Comparison of Equivalent Circuit Model Parameterization Using GITT and EIS

Comparison of Equivalent Circuit Model Parameterization Using GITT and EIS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of Equivalent Circuit Model Parameterization Using GITT and EIS Marek Sedlařík, Petr Vyroubal, Jakub Lysák, Martin Mačák, Martin Šedina, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7885669/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Monatshefte für Chemie - Chemical Monthly → Version 1 posted 4 You are reading this latest preprint version Abstract Accurate parameterization of Equivalent Circuit Models (ECMs) is essential for reliable state estimation and dynamic simulation of lithium-ion (Li-ion) batteries. These models provide a representation of the electrochemical processes governing cell behavior and are widely applied across various fields, including electric mobility, stationary energy storage, avionics and industrial applications. This work investigates two commonly employed approaches for ECM parameter identification: Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical Impedance Spectroscopy (EIS). Both methods were applied to Li-ion cells under controlled laboratory conditions and the parameters of a 3RC model were extracted and analyzed. The study compares time-domain and frequency-domain parameterization, addressing their methodological differences, measurement requirements and resulting model characteristics. To demonstrate practical applicability, the identified models were validated using real racing data from the TU Brno Racing Formula Student electric vehicle. Li-ion battery Galvanostatic Intermittent Titration Technique Electrochemical Impedance Spectroscopy Equivalent Circuit Model State of Charge Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 6 Figure 7 Figure 7 Figure 8 Figure 9 Figure 11 Figure 12 Figure 21 Figure 25 Introduction Lithium-ion (Li-ion) batteries represent a fundamental element of modern energy storage, offering a combination of high energy density, long cycle life, and stable electrochemical performance [ 1 ]. Their role is particularly critical in applications that demand efficient and reliable power delivery, such as electric vehicles, avionics and stationary storage systems [ 2 , 3 ]. With the continuous expansion of global energy storage demand, the necessity of accurate modeling of Li-ion battery behavior has become increasingly evident. Such models provide a basis for optimizing performance, ensuring operational safety and extending operation life, while also supporting advanced functionalities including diagnostics, control and predictive maintenance in battery management systems. Equivalent Circuit Models (ECMs) are commonly applied to describe the behavior of electrochemical systems [ 4 ]. Lower-order structures, such as 1RC and 2RC models, are often sufficient for basic applications, but their accuracy decreases when several dynamic processes interact within the cell [ 5 , 6 ]. In this work, 3RC configuration is adopted, as shown in Fig. 1 . The model consists of a series ohmic resistance representing instantaneous voltage drop and three parallel RC branches associated with fast interfacial processes, charge-transfer phenomena and slower diffusion effects. [ 7 , 8 , 9 ] The terminal voltage of the cell can be formulated as the sum of the open-circuit voltage and the dynamic overpotentials associated with the RC elements. For the 3RC structure shown in Fig. 1 , the voltage response is given by $$\:U\left(t\right)={U}_{\text{O}\text{C}\text{V}}-{R}_{s}I\left(t\right)-\sum\:_{i=1}^{3}\:{U}_{i}\left(t\right)$$ 1 where U OCV denotes the open-circuit voltage, R S represents the ohmic resistance, I(t) is the applied current, and U i (t) corresponds to the voltage drop across the i-th RC branch. The evolution of each polarization voltage U i (t) is governed by a first-order differential equation [ 11 ]. $$\:\frac{d{U}_{i}\left(t\right)}{dt}=-\frac{1}{{R}_{i}{C}_{i}}{U}_{i}\left(t\right)+\frac{1}{{C}_{i}}I\left(t\right)$$ 2 with the time constant of each branch defined as $$\:\tau\:\:\left(t\right)={R}_{i}{C}_{i}$$ 3 This configuration captures polarization over multiple timescales and provides a more detailed description of cell dynamics than lower-order models. In the paper by Murarka et al. [ 12 ], the ECM was applied to assess Li-ion batteries in stationary energy storage systems. The model was used to evaluate performance and safety under different load conditions, highlighting its role in