Lithium-ion batteries constitute the cornerstone technology for contemporary energy storage systems, particularly in automotive applications, due to their superior energy density, extended operational lifetime, and exceptional efficiency characteristics. However, thermal management represents a critical engineering challenge, as heat generation during electrochemical cycling significantly impacts performance, safety margins, and long-term reliability. Elevated operational temperatures necessitate sophisticated thermal management architectures, while the complex coupling between electrochemical and thermal phenomena presents substantial computational modeling challenges. This research addresses the critical need for accurate thermal prediction models in lithium-ion batteries by developing and validating two distinct modeling approaches for voltage and power estimation applied to a Samsung 53G 21700 Li-ion cell.