Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

Dapai Shi, Jingyuan Zhao (), Zhenghong Wang, Heng Zhao, Chika Eze, Junbin Wang, Yubo Lian and Andrew F. Burke ()
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Dapai Shi: Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441053, China
Jingyuan Zhao: Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA
Zhenghong Wang: Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441053, China
Heng Zhao: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Chika Eze: Department of Mechanical Engineering, University of California, Merced, CA 94720, USA
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 9, 1-19

Abstract: Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

Keywords: lithium-ion battery; state of charge; state of health; deep learning; cloud; field 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 (3)

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