supporting state estimation and operational strategies for grid applications. The accuracy of an ECM strongly depends on proper parameter identification, as resistance and capacitance values vary with temperature, State-of-Charge (SOC) and capacity fade of the cell. Among the most widely applied methods are the Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical Impedance Spectroscopy (EIS). GITT provides high resolution of long-term polarization dynamics by analyzing voltage relaxation after current pulses [ 13 , 14 ] and can be implemented with relatively simple laboratory equipment. EIS, on the other hand, offers superior capability in separating processes over a broad frequency spectrum, allowing the identification of ohmic resistance, charge-transfer kinetics and diffusion-related behavior within a single measurement [ 8 , 9 ]. This makes EIS particularly powerful for detailed diagnostics and the construction of higher-order ECMs, although it requires specialized impedance analyzers and careful experimental design. In the paper [ 13 ] by LeBel et al., the GITT method was applied to nickel-manganese-cobalt (NMC) Li-ion cells to identify parameters of ECMs. The study focused on the development of 1RC and 2RC configurations, demonstrating that GITT can be used to generate accurate model response surfaces and improve parameter fidelity compared to pulse-based identification methods. In research [ 9 ] by Meddings et al., EIS was used to parameterize ECMs of Li-ion cells. By fitting impedance spectra over a wide frequency range, the method captured ohmic, charge-transfer and diffusion processes, demonstrating its effectiveness for multi-RC models. This work aims to directly compare the use of GITT and EIS for parameterization of a 3RC ECM of Li-ion batteries. The study evaluates the accuracy and applicability of both approaches and discusses their advantages and limitations. To demonstrate practical relevance, the parameterized model is validated using measurement data obtained from the TU Brno Racing Formula Student electric vehicle during race. Experimental The experiment was performed on Li-ion pouch cells with lithium-cobalt oxide (LCO) as the cathode material and graphite as the anode material. The tested cell was a Grepow 15.4 Ah battery with dimensions of 7.5 × 79.5 × 207 mm and a weight of 263 g. Both EIS and GITT measurements were conducted at temperatures of 22, 30, 40, and 50°C. Table 1 Parameters of Grepow 7579207 battery. Parameter Value Type Pouch Nominal Capacity 15.4 Ah Nominal Voltage Maximum Voltage Minimum Voltage Maximum Current 3.8 V 4.35 V 3 V 10 C GITT parametrization As a first approach, the GITT was employed to parameterize the ECM. The applied protocol consisted of repeated discharge pulses with a duration of 5 minutes, each followed by a 1 hour relaxation period to allow the cell voltage to stabilize before the next step. Preliminary measurements confirmed that extended relaxation is necessary to ensure consistent extraction of RC parameters. The test was performed under controlled laboratory conditions, and the procedure is schematically illustrated in Fig. 2 . The sequence started with a Constant-Current (CC) charge at 0.2 C until the cell voltage reached 4.3 V, followed by a Constant-Voltage (CV) step at 4.3 V until the current decreased to 0.05 C, corresponding to 0.77 A. After a 1 hour rest, the discharge phase was initiated using CC pulses at 0.3 C for 5 minutes, each separated by 1 hour relaxation intervals. This cycle was repeated until the cut-off voltage of 3.0 V was reached, which marked the end of the experiment. In total, 38 pulses were obtained throughout the experiment. The final pulse was omitted from the analysis, since the cell reached the cut-off voltage and could no longer provide reliable data for RC parameter extraction. The overall duration of the test, not including the initial charging step, was approximately 41 hours. The parameter identification process relies on analyzing the cell’s voltage response to controlled current pulses using the GITT. The ECM structure consists of a series resistance R S and three RC pairs, enabling refined characterization of both fast and slow transient processes occurring within the cell. A critical element of the model is the series resistance Rs , which accounts for ohmic losses such as electrolyte resistance and contact resistances at the electrode interfaces [ 15 , 16 , 17 ]. Its value is determined from the instantaneous voltage drop at the start of each current pulse, as