A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Chao Li and Assimina A. Pelegri
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Chao Li: Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8058, USA
Assimina A. Pelegri: Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8058, USA

Energies, 2021, vol. 14, issue 24, 1-24

Abstract: Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

Keywords: imbalance within battery cells; GPR-based data-driven framework; predict the thermal and electrical behaviors of battery cells (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: 2021
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