Cargando…
Towards interactional management for power batteries of electric vehicles
With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling a...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832365/ https://www.ncbi.nlm.nih.gov/pubmed/36712619 http://dx.doi.org/10.1039/d2ra06004c |
_version_ | 1784868040528101376 |
---|---|
author | He, Rong Xie, Wenlong Wu, Billy Brandon, Nigel P. Liu, Xinhua Li, Xinghu Yang, Shichun |
author_facet | He, Rong Xie, Wenlong Wu, Billy Brandon, Nigel P. Liu, Xinhua Li, Xinghu Yang, Shichun |
author_sort | He, Rong |
collection | PubMed |
description | With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications. |
format | Online Article Text |
id | pubmed-9832365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-98323652023-01-26 Towards interactional management for power batteries of electric vehicles He, Rong Xie, Wenlong Wu, Billy Brandon, Nigel P. Liu, Xinhua Li, Xinghu Yang, Shichun RSC Adv Chemistry With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications. The Royal Society of Chemistry 2023-01-11 /pmc/articles/PMC9832365/ /pubmed/36712619 http://dx.doi.org/10.1039/d2ra06004c Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry He, Rong Xie, Wenlong Wu, Billy Brandon, Nigel P. Liu, Xinhua Li, Xinghu Yang, Shichun Towards interactional management for power batteries of electric vehicles |
title | Towards interactional management for power batteries of electric vehicles |
title_full | Towards interactional management for power batteries of electric vehicles |
title_fullStr | Towards interactional management for power batteries of electric vehicles |
title_full_unstemmed | Towards interactional management for power batteries of electric vehicles |
title_short | Towards interactional management for power batteries of electric vehicles |
title_sort | towards interactional management for power batteries of electric vehicles |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832365/ https://www.ncbi.nlm.nih.gov/pubmed/36712619 http://dx.doi.org/10.1039/d2ra06004c |
work_keys_str_mv | AT herong towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT xiewenlong towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT wubilly towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT brandonnigelp towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT liuxinhua towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT lixinghu towardsinteractionalmanagementforpowerbatteriesofelectricvehicles AT yangshichun towardsinteractionalmanagementforpowerbatteriesofelectricvehicles |