illustrated in Fig. 3 . This immediate drop is attributed to the purely resistive components of the cell. Beyond the ohmic contribution, the battery exhibits exponential relaxation behavior after each current step, reflecting charge-transfer and diffusion-related processes. To capture these effects, a 3RC ECM configuration is employed, where each RC pair represents a distinct transient mechanism. The first RC branch ( R1, C1 ) is typically associated with fast interfacial charge-transfer dynamics, the second branch ( R2, C2 ) with intermediate processes, and the third branch ( R3, C3 ) with slower diffusion and redistribution within the electrode material [ 10 ]. Accurate determination of the ECM parameters requires an iterative optimization process that minimizes the difference between the simulated and measured voltage responses. Because the relaxation behavior of the cell is governed by several dynamic processes, each described by a characteristic time constant τ i , the initial parameter values were empirically estimated and constrained within physically reasonable limits: $$\:{\tau\:}_{min,i}\le\:{\tau\:}_{i}\le\:{\tau\:}_{max,i}$$ 3 These limits define the admissible range for the optimization algorithm, which aims to minimize the squared deviation between experimental and simulated data according to the following objective function: $$\:\underset{{R}_{i},{C}_{i}}{min}\:\sum\:_{k}\:{\left({U}_{\text{exp}}\left({t}_{k}\right)-{U}_{\text{sim\:}}\left({t}_{k}\right)\right)}^{2}$$ 4 where U exp (t k ) denotes the experimentally recorded voltage at time step t k and U sim (t k ) is the corresponding value generated by model. During the iterative fitting procedure, the resistance and capacitance of each RC branch are continuously adjusted until the simulated relaxation curve accurately reproduces the measured response. The resulting parameter set was validated through direct comparison between simulated and experimental voltage profiles, as shown in Fig. 4 . The strong agreement between both confirms that the optimized 3RC model reliably captures the transient behaviour of the cell. The residual voltage error remains consistently low throughout the measurement sequence, indicating the robustness and precision of the applied parameter identification method. EIS parametrization The second method applied for ECM parameterization was EIS. Charging and discharging were performed at a current rate of 0.2 C This current rate was selected as an optimal balance between the overall duration of the experiment and the precision of the recorded data, considering that the tested cell is designed for high-current operation. Measurements were performed at defined intervals of 15% SOC, starting from the fully charged condition down to 10% SOC using CC discharge. To extend the characterization to lower SOC values, additional measurements were taken at 5% and 0% SOC. Since the cell was new and exhibited negligible voltage drop during low-current operation, the measurement at each step was time-limited rather than voltage-limited, with the 5% SOC step corresponding to approximately one hour of discharge. Impedance data were collected over a frequency range of 30 mHz to 10 kHz with an excitation amplitude of 10 mV. Five logarithmically spaced points per frequency decade were recorded, and each spectrum represents the mean of three consecutive measurements to ensure repeatability and minimize random deviations. Following the impedance measurements, the obtained spectra were subjected to numerical fitting based on a 3RC ECM. The fitting and optimization process was implemented to accurately identify the RC components corresponding to individual electrochemical processes. The primary objective of this procedure was to minimize the deviation between the experimentally measured impedance Z exp (ω) and the impedance predicted by the simulation Z sim (ω) . The 3RC configuration (depicted in Fig. 1 ) comprises a series resistance R S and three parallel RC branches, each representing a distinct electrochemical process characterized by a specific time constant. The total complex impedance of the model is given by [ 7 , 18 ]: $$\:{Z}_{sim}\left(\omega\:\right)={R}_{S}+\frac{{R}_{1}}{1+j{\omega\:R}_{1}{C}_{1}}+\frac{{R}_{2}}{1+j{\omega\:R}_{2}{C}_{2}}+\frac{{R}_{3}}{1+j{\omega\:R}_{3}{C}_{3}}$$ 5 where j is the imaginary unit and ω = 2πf represents the angular frequency. To identify optimal parameter values, an iterative optimization algorithm was implemented. The objective of the procedure