Accurate estimation of volumetric heat generation of a li-ion battery is crucial for thermal modeling, thermal safety, and the design of thermal management systems for electric vehicles. Existing experimental methods for measuring the volumetric heat generation of li-ion batteries are expensive and time-consuming, lacking insight into sensitivity to operating conditions and hotspot locations. The primary objective of this work is to develop a novel inverse heat transfer method-based numerical model where commercial solver ANSYS transient thermal module can be integrated with genetic algorithm to determine internal heat generation in a domain if temperature data are provided. The experimental temperature data collected from the research article is taken as a reference input and the proposed model is used to estimate the internal heat generation rate (QHg) of a li-ion battery. The results demonstrate the effectiveness of the inverse method in estimating the heat generation rate, with the mean percentage error between estimated and experimental temperatures ranging from 3.31 to 5.67% and for the estimated heat generation rate was 11.23%, showing a considerable agreement with the values reported in the referenced study.