Accurate estimation of internal parameters in lithium-ion batteries is essential to develop reliable battery model, ensure safety and performance. This study presents a comprehensive approach to identify the parameters of a second-order equivalent circuit model by integrating experimental testing with advanced optimization techniques. To create a robust dataset, Open Circuit Voltage and Hybrid Pulse Power Characterization tests are conducted under two thermal conditions: natural-cooled and immersion-cooled. These experiments are carried out under three different C-rates: low, medium, and high, to capture the dynamic response under varying loads and temperatures of the battery. The battery parameters are optimized with the Harris Hawks Optimization (HHO) algorithm, aiming to reduce the error between the simulated and measured terminal voltages. Model validation was performed by comparing voltage outputs before and after optimization, in terms of root mean square error (RMSE), mean absolute error, and maximum error. Furthermore, the proposed method was benchmarked in contrast to optimization algorithms, such as Particle Swarm Optimization, Cuckoo Search Algorithm (CSA), and Improved CSA. In comparison to listed optimization algorithms, HHO offers faster convergence and robust to initial parameter guesses. However, existing methods, including PSO and Cuckoo Search, often suffer from premature convergence, sensitivity to initial conditions, and high computational costs, limiting their accuracy for complex dynamics of the battery. The HHO-based model achieved the lowest RMSE value of 0.06% under natural-coolant conditions, which highlights the effectiveness in the parameter estimation. This work demonstrates the significance of thermal management using HHO as a promising tool to enhance the accuracy in parameter estimation of the battery model.