is to minimize the normalized squared error between the measured and simulated impedance magnitudes across all frequencies, as defined by [ 19 , 20 ]: $$\:{\chi\:}^{2}=\sum\:_{k=1}^{N}\frac{\left|{Z}_{\text{exp}}\left({\omega\:}_{k}\right)-{Z}_{\text{sim\:}}\left({\omega\:}_{k}\right)\right|}{{{Z}_{\text{exp}}\left({\omega\:}_{k}\right)}^{2}}\:$$ 6 where N is the total number of frequency points. The algorithm employs a hybrid search strategy that combines a global random initialization of the parameters with a local refinement stage based on the Nelder–Mead simplex method. During the randomization phase, initial parameter values ( R i , C i ) are automatically generated within predefined numerical intervals using random functions. These intervals define implicit boundaries for each parameter, ensuring that the search is constrained within physically meaningful ranges and preventing divergence or trapping in non-physical minima: $$\:{R}_{i,min\:}\le\:{R}_{i\:}\le\:{R}_{i,max\:},\:{C}_{i,min\:}\le\:{C}_{i\:}\le\:{C}_{i,max\:}$$ 7 The algorithm iteratively updates all parameters to achieve the best fit between the experimental and simulated Nyquist spectra. As depicted in Fig. 5 , the fitted curve accurately reproduces both the high-frequency semicircle related to charge-transfer resistance and the low-frequency tail associated with diffusion processes. The low residual error confirms that the identified 3RC parameters provide an accurate and physically consistent representation of the electrochemical behavior of the tested cell. Results In this section, both approaches, GITT and EIS, are compared in terms of their ability to parameterize the 3RC ECM. Figure 6 presents the fitted parameters as a function of the SOC, allowing a direct evaluation of the consistency and physical relevance of each method. The comparison highlights how the two identification techniques capture different aspects of the cell’s electrochemical response. EIS and GITT parameterizations exhibit similar OCV profiles, while noticeable differences appear in the RC elements. The EIS parameters remain stable with nearly constant resistances and moderate capacitances. In contrast, the GITT parameters show higher values and stronger SOC dependence, indicating that they capture additional transient and diffusion effects not reflected in the EIS results. The validation input profile, shown in Fig. 7 , was derived from real racing data of the TU Brno Racing Student Formula electric vehicle during a dynamic lap segment between 630 s and 660 s. The profile captures realistic operating conditions with rapidly changing current loads and a gradual temperature increase. Both current and temperature were used as input variables for the ECM. Both modeling approaches provide an accurate representation of the measured voltage response under dynamic load conditions. The simulated profiles follow the experimental signal closely, capturing both the magnitude and time evolution of voltage variations with deviations limited to several tens of millivolts. This consistency demonstrates that the ECMs parameterized by both EIS and GITT methods are able to describe the electrochemical behavior of the cell with a high degree of accuracy within the considered operating range, as depicted in Fig. 8 . The quantitative agreement was evaluated using the root mean square error (RMSE), defined as $$\:\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{1}{\text{N}}\sum\:_{k=1}^{\text{N}}\left({U}_{sim,i}-\:{U}_{exp,i}\right)\:}$$ 8 which quantifies the average deviation between the simulated and measured voltages. The EIS based model achieved an RMSE of 9.86 mV, while the GITT based model reached 9.61 mV. Both models exhibit very good predictive accuracy, with the GITT derived parameters providing a slightly better description of the cell voltage response, particularly during transient periods associated with diffusion controlled processes. Conclusion Both parameterization approaches provided consistent and repeatable results across the entire SOC range. The EIS based model produced stable parameter sets with small variations in resistance and capacitance values throughout the measurement range. In contrast, the GITT based model produced higher absolute resistance and capacitance values, with more pronounced changes observed in the lower-frequency RC branches. These differences reflect the distinct nature of the two identification methods, as EIS captures frequency-domain behavior while GITT is based on time-domain voltage