To address the issues of low accuracy, slow convergence, and difficult data acquisition in intelligent search algorithms for solar cell parameter estimation, we propose a method that combines second-order Bézier curves with an enhanced Squirrel Search Algorithm. First, the optimum Bézierr control point is found on the line that passes through the maximum power point and is parallel to the line of the open circuit voltage point and the short circuit current point. This approach leverages the relationship between control point positions and battery fill factor to achieve precise modeling of the I-V characteristic curve without the need for experiments. This method not only accurately describes the output characteristics of HIT cells but also effectively reduces the impact of measurement noise on parameter identification. Secondly, we introduce Sobol sequences, reverse learning, and chaos theory to improve the standard squirrel algorithm. Sobol sequences are integrated into the initialization process as quasi-random samples, and a reverse learning strategy enhances population diversity and search space coverage. Additionally, a tent chaotic mapping perturbs the optimal solution, enhancing the algorithm’s capability to escape local optima. The improved squirrel optimization algorithm is applied to heterogeneous junction solar cell parameter estimation and compared with other intelligent optimization algorithms. The results showed that the improved algorithm achieved root mean square errors of 0.028 25, 0.017 458, and 0.023 61, respectively, indicating the highest accuracy. This demonstrates the effectiveness and accuracy of the algorithm in the parameter identification of heterojunction solar cells, providing a reliable and precise new method for solar cell parameter identification.