AI for Smart Battery State Estimation: A Perspective

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Abstract

In battery management systems (BMSs), state estimation stands as a pivotal element yet encounters significant challenges. These include the poor observability inherent in fixed configuration battery packs, limited generalizability of pre-trained machine learning models, and the deficiency of higher-level management strategies. To address these obstacles, we propose a forward-looking perspective on the future BMS state estimation, introducing the concept of a "Smart Battery". Battery digital twin enables synthetic data generation and physics-informed AI development. This approach integrates battery digital twin to generate synthetic data, which is then used for data augmentation and physics-informed AI development. Additionally, it incorporates advanced data cleaning and selection techniques to preserve essential information and augment data management efficiency. Leveraging cutting-edge AI algorithms, such as transfer learning and meta-learning, aims to mitigate issues of model generalization and feature invalidation under various operating conditions. Furthermore, this paper emphasizes the importance of multi-task learning for batteries, enabling comprehensive health assessments. By fully utilizing both short-term estimations and long-term predictions, the proposed framework contributes to the advancement of higher-level health and thermal management designs. We aim to furnish pioneering insights for state estimation in future intelligent BMSs.

OriginalsprogEngelsk
Titel2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia
Antal sider5
ForlagIEEE Signal Processing Society
Publikationsdato2024
Sider5126-5130
ISBN (Elektronisk)9798350351330
DOI
StatusUdgivet - 2024
Begivenhed10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia - Chengdu, Kina
Varighed: 17 maj 202420 maj 2024

Konference

Konference10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia
Land/OmrådeKina
ByChengdu
Periode17/05/202420/05/2024
SponsorChina Electrotechnical Society (CES), IEEE Power Electronics Society (PELS), Southwest Jiaotong University
Navn2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

Bibliografisk note

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© 2024 IEEE.

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