relaxation. The validation with a real dynamic current profile from the TU Brno Racing Student Formula confirmed a very good agreement between the simulated and measured voltage responses. The EIS model achieved an RMSE of 9.86 mV, while the GITT model reached 9.61 mV. Although the difference between the two models is minor, the GITT model showed a slightly better performance during transient operating conditions, where current gradients are dominant. Overall, the EIS based approach enables more detailed insight into the battery state through separation of individual electrochemical processes, but it is a more complex and demanding method for ECM parameterization, whereas the GITT based approach provides a more accessible alternative that does not require specialized equipment and allows straightforward creation of ECMs. Declarations Funding This work was supported by the BUT Specific Research Program (project No. FEKT-S-23-8286). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Acknowledgements This work was supported by the BUT specific research program (project No. FEKT S 23-8286). References Kebede AA, Kalogiannis T, Van Mierlo J, Berecibar M (2022) Renew Sustain Energy Rev 159:112213 Hasan MM, Haque R, Jahirul MI, Rasul MG, Fattah IMR, Hassan NMS, Mofijur M (2025) J Energy Storage 120:116511 Schmuch R, Wagner R, Hörpel G, Placke T, Winter M (2018) Nat Energy 3:267 Kulkarni A, Nadeem A, Di Fonso R, Zheng Y, Teodorescu R (2024) J Energy Storage 91:112029 Santoni F, De Angelis A, Moschitta A, Carbone P, Galeotti M, Cinà L, Giammanco C, Di Carlo A (2024) J Energy Storage 82:110389 Nicodemo N, Di Rienzo R, Lagnoni M, Bertei A, Baronti F (2024) J Energy Storage 99:113257 Sun J, Liu Y, Kainz J (2025) J Energy Storage 114:115707 Iurilli P, Brivio C, Wood V (2021) J Power Sources 505:229860 Meddings N, Heinrich M, Overney F, Lee JS, Ruiz V, Napolitano E, Seitz S, Hinds G, Raccichini R, Gaberšček M, Park J (2020) J Power Sources 480:228742 Jackey R, Saginaw M, Sanghvi P, Gazzarri J, Huria T, Ceraolo M (2013) SAE Technical Paper Series. 10.4271/2013-01-1547 Meng J, Boukhnifer M, Diallo D, Wang T (2020) Appl Sci 10:1009 Murarka M, Purohit PR, Rakshit D, Verma A (2022) J Clean Prod 377:134279 LeBel FA, Messier P, Sari A, Trovão JPF (2022) J Energy Storage 54:105303 Deng C, Lu W (2020) J Power Sources 473:228613 Wang J, Jia Y, Yang N, Lu Y, Shi M, Ren X, Lu D (2022) J Energy Storage 52:104980 Lekouaghet B, Merrouche W, Bouguenna E, Taghezouit B, Benghanem M (2024) J Power Sources 624:235615 Liu E, Sun L, Lyu D (2025) J Power Sources 659:238310 Pizarro-Carmona V, Castano-Solís S, Cortés-Carmona M, Fraile-Ardanuy J, Jimenez-Bermejo D (2021) Expert Syst Appl 172:114647 Mesbahi T, Khenfri F, Rizoug N, Chaaban K, Bartholomeüs P, Le Moigne P (2016) Electr Power Syst Res 131:195–204 Moravčík D (2025) Brno University of Technology, Faculty of Electrical Engineering and Communication. Available at: https://www.vut.cz/studenti/zav-prace/detail/168655 Supplementary Files floatimage17.png Graphical abstract Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Monatshefte für Chemie - Chemical Monthly → Version 1 posted Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 21 Oct, 2025 First submitted to journal 19 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7885669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541356615,"identity":"4edaa107-15c9-4d61-a138-2183d16baeef","order_by":0,"name":"Marek Sedlařík","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3QMWrDMBTG8U8InMV4jhbnCgqCQqGkV5EJuAfo0sk1dOiSA7i3EOQCKg/sxUlWQ4Ymi+eELulWte7SRe7YQX+0vOHHewgIhf5h0gKsRAE0btL2ZpxcD4Tw9RzJgWiE3A7EDgSWxslVszkeK9g0Id6fDu0uM28lO198pL1Tc4NCCYpUpbt9Zmrw6dRHbB6JA3hmCAr6tFeydqdJD5G7fvLhyKOhybsj24FoH+nyiBlwLSl2WzqbfhPrJT0XlaT5C8X30O0yFXX2JErvYTk7rx6KWdI8r9mlXsQJ0av3x37gr4n5doRCoVDoL30CTyJVKRGCCGsAAAAASUVORK5CYII=","orcid":"","institution":"Brno University of Technology Faculty of Electrical Engineering and Communication: Vysoke uceni technicke v Brne Fakulta Elektrotechniky a Komunikacnich Technologii","correspondingAuthor":true,"prefix":"","firstName":"Marek","middleName":"","lastName":"Sedlařík","suffix":""},{"id":541356616,"identity":"87cde254-d0b4-4ab6-8ad7-48bd0d39d409","order_by":1,"name":"Petr Vyroubal","email":"","orcid":"","institution":"Brno University of Technology Faculty of Electrical Engineering and Communication: Vysoke uceni technicke v Brne Fakulta Elektrotechniky a Komunikacnich