Thermal management of lithium-ion battery pack in hybrid electric vehicles (HEVs) and electric vehicles (EVs) have become a research focus in recent years because performance, safety and life time of battery have significant dependency on temperature distribution throughout the cell. As the first step in designing a reliable thermal strategy, heat generation of battery must be computed accurately. But the most effective physical properties involved in heat source may not be exactly known, possibly because of intrinsic variability which cannot be measured directly in practice.

In any simulation process, some uncertainties appear whose understanding and quantification is critical to assess the differences between the numerical predictions and actual system behavior. For this purpose, these uncertainties must be quantified and propagated through energy equation and heat flux to roll up the uncertainties of interest in order to determine range of temperature distribution, and consequently improve the safety and increase the life-cycle of lithium-ion battery.

Commonly, the uncertain model parameters are represented by random variables/processes which are known as probabilistic techniques. In the present study, stochastic spectral methods based on polynomial chaos (PC) expansions is employed due to their advantages over traditional uncertainty quantification (UQ) techniques, i.e., perturbation-based and Monte Carlo sampling (MCS) methods. In PC-based method, the coefficients of the solution expansion are computed intrusively or non-intrusively. Non-intrusive methods depend on individual realizations to determine the stochastic model response to random inputs and consume less time in comparison with intrusive methods. Since governing equations of lithium-ion battery are very complex, non-intrusive method is chosen in the present study.

Numerical simulation results are verified by discharge process at different C-rates which show good agreement with previous studies. Moreover, PC-based model is implemented to study the effects of parametric model uncertainties on the heat generation of cell and temperature distribution. The obtained numerical results can be used to design more efficient thermal management strategy. Moreover, the numerical results show that the proposed UQ method can accurately compute the variability in the output quantity of interest such as the heat generation and temperature distribution within a wide range of heat convection coefficients for lithium-ion battery.