An intelligent battery management system (BMS) with end-edge-cloud connectivity - a perspective

被引:0
|
作者
Mulpuri, Sai Krishna [1 ]
Sah, Bikash [2 ,3 ]
Kumar, Praveen [1 ,4 ]
机构
[1] Indian Inst Technol Guwahati, Dept Elect & Elect Engn, Gauhati 781039, Assam, India
[2] Bonn Rhein Sieg Univ Appl Sci, Dept Engn & Commun, D-53757 St Augustin, North Rhine Wes, Germany
[3] Fraunhofer Inst Energy Econ & Energy Syst Technol, Dept Power Elect & Elect Drive Syst, D-34117 Kassel, Germany
[4] Oak Ridge Natl Lab, Oak Ridge, TN USA
来源
SUSTAINABLE ENERGY & FUELS | 2025年 / 9卷 / 05期
关键词
LITHIUM-ION BATTERIES; SUPPORT VECTOR MACHINE; REMAINING USEFUL LIFE; ELECTROCHEMICAL MODEL; HEALTH ESTIMATION; PARTICLE FILTER; STATE; CHARGE; INITIALIZATION; TECHNOLOGY;
D O I
10.1039/d4se01238k
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The widespread adoption of electric vehicles (EVs) and large-scale energy storage has necessitated advancements in battery management systems (BMSs) so that the complex dynamics of batteries under various operational conditions are optimised for their efficiency, safety, and reliability. This paper addresses the challenges and drawbacks of conventional BMS architectures and proposes an intelligent battery management system (IBMS). Leveraging cutting-edge technologies such as cloud computing, digital twin, blockchain, and internet-of-things (IoT), the proposed IBMS integrates complex sensing, advanced embedded systems, and robust communication protocols. The IBMS adopts a multilayer parallel computing architecture, incorporating end-edge-cloud platforms, each dedicated to specific vital functions. Furthermore, the scalable and commercially viable nature of the IBMS technology makes it a promising solution for ensuring the safety and reliability of lithium-ion batteries in EVs. This paper also identifies and discusses crucial challenges and complexities across technical, commercial, and social domains inherent in the transition to advanced end-edge-cloud-based technology.
引用
收藏
页码:1142 / 1159
页数:18
相关论文
共 50 条
  • [31] A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing
    Jiang, Mingzhi
    Wu, Tianhao
    Wang, Zhe
    Gong, Yi
    Zhang, Lin
    Liu, Ren Ping
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (03) : 2459 - 2471
  • [32] A Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated Computing
    Peng, Bo
    Peng, Shi Lin
    Li, Qiang
    Chen, Cheng
    Zhou, Yu Zhu
    Lei, Xiang
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2023, PT VI, 2024, 14492 : 299 - 310
  • [33] Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks
    Shahhosseini, Sina
    Seo, Dongjoo
    Kanduri, Anil
    Hu, Tianyi
    Lim, Sung-Soo
    Donyanavard, Bryan
    Rahmani, Amir M.
    Dutt, Nikil
    ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 2022, 21 (06)
  • [34] A smart grid computational offloading policy generation method for end-edge-cloud environments
    Weiwei Liu
    Yue Wang
    Chenxi Xu
    Min Zheng
    Journal of Reliable Intelligent Environments, 2025, 11 (1)
  • [35] Attention to Task-Aligned Object Detection for End-Edge-Cloud Video Surveillance
    Liu, Yuanyuan
    Yu, Zhiyuan
    Zong, Danlong
    Zhu, Lu
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (08): : 13781 - 13792
  • [36] An Incentive Mechanism for Big Data Trading in End-Edge-Cloud Hierarchical Federated Learning
    Zhao, Yunfeng
    Liu, Zhicheng
    Qiu, Chao
    Wang, Xiaofei
    Yu, F. Richard
    Leung, Victor C. M.
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [37] End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment
    Peng, Kai
    Huang, Hualong
    Wan, Shaohua
    Leung, Victor C. M.
    WIRELESS NETWORKS, 2024, 30 (05) : 3495 - 3506
  • [38] Deep-Learning-Enhanced Multitarget Detection for End-Edge-Cloud Surveillance in Smart IoT
    Zhou, Xiaokang
    Xu, Xuesong
    Liang, Wei
    Zeng, Zhi
    Yan, Zheng
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (16): : 12588 - 12596
  • [39] Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
    Wu, Zhiyuan
    Sun, Sheng
    Wang, Yuwei
    Liu, Min
    Gao, Bo
    Pan, Quyang
    He, Tianliu
    Jiang, Xuefeng
    IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, 2024, : 131 - 140
  • [40] Never Lost Keys: A Novel Key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System
    Yichuan Wang
    Dan Wu
    Xiaoxue Liu
    Xinhong Hei
    ChinaCommunications, 2022, 19 (07) : 172 - 184