Cloud-Based Artificial Intelligence Framework for Battery Management System

Dapai Shi, Jingyuan Zhao (), Chika Eze, Zhenghong Wang, Junbin Wang, Yubo Lian and Andrew F. Burke ()
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Dapai Shi: Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, China
Jingyuan Zhao: Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA
Chika Eze: Department of Mechanical Engineering, University of California, Merced, CA 95343, USA
Zhenghong Wang: Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China
Junbin Wang: BYD Automotive Engineering Research Institute, Shenzhen 518118, China
Yubo Lian: BYD Automotive Engineering Research Institute, Shenzhen 518118, China
Andrew F. Burke: Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA

Energies, 2023, vol. 16, issue 11, 1-21

Abstract: As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Keywords: lithium-ion battery; battery management system; machine learning; cloud; artificial intelligence; state of charge; state of health; safety; field; real-world application (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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