A particle filter-based approach for real-time temperature estimation in a lithium-ion battery module during the cooling-down process

• Published in: Journal of energy storage Vol. 94; p. 112413
• Main Authors: Paccha-Herrera, Edwin, Jaramillo-Montoya, Francisco, Calderón-Muñoz, Williams R., Tapia-Peralta, Darwin, Solórzano-Castillo, Byron, Gómez-Peña, Julio, Paccha-Herrera, Jackson
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.07.2024
• Subjects: Battery thermal management; Bayesian filtering; CFD; Temperature measurements; CFD; Battery thermal management; Temperature measurements; Bayesian filtering
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2024.112413

Temperature exerts a remarkable influence on the safety, performance, and lifespan of lithium-ion (Li-ion) batteries. Consequently, the real-time monitoring of this variable within battery cells emerges as both crucial and a significant challenge for battery thermal management systems. This paper introduces an approach for real-time temperature estimation in a Li-ion battery module during the cooling-down process by applying the particle filter (PF) technique. A battery module comprising fifteen cylindrical lithium cobalt oxide cells connected in series was employed for experimentation. The battery module was arranged into three rows, the PF employs real-time temperature measurements from the first cell of each string, based on a fractal-time model and an artificial evolution approach for parameter estimation, to estimate two unknown parameters and the temperature of the remaining twelve cells. Furthermore, the temperature measured on the surface of each cell in the module was compared both the PF and computational fluid dynamics (CFD) approach. The results exhibit a favorable agreement between the cell temperature estimates derived from the PF, the CFD simulations, and their respective experimental measurements. Finally, the proposed approach facilitates real-time temperature monitoring in battery modules with a reduced number of temperature sensors, thereby implying a cost reduction in the data acquisition system. •Application of Bayesian filtering to estimate temperature of cells in a battery module.•Real-time monitoring of temperature in a battery module.•A lumped model with a fractal formulation was employed as a process equation.•Numerical model solved using ANSYS Fluent.