## 1 Introduction  
Under the \"Dual Carbon\" policy objectives, ESSs have become indispensable components in power systems with high renewable energy penetration, owing to their flexible power regulation capabilities [1, 2, 3]. With the widespread adoption of grid-forming control and millisecond-level inertia response technologies, the dynamic characteristics of ESSs during voltage/frequency emergency support scenarios directly impact grid transient stability [4; 5]. However, the control logic of energy storage converters (PCS) is highly complex, and the nonlinear coupling mechanisms between HVRT/LVRT parameters and dynamic responses remain insufficiently resolved, resulting in significant discrepancies between simulation models and actual device behaviors, posing risks to grid security. The FRT process of energy storage systems involves dynamic interactions across multiple timescale control loops. However, the core control parameters--such as converter PI controller gains and reactive power compensation slopes--are typically subject to commercial confidentiality restrictions imposed by manufacturers. The authors of existing studies primarily rely on idealized assumptions or simplified equivalent models for parameter identification, which fail to capture the transient coupling effects under real-world operating conditions (e.g., control loop interference caused by sudden power fluctuations), leading to insufficient accuracy in parameter inversion and significantly hindering the reliability assessment of energy storage systems in providing active grid support [6; 7; 8; 9]. The authors of recent studies on parameter identification in energy storage systems have mainly focused on batteries and electrochemical characteristics. For instance, the authors of References [10; 11] developed improved battery cell models that incorporate effects such as capacity degradation, ambient temperature, and current rate, forming the basis for state-of-charge (SOC) estimation in lithium batteries. The authors of Reference [12] identify parameters to produce a more accurate electrochemical model of lithium-ion batteries. In [13], a bias-compensated least-squares method is proposed for online parameter identification to develop a high-fidelity dynamic model of lithium batteries. The authors of Reference [14] introduce a new state estimation method for lead-carbon batteries based on the relationship between the open-circuit voltage and available energy at full discharge. In addition, the authors of [15] utilize a polynomial fitting approach to identify the system's inertia coefficient by fitting the measured power and frequency data at the point of common coupling. The results of the above studies reveal a clear research gap in identifying the control parameters of energy storage converters. Conventional parameter identification methods--such as frequency sweep analysis, single-objective gradient-based optimization, and least-squares techniques--present notable limitations [16; 17; 18; 19; 20; 21]:  
1. They rely heavily on prior knowledge of internal device models, making them incompatible with the \"black-box\" nature of commercial energy storage converter control strategies;
2. They struggle to capture the divergent dynamic trajectories induced by charge/discharge mode switching, resulting in poor parameter generalization;
3. Single optimization algorithms often fall into local optima, making it challenging to achieve parameter compatibility across multiple scenarios, such as HVRT and LVRT. Accurate identification of the FRT control parameters in ESSs through measurement-driven methods has emerged as a critical challenge for enhancing the credibility of digital simulations in renewable energy power systems. To address these challenges, we propose a measurement-driven parameter identification method for FRT control in ESSs, which integrates dynamic trajectory feature extraction with a hybrid optimization algorithm [22]. The main contributions of this work can be summarized as follows:  
1.