A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

• Published in: IEEE access Vol. 8; pp. 187893 - 187901
• Main Authors: Shen, Wenjing, Xu, Kangkang, Deng, Liming, Zhang, Shupeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational modeling; Dynamics; Electrodes; Galerkin method; Karhunen-Loeve Galerkin method; Li-ion batteries; Lithium; Lithium-ion batteries; Mathematical model; Mathematical models; Method of moments; Modelling; Parameter identification; Parameter uncertainty; Partial differential equations; Physical properties; Rechargeable batteries; spatiotemporal separation; Temperature distribution
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3030719

The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.