<p>We propose a parameter identification method for heterojunction (HIT) solar cells based on the Pivora function and multi-strategy differential cooperative optimization algorithm (MSDCOA) to address the problems of relying on a large amount of measured data and being vulnerable to environmental noise in the parameter identification of HIT solar cells, as well as the insufficient convergence accuracy and proneness to falling into local optima of traditional intelligent algorithms in nonlinear multi-parameter optimization. This method utilizes the characteristic parameters provided by the manufacturer, such as short-circuit current, open-circuit voltage, and current and voltage at the maximum power point, to construct a smooth theoretical I–V curve of HIT cells through piecewise Pivora function fitting. It replaces the measured curve that is susceptible to interference, reduces the impact of measurement errors on the identification results, and achieves the purpose of accurately modeling the I–V curve of HIT cells without experiments. Experimental results show that the average errors of the theoretical I–V curve are only 0.55% and 0.81%, indicating high fitting accuracy. Subsequently, MSDCOA is adopted to solve the optimal parameters of HIT solar cells. Compared with traditional intelligent optimization algorithms, the root mean square error of parameter identification by MSDCOA is as low as 0.005519, 0.008483, 0.009900, and 0.009917, respectively, showing better performance. The proposed method does not rely on a large amount of measured data, has both high accuracy and high stability, and provides a reliable new approach for the parameter identification of HIT solar cells.</p>