Technologii","correspondingAuthor":false,"prefix":"","firstName":"Petr","middleName":"","lastName":"Vyroubal","suffix":""},{"id":541356617,"identity":"9cbc64a7-3665-448b-8461-b658a622adb8","order_by":2,"name":"Jakub Lysák","email":"","orcid":"","institution":"Brno University of Technology Faculty of Electrical Engineering and Communication: Vysoke uceni technicke v Brne Fakulta Elektrotechniky a Komunikacnich Technologii","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Lysák","suffix":""},{"id":541356618,"identity":"fff4ee23-23b5-4828-9ca9-48183f7dfff0","order_by":3,"name":"Martin Mačák","email":"","orcid":"","institution":"Austrian Institute of Technology GmbH","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Mačák","suffix":""},{"id":541356619,"identity":"9d89a4e6-0028-445b-ab30-03948c0671a3","order_by":4,"name":"Martin Šedina","email":"","orcid":"","institution":"Brno University of Technology Faculty of Electrical Engineering and Communication: Vysoke uceni technicke v Brne Fakulta Elektrotechniky a Komunikacnich Technologii","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Šedina","suffix":""},{"id":541356620,"identity":"33e46e29-14cd-44b1-8778-623a51500439","order_by":5,"name":"Tomáš Kazda","email":"","orcid":"","institution":"Brno University of Technology Faculty of Electrical Engineering and Communication: Vysoke uceni technicke v Brne Fakulta Elektrotechniky a Komunikacnich Technologii","correspondingAuthor":false,"prefix":"","firstName":"Tomáš","middleName":"","lastName":"Kazda","suffix":""}],"badges":[],"createdAt":"2025-10-17 10:33:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7885669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7885669/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00706-026-03438-5","type":"published","date":"2026-03-16T15:59:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96251467,"identity":"a3cabaef-c32d-4be5-bb05-7dde1916493b","added_by":"auto","created_at":"2025-11-19 07:39:45","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8702,"visible":true,"origin":"","legend":"","description":"","filename":"mccmMCCMD2500429.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/6b5e71d8800ff42434708935.xml"},{"id":96251445,"identity":"2f1128d2-87aa-48a1-811b-14cb5edfb09d","added_by":"auto","created_at":"2025-11-19 07:39:44","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":942,"visible":true,"origin":"","legend":"","description":"","filename":"MCCMD250042910078.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/9434d9707a69642b606729a6.xml"},{"id":96251471,"identity":"0c519e85-7022-4dce-893b-827bae3ef47c","added_by":"auto","created_at":"2025-11-19 07:39:45","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":862,"visible":true,"origin":"","legend":"","description":"","filename":"MCCMD2500429Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/7ce335b0bb0451d022577c78.xml"},{"id":96174636,"identity":"9e549241-2404-4fdd-86f6-f06f3b58ed0b","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65411,"visible":true,"origin":"","legend":"","description":"","filename":"MCCMD25004290enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/86249027471f4f72d4f40366.xml"},{"id":96174659,"identity":"c25afff0-23c7-4381-b19d-a221b324fa15","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"emf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3637888,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.emf","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/779b039e91bb4210171f2b69.emf"},{"id":96174653,"identity":"41995d4d-ac9b-4303-ba5e-d174af2eb6bb","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108255,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/7775539e6042c4154663fb37.png"},{"id":96174663,"identity":"1437da54-53e5-4ba6-9021-d48793c7f55f","added_by":"auto","created_at":"2025-11-18 11:17:45","extension":"emf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3637888,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.emf","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/be92e96c7c83d96879335bab.emf"},{"id":96250560,"identity":"661dac83-8c67-477c-b627-bed3fe841d0c","added_by":"auto","created_at":"2025-11-19 07:38:39","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14801,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/ad60c5e9b05ef0b8caad32c7.png"},{"id":96252199,"identity":"7ae227b3-45e6-4443-9808-496a3592ae61","added_by":"auto","created_at":"2025-11-19 07:40:38","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18915,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/5da53c5374802966811d1a95.png"},{"id":96252678,"identity":"7bf98512-6290-4264-8e2f-99bc411d21f4","added_by":"auto","created_at":"2025-11-19 