Accurate assessment of electrochemical parameters in proton exchange membrane (PEM) fuel cells is essential for monitoring system health, understanding degradation stressors, and optimizing dynamic operation. However, conventional characterization methods often require intrusive experiments or offline post-processing, limiting their applicability during real-world operation. To address this challenge, this work presents a parameter estimation algorithm using a zero-dimensional, physics-based PEM fuel cell performance model designed to enable fast and reliable estimation of key electrochemical parameters, such as exchange current density, ohmic resistance, and cross-over current, directly from measurement data recorded during dynamic operation. The model captures the dominant electrochemical processes through physically motivated formulations of activation, ohmic, and concentration losses, while maintaining a structure that supports efficient parameter identification. By integrating a numerically robust fitting procedure, the framework enables online extraction of parameter sets that reflect the current state of the cell directly correlated to material properties. This capability provides a pathway toward online degradation monitoring and model-based diagnostics. Validation of the proposed approach is performed using both in-situ measurement and ex-situ characterization results from literature, including electrochemical impedance spectroscopy and polarization-curve analyses. The comparison demonstrates that the zero-dimensional model reliably reproduces observed voltage-current behavior across a broad range of operating conditions, while the identified parameters show consistent trends correlated with known degradation mechanisms. The presented methodology highlights the potential of simplified yet physics-informed performance models to bridge the gap between high-fidelity electrochemical understanding and practical system integration. By enabling rapid parameter characterization during operation, the framework supports the development of online health assessments and provides a valuable tool for analyzing degradation in PEM fuel cell systems under realistic dynamic environments.