Parameterization and Observability Analysis of Scalable Battery Clusters for Onboard Thermal Management

Xinfan Lin (1) , Huan Fu (1) , Hector E. Perez (1) , Jason B. Siege (1) , Anna G. Stefanopoulou (1) , Yi Ding (2) , Matthew P. Castanier (2)

1 University of Michigan [Ann Arbor]
2 Tardec US Army

Although the battery surface temperature is commonly measured, the core temperature of a cell may be much higher hence more critical than the surface temperature. The core temperature of a battery, though usually unmeasured in commercial applications, can be estimated by an observer, based on a lumped-parameter battery thermal model and the measurement of the current and the surface temperature. Even with a closed loop observer based on the measured surface temperature, the accuracy of the core temperature estimation depends on the model parameters. For such purpose, an online parameterization methodology and an adaptive observer are designed for a cylindrical battery. The single cell thermal model is then scaled up to create a battery cluster model to investigate the temperature pattern of the cluster. The modeled thermal interconnections between cells include cell to cell heat conduction and convection to the surrounding coolant flow. An observability analysis is performed on the cluster before designing a closed loop observer for the pack. Based on the analysis, guidelines for determining the minimum number of required sensors and their exact locations are derived that guarantee the observability of all temperature states.

Domaines: Physique [physics]

Xinfan Lin, Huan Fu, Hector E. Perez, Jason B. Siege, Anna G. Stefanopoulou, et al.. Parameterization and Observability Analysis of Scalable Battery Clusters for Onboard Thermal Management. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2013, 68 (1), pp.165-178. ⟨10.2516/ogst/2012075⟩. ⟨hal-01935947⟩