Commercial software tools have become essential for modeling and simulating lithium-ion batteries,
offering researchers and engineers powerful platforms to analyze battery behavior under various operating
conditions. Among the most widely used tools are MATLAB/Simulink, COMSOL Multiphysics, and
ANSYS Fluent, each offering unique capabilities for electrical and thermal simulations. MATLAB/
Simulink is popular for its user-friendly environment and fast execution of simplified lumped-parameter
models, while COMSOL Multiphysics and ANSYS Fluent enable detailed, physics-based simulations
with higher spatial resolution. In this paper, the performance of MATLAB/Simulink, COMSOL
Multiphysics, and ANSYS Fluent is compared by modeling and simulating a lithium-ion polymer battery
cell designed for electric vehicle applications. All the parameters required for model development are
obtained from experimental data. MATLAB/Simulink is used to simulate the effects of terminal current
and ambient temperature on the battery’s discharge voltage and usable capacity. COMSOL Multiphysics
and ANSYS Fluent are employed to simulate cell terminal voltage and temperature distribution profiles
over the battery cell surface under various continuous charge and discharge conditions. Additionally,
ANSYS Fluent is used to model the surface temperature distribution caused by an internal short-circuit
resulting from foreign object penetration. The simulation results are validated using experimental data,
showing good agreement for both electrical and thermal behavior under different loading and ambient
conditions. The comparison highlights that MATLAB/Simulink is particularly well-suited for quick,
zero-dimensional lumped simulations of battery electrical and thermal responses due to its simplicity
and computational efficiency. In contrast, COMSOL Multiphysics and ANSYS Fluent involve more
complex model setups but offer significant advantages for performing detailed three-dimensional
simulations, capturing spatial distributions of current and temperature across the battery cell. These
insights are valuable for selecting the appropriate simulation tool based on the complexity and goals of
battery modeling applications.
