## 1 Introduction
The authors aimed to improve voltage simulation and estimation techniques to reduce errors. [PERSON] et al. [22] introduced an algorithm to improve the real-time performance of SOC estimation filters by solving for suboptimal fading factors, crucial for maintaining filter effectiveness under varying conditions. [PERSON] et al. [23] proposed a novel approach combining maximum correntropy criterion (MCC) with EKF, called C-WLS-EKF. Experimental results show that the C-WLS-EKF significantly reduces SOC estimation errors compared to traditional EKF methods, achieving a mean square error (MSE) reduction from 1.361% to 0.512%. The authors stated the importance of accurate SOC estimation in electric vehicles for battery protection, efficiency, and longevity. In the current literature, various approaches have been proposed for SOC estimation and ECM parameter identification for lithium-ion batteries. These methods typically focus on specific optimization techniques and limited test profiles, often without considering the combined effect of multiple variables like temperature, SOC levels, and operational conditions. While the literature provides significant insights into battery modelling and estimation methods, it lacks comprehensive studies that address the following gaps. [PERSON] et al. [8] and [PERSON] et al. [17] focused on the impact of pulse width or rest time during HPPC tests. They explored how pulse width and rest time affect identification accuracy but did not comprehensively examine the effects of multiple HPPC parameters, such as pulse time gap, discharge pulse time, and C-rate, under various conditions. Also, the simultaneous effects of multiple HPPC parameters on ECM performance under different environmental conditions (e.g., low and high ambient temperatures) were not investigated by the researchers. While temperature is recognized as a critical factor affecting lithium-ion battery performance, studies such as those by [PERSON] et al. [12] and [PERSON] et al. [13] generally focused on low or high temperatures without considering intermediate conditions. They rarely explore how temperature interacts with SOC and C-rate to influence the overall model accuracy. There is limited research on the combined effects of temperature, SOC, and operational variables (e.g., C-rate) on ECM parameterization and SOC estimation accuracy. Most of the existing work, such as [PERSON] et al. [9] and [PERSON] et al. [11], focused on single-objective optimization techniques for ECM parameter identification. While these methods improve certain aspects of model accuracy (e.g., reducing RMSE for specific SOC levels), they may fail to balance competing objectives, such as minimizing RMSE at different SOC levels and temperatures simultaneously. To fill these research gaps, this study examines varying levels of four widely utilized HPPC parameters, specifically focusing on discharge parameters: pulse time gap, discharge pulse time, discharge pulse C-rate, and rest time. In this context, the Taguchi method (DoE) was applied to set the research plan, and a statistical analysis (ANOVA) was performed to evaluate the results. This investigation includes conducting HPPC tests at different operating temperatures (\(-10\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, \(40\)\({}^{\circ}\)C) and obtaining equivalent circuit models (ECMs) for diverse combinations of these parameters. The selection of the most suitable parameters for each test was facilitated by employing RMSE (root mean square error) values. These values, computed from the differences between experimental and simulated data, functioned as quality metrics in the evaluation process. Analysing the effect of each parameter variation on model performance offers valuable insights, aiding in the selection of optimal parameters for a generic ECM. Additionally, after the parameters were determined based on the model that minimizes the RMSE value, the EKF algorithm was utilized for SoC estimation, which optimizes the model accuracy and the estimation performance with simultaneous iterations.