## 1 Introduction  
The electric vehicle (EV) market is witnessing rapid expansion, with sales exceeding 10 million units in 2022. This surge represented 14% of all new car sales, a significant jump from around 9% in 2021 and less than 5% in 2020. The momentum in electric vehicle sales is projected to maintain its strength throughout 2023, with forecasts indicating that sales could reach 14 million by the end of the year, accounting for 18% of total car sales for the entire year [1]. Recently, the power battery, which is a crucial component of EVs, has garnered significant attention. To improve battery performance, extend battery life, and optimize behaviour in applications, the implementation of a battery management system (BMS) is crucial. The BMS plays an essential role in monitoring and managing various aspects of the battery's operation, ensuring efficient performance and protecting against potential issues, thereby enhancing the overall reliability of the system [2]. For the BMS to perform these functions effectively, a precise evaluation of the battery's state is necessary, including parameters like the State of Charge (SOC). This level of accuracy is facilitated using a battery model. Battery models are typically divided into four categories: data-driven models (including extreme learning machine models, support vector machine models, and neural networks), empirical models (such as the Nernst model, Unnewehr universal model, and Shepherd model), electrochemical models (like the single particle model and pseudo-2D model), and equivalent circuit models (including the Thevenin model, dual-polarization model, and Rint model). These models provide the necessary frameworks to accurately assess and manage battery performance [3]. The equivalent circuit model (ECM) has become widely utilized in practical applications due to its straightforward parameterization, effective representation of Li-ion battery characteristics, and cost-effectiveness [4]. ECM models are highly effective in estimating the nonlinear behaviour of batteries and in parameterizing model variables based on factors, such as temperature, State of Charge (SOC), State of Health (SOH), and C-rate. The influence of these factors on battery performance can differ significantly depending on the specific chemistry and manufacturing processes of the battery [3]. Since ECM parameters are not physical attributes and cannot be measured directly, they must be identified through an optimization algorithm once the battery model is established. There are two principal methods for this identification: offline and online. Offline identification involves determining the parameters based on the State of Charge (SOC) through controlled experiments under specific operational conditions. Various testing profiles can be employed for this parameter identification, including the pulse test [5], hybrid pulse power characterization (HPPC) test [6], and drive cycle test [7,8]. Before using the HPPC test for ECM parameter identification, it is crucial to analyse the correlation between each HPPC parameter and the model's accuracy. This analysis is essential to understand the impact of each parameter on the overall accuracy of the model. However, there is limited research in this field. [PERSON] et al. [8] explored how various HPPC profiles affect identification accuracy, specifically examining the pulse width of the simulating current and the rest time. [PERSON] et al. [4] examined how the State of Health (SOH) impacts the parameters of the equivalent circuit model (ECM) utilized for lithium-ion batteries. [PERSON] et al. [9] identified the parameters of the equivalent circuit using the particle swarm optimization (PSO) algorithm for a lithium-ion battery cell using the results obtained from HPPC tests. [PERSON] et al. [10] assessed the impact of different test profiles on ECM accuracy by comparing various pulse test lengths, two identification methods, and a combination of pulse and drive cycle tests, finding that application optimization yields the highest accuracy.