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© 2024 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field.

Details

Title
Battery Prognostics and Health Management: AI and Big Data
Author
Li, Di 1 ; Jinrui Nan 2 ; Burke, Andrew F 3 ; Zhao, Jingyuan 3 

 Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, China 
 Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen 518000, China 
 Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA; [email protected] 
First page
10
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20326653
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3159622045
Copyright
© 2024 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.