电动汽车数字孪生体电池储能系统准备概述

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Smart Grid Pub Date : 2023-02-05 DOI:10.1049/stg2.12101
Rolando Gilbert Zequera, Anton Rassõlkin, Toomas Vaimann, Ants Kallaste
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引用次数: 1

摘要

能源需求是科学和工程领域的一个相关话题,由于全球气候变化和环境问题的挑战,过去几年一直在讨论这个话题。目前,电动汽车(ev)提供了一种强调使用储能设备来减少二氧化碳排放的机动性来源。先进数据分析和物联网的不断发展推动了数字孪生(DT)的实施,所有这些都是为了提高系统的构建、设计和运行效率。对于电动汽车的组件,电池被认为是根据健康状态和充电状态进行分析的最昂贵的元素,这导致实现最优模型,以及电池系统的DT。本文对当前电动汽车储能技术及其相应的电池系统的发展趋势进行了文献综述,概述了不同类型的模型,并确定了工业领域未来的挑战。此外,本文还讨论了针对电动汽车电池系统的DT的可靠解释,重点介绍了一些研究案例、特征和技术机遇。
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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Overview of battery energy storage systems readiness for digital twin of electric vehicles

The demand for energy is a relevant topic in the field of science and engineering, which has been discussed throughout the last years due to the challenges of climate change and environmental concerns around the world. Currently, electric vehicles (EVs) offer a source of mobility that emphasises the use of energy storage devices to reduce CO2 emissions. The growing development of advanced data analytics and the Internet of Things has driven the implementation of the Digital Twin (DT), all to improve efficiency in the build, design and operation of the system. Regarding the components of EVs, the batteries are considered as the most expensive elements to analyse according to the State of Health and the State of Charge, which lead to implement the most optimal models, along with a DT for battery systems. The present article provides a literature review about the current development trends of EVs' energy storage technologies, with their corresponding battery systems, which gives an overview to understand different type of models and to identify future challenges in the industrial sector. Additionally, a solid explanation of the DT focussed on battery systems for EVs is discussed, highlighting some study cases, characteristics, and technological opportunities.

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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
期刊最新文献
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