# Enhancing Supercapacitor Simulation Accuracy Through a Novel Hybrid Modeling Approach
## 1 Introduction
The factors that are mainly discussed as essential for supercapacitors to operate dependably, effectively, and resiliently are state of charge (SOC), state of health (SOH), and self-discharge [10; 13; 19; 23]. Since supercapacitors have low internal resistance, in the majority of cases, the thermal effect is neglected. In some cases, self-discharge phenomena are also neglected because, usually, the supercapacitors are used in applications for short-term and mid-term energy storage systems [28]. SOC and SOH are the most studied because of their important impact on the modeling results. A detailed description of the most used modeling methods for SCs is presented in Chapter 2 of the paper. All these modeling methods are based on the identification of the parameters that govern them and to obtain the model parameters. Numerous approaches with differing degrees of accuracy and complexity of execution have been proposed. Hence, the simplest methods rely on estimating the model parameters after conducting experimental data acquisition [26; 28; 29; 30; 31]. Some of these approaches use short-term or long-term behavior for modeling the SC or either. Implementation is done using different estimation techniques, such as the least-square method, or different algorithms to minimize the mean-square error between the model response and experimental data. The latest research papers compare several established supercapacitor models by employing diverse identification current pro files and parameter identification methods. This is necessary due to the difficulty in arriving at definitive conclusions about the most suitable model for specific applications [32]. In recent years, the field of supercapacitor modeling has witnessed significant advancements, fueled by the growing demand for efficient energy storage solutions. However, existing research often focuses on general modeling techniques and parameter estimation methods that are dependent on the accuracy and hardware performance that is used in the process of data collection. These could lead to poor results concerning the model reproduction of the SC dynamics, which could lead to less effective use for hybrid energy storage applications, for example. As a result, there is still a strong scientific interest in this subject. The paper proposes a new method for modeling the SCs called the Hybrid Modeling Approach for Supercapacitors (HyMASC). The method offers a novel approach that integrates the well-established [PERSON]'s modeling and parameter estimation method with the MATLAB \"Parameter Estimator\" application, coupled with Simscape modeling through MATLAB/Simulink. HyMASC improves the results of [PERSON]'s modeling and parameter estimation method [33], which has become increasingly popular in recent years as can be seen in the literature [8; 34]. By combining the two methods, HyMASC has the ability to compensate for the deviations in calculating the parameters using classic modeling methods or due to experimental data limitations (DAQ accuracy, signal noise, etc.). The experimental and numerical results showed a decrease in MRE and MAE errors using HyMASC in comparison with the [PERSON] method. The paper also highlights the advantages brought by the use of Simscape components in modeling SCs. By exploiting this hybrid methodology, our study aims to offer a more accurate modeling procedure of SC behavior that can be further used in the development of optimized energy management strategies, enhanced system performance, and prolonged lifespan of energy storage units. The proposed method is tested and validated by experimental data acquired from two supercapacitors: 300 F, 2.7 V and 400 F, 2.7 V from Eaton Electronics. The testing setups for the supercapacitor modeling have been developed under the specified conditions mentioned in the literature [33].