07:41:21","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37297,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/0f61232a2ffcd87334b7e889.png"},{"id":96174646,"identity":"44a44c1a-a7ba-4cc2-b700-a7f64c818fd0","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48095,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/7f384401ca9086a594613332.png"},{"id":96174647,"identity":"4918f90b-97bf-4580-9533-0c960dc5b6c6","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23792,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/fed4ff2ab226329bcfebfc48.png"},{"id":96251675,"identity":"a09443d7-f4f7-4f6f-8831-dd45311747f9","added_by":"auto","created_at":"2025-11-19 07:39:53","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53591,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/02597b70357d03cda87ff860.png"},{"id":96174651,"identity":"133874e5-39c7-4ba8-b4f7-4081d8c50a62","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114500,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/9dfcdff1ed110b1dd381abcc.png"},{"id":96252396,"identity":"3f340f78-ef08-4238-9e16-6540cc1323f0","added_by":"auto","created_at":"2025-11-19 07:40:53","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31984,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/db5519f8907ca6f3ab1f3634.png"},{"id":96174648,"identity":"d36bed57-02be-4778-a9a6-54a359d6cc08","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18915,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/75b71daae718dba71027d296.png"},{"id":96174650,"identity":"8bbe775e-8e3a-4e21-9810-7d6cf720a3f0","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37297,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/4f4fa39f9d814398ef33fe43.png"},{"id":96249678,"identity":"e594044d-bf65-48dd-8e9a-dfd540f3a0b5","added_by":"auto","created_at":"2025-11-19 07:35:57","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48095,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/6d2d4ce87ce3056f1f19ac2d.png"},{"id":96174661,"identity":"17956703-306d-4c4e-a7f6-66d9ba20e5ba","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50097,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/00f505b3fd57e4b92ed89869.png"},{"id":96174656,"identity":"093990b2-5a91-4351-a1b0-9482ed90eac0","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53591,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/c5102b05c8080a7432effb61.png"},{"id":96174658,"identity":"cc7d1818-4334-4da0-9351-4d5e56ecf858","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114500,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/0f018a0bb5dbc8a4ea46f8ef.png"},{"id":96174657,"identity":"e5c43905-d2ce-4a03-9cde-f23f29a79e2d","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14801,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/6d05d5b5138d136f99b9a191.png"},{"id":96174654,"identity":"6353c923-fcd0-4d09-8db2-5a280a166bfe","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64660,"visible":true,"origin":"","legend":"","description":"","filename":"MCCMD25004290structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/6d2206253b38285980b3a3d2.xml"},{"id":96252666,"identity":"ffd7b29d-e4c5-466f-8aee-f1828f2a9815","added_by":"auto","created_at":"2025-11-19 07:41:20","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74350,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/7ca4d39495fc44f614654bae.html"},{"id":96174625,"identity":"1808db05-47a8-459d-8efc-64b4464e3cb9","added_by":"auto","created_at":"2025-11-18 11:17:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50271,"visible":true,"origin":"","legend":"\u003cp\u003eEquivalent circuit representation of a Li-ion cell using a 3RC model. [10]\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/618e6cfafb52b4b07f9acfb4.png"},{"id":96174627,"identity":"f14a7258-de10-4da3-872a-388b7433d6e4","added_by":"auto","created_at":"2025-11-18 11:17:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56532,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the GITT measurement procedure.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/99295025cd48a97c2d97ec91.png"},{"id":96251537,"identity":"58083243-bae3-4b79-8292-b509241cdc98","added_by":"auto","created_at":"2025-11-19 07:39:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122209,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of voltage response phases and 3RC relaxation processes from GITT measurement.