Impact of Numerical Methods in Thermal Modeling of Li‐Ion Batteries on Temperature Distribution and Computation Time

Abstract

Thermal battery modeling is important for further battery development and optimization. The temperature strongly influences the performance and aging behavior. In the cell stack, electrochemical processes take place resulting in a large amount of heat release, which, in turn, affects the temperature distribution. Therefore, the main focus is on the cell stack, the most complex structure inside the cell. In particular, the discontinuous and anisotropic material properties represent a major challenge for simulations due to the layering. This work proposes self‐developed methods, based on the Finite Volume Method and the Finite Element Method, taking on these challenges. First, for both methods the functionality is verified and numerical convergence is validated. These, and also classical methods, are compared based on test problems with a known analytical solution in view of numerical errors as well as computing time. It if found that their accuracy and efficiency depends strongly on the specific problem, which makes their numerical investigation necessary and inevitable. Second, the methods are evaluated on a specific battery problem. Their results are plausible and correspond to the physical phenomena.

State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy

“…Thermal factors are not only indicators of the thermal stability of battery materials but also have an important influence on the physical and chemical reactions inside the battery. [ 27,28 ] During operation, the thermal effect of the batteries influences electrochemical properties (charging and discharging performance, internal resistance, etc. ), and greatly affects the safety and lifespan.…”