheng, Dewang Chen, Yusheng Wang, Liping Zhuang
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引用次数: 0

Abstract

The performance of lithium-ion batteries declines rapidly over time, inducing anxiety in their usage. Ascertaining the capacity of these batteries is difficult to measure directly during online remaining useful life (RUL) prediction, and a single deep learning model falls short of accuracy and applicability in RUL predictive analysis. Hence, this study proposes a lithium-ion battery RUL indirect prediction model, fusing convolutional neural networks and bidirectional gated recurrent units (CNN-BiGRU). The analysis of characteristic parameters of battery life status reveals the selection of pressure discharge time, average discharge voltage and average temperature as health factors of lithium-ion batteries. Following this, a CNN-BiGRU model for lithium-ion battery RUL indirect prediction is established, and the Tree-structured Parzen Estimator (TPE) adaptive hyperparameter optimization method is used for CNN-BiGRU model hyperparameter optimization. Overall, comparison experiments on single-model and other fusion models demonstrate our proposed model's superiority in the prediction of RUL in terms of stability and accuracy.

基于CNN-BiGRU融合模型和TPE优化的锂离子电池剩余使用寿命间接预测
<abstract>< >随着时间的推移,锂离子电池的性能迅速下降,在使用中引起焦虑。在在线剩余使用寿命(RUL)预测中,很难直接测量这些电池的容量,单一的深度学习模型在RUL预测分析中缺乏准确性和适用性。因此,本研究提出了一种融合卷积神经网络和双向门控循环单元(CNN-BiGRU)的锂离子电池RUL间接预测模型。通过对电池寿命状态特征参数的分析,选择压力放电时间、平均放电电压和平均温度作为锂离子电池的健康因素。在此基础上,建立了用于锂离子电池RUL间接预测的CNN-BiGRU模型,并采用树结构Parzen Estimator (TPE)自适应超参数优化方法对CNN-BiGRU模型进行了超参数优化。总体而言,单模型和其他融合模型的对比实验表明,我们提出的模型在预测RUL的稳定性和准确性方面具有优势。& lt; / abstract>
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来源期刊
AIMS Energy
AIMS Energy ENERGY & FUELS-
CiteScore
3.80
自引率
11.10%
发文量
34
审稿时长
12 weeks
期刊介绍: AIMS Energy is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of Energy technology and science. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Energy welcomes, but not limited to, the papers from the following topics: · Alternative energy · Bioenergy · Biofuel · Energy conversion · Energy conservation · Energy transformation · Future energy development · Green energy · Power harvesting · Renewable energy
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