基于多特征协作和关注机制的 bi-LSTM 的锂离子电池健康状况估计端云协作方法

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

International Journal of Green Energy Pub Date : 2024-01-08 DOI:10.1080/15435075.2023.2299402

Pengchang Jiang, Tianyi Zhang, Guangjie Huang, Wei Hua, Yong Zhang, Wentao Wang, Tao Zhu

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引用次数: 0

摘要

本研究开发了一种用于估算电池健康状况（SOH）的端云协作方法。它融合了用于详细分析的云端深度学习模型和用于评估电池健康状况的端侧模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An end-cloud collaboration approach for state-of-health estimation of lithium-ion batteries based on bi-LSTM with collaboration of multi-feature and attention mechanism

This study develops an end-cloud collaboration method for estimating the State-of-Health (SOH) of batteries. It fuses a cloud-based deep learning model for detailed analysis and an end-side model f...

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来源期刊

International Journal of Green Energy 工程技术-能源与燃料

CiteScore

6.60

自引率

9.10%

发文量

112

审稿时长

3.7 months

期刊介绍： International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.