A stack pressure based equivalent mechanical model of lithium-ion pouch batteries. (15th April 2021)

• Record Type: Journal Article
• Authors: Jiang, Yihui; Xu, Jun; Hou, Wenlong; Mei, Xuesong
• Is Part Of: Energy. Volume 221(2021)
• Publication Date: 2021-04-15
• Subjects: Equivalent mechanical model -- Stack pressure -- Battery thickness -- State estimation -- Li-ion battery; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.119804
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The stack level mechanical pressure will be inevitably generated among the battery cells, which dramatically affects the battery properties. To accurately characterize the battery performance with such aspects, an equivalent mechanical model with full consideration of stack pressure is proposed in this paper. Consisting of a small amount of simple mechanical components, the proposed model is simple but accurate, which is suitable for real time applications. By measuring the stack pressure, the battery thickness can be predicted by the proposed model, and accordingly, the battery states can be estimated. So the proposed equivalent mechanical model provides a potential use of the force signal based battery state estimation. The characteristics of the battery thickness variations caused by the applied force are first analyzed and the equivalent mechanical model is proposed. Furthermore, the coupling of stack pressure and the battery thickness is incorporated to the proposed model. Then a reliable parameter identification method is proposed to obtain the model parameters under different conditions. On this basis, the accuracy and the adaptability of the proposed model are experimentally validated. Highlights: An equivalent mechanical model for Li-ion pouch batteries is developed. The equivalent mechanical model is simple and applicable for battery packs. The coupling of stack pressure and battery thickness is analyzed. The model can accurately predict battery thickness from stack pressure. The model makes the battery state estimation by force measurements more feasible.