A transient multi-path decentralized resistance-capacity network model for prismatic lithium-ion batteries based on genetic algorithm optimization

• Published in: Energy conversion and management Vol. 300; p. 117894
• Main Authors: He, C.X., Liu, Y.H., Huang, X.Y., Wan, S.B., Chen, Q., Sun, J., Zhao, T.S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2024
• Subjects: Genetic algorithm; Lithium-ion batteries; RC networks; Thermal management; Thermal resistance networks; Lithium-ion batteries; Thermal management; RC networks; Genetic algorithm; Thermal resistance networks
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2023.117894

•An enhanced thermal network model with added thermal resistances and capacities.•Thermal resistances and heat capacities are determined via genetic algorithm.•The impact of added resistances and capacities on thermal response is discussed.•Temperature prediction error is reduced from 4.24 to 0.95 ℃. Battery thermal management is crucial for preventing the safety issues of lithium-ion batteries. Due to the simple modeling and fast calculation speed, the thermal resistance-capacity (RC) network model is broadly applied in the design of battery thermal management systems. However, the simplification of heat flow paths and the lumped definition of thermal capacity in traditional RC models result in large temperature prediction errors, which fail to reflect the thermal response in complex and diverse heat transfer situations. To improve the prediction accuracy, a decentralized centroid multi-path RC network model is constructed for a typical prismatic lithium-ion battery. This novel model incorporates multiple heat flow paths with additional thermal resistances and legitimately decentralizes the lumped heat capacity to other surface center points, resulting in a more realistic thermal response. A genetic algorithm is employed to determine the unknown thermal resistances and heat capacities at the attributed nodes. Results show that compared to the traditional RC network model, the multi-path decentralized RC network model can reduce the temperature prediction error from 4.24 to 0.95 ℃. This more refined modeling approach extends the application scope of thermal resistance network models to more complex scenarios while maintaining efficient simulation speed, which enables the attainment of more accurate and reliable onboard temperature predictions for lithium-ion power systems.