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/92826f014b595d98854e57d6.png"},{"id":96174631,"identity":"231e168b-fc42-4e50-98f0-0de713e369f5","added_by":"auto","created_at":"2025-11-18 11:17:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13185,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of measured and simulated voltage responses of the 3RC ECM during GITT measurement.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/1364579b418b8adadef06891.png"},{"id":96174628,"identity":"f1a1aae0-73ad-4a59-b9ac-929d4f04a170","added_by":"auto","created_at":"2025-11-18 11:17:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119883,"visible":true,"origin":"","legend":"\u003cp\u003eMeasured and fitted Nyquist plot using the 3RC ECM.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/fbe7a21ecfaa6226430c1da1.png"},{"id":96251655,"identity":"27a1abb4-79b3-40c7-b431-37254a8b24f1","added_by":"auto","created_at":"2025-11-19 07:39:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":56532,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the GITT measurement procedure.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/756484b710633e37df464fc5.png"},{"id":96251641,"identity":"2396ae35-1723-438a-a108-ad1a9fecaa9c","added_by":"auto","created_at":"2025-11-19 07:39:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":110293,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of fitted 3RC ECM parameters (\u003cem\u003eR₀, R₁–R₃, C₁–C₃, VoC\u003c/em\u003e) obtained from GITT and EIS measurements across the SOC range.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/35c7c4e1531f703ac25adb03.png"},{"id":96252256,"identity":"94c4ef58-ba07-44ab-8070-a4409fb77533","added_by":"auto","created_at":"2025-11-19 07:40:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82999,"visible":true,"origin":"","legend":"\u003cp\u003eValidation profile from the TU Brno Racing Formula Student car with current load and corresponding temperature evolution.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/d97f19ef9da64637eb0dfbeb.png"},{"id":96174635,"identity":"8fd2d687-e768-4884-82c4-edcdc08031bd","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122209,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of voltage response phases and 3RC relaxation processes from GITT measurement.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/9ee389d04a751d5e13c9909a.png"},{"id":96174640,"identity":"d104405b-6b32-49b5-bf01-69a5e07a9047","added_by":"auto","created_at":"2025-11-18 11:17:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":200251,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of simulated voltage response using EIS and GITT based models with measured data, including error analysis.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/d49cc098dac1b95b5232ad63.png"},{"id":96174664,"identity":"549fc36b-bef5-49c1-a58e-06ad631b386c","added_by":"auto","created_at":"2025-11-18 11:17:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119883,"visible":true,"origin":"","legend":"\u003cp\u003eMeasured and fitted Nyquist plot using the 3RC ECM.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/156afb343dd25b784c48343b.png"},{"id":96251650,"identity":"9fc1cdef-e95f-4261-bf6b-4b6005975b2b","added_by":"auto","created_at":"2025-11-19 07:39:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":82999,"visible":true,"origin":"","legend":"\u003cp\u003eValidation profile from the TU Brno Racing Formula Student car with current load and corresponding temperature evolution.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/ceeae0e966326e92b026f879.png"},{"id":96252686,"identity":"6e1e1ceb-b21c-49fb-b602-eb9c7fa719e6","added_by":"auto","created_at":"2025-11-19 07:41:21","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":200251,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of simulated voltage response using EIS and GITT based models with measured data, including error analysis.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/4642af20eaf68850fa556ce6.png"},{"id":96251454,"identity":"121e7259-e997-4c7a-b9f0-3600344bf13b","added_by":"auto","created_at":"2025-11-19 07:39:44","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":50271,"visible":true,"origin":"","legend":"\u003cp\u003eEquivalent circuit representation of a Li-ion cell using a 3RC model. [10]\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/58fd837766f21f0860f76a8c.png"},{"id":96251596,"identity":"09d24c6b-b0ac-4c7a-999c-0d6e4b72bb98","added_by":"auto","created_at":"2025-11-19 07:39:50","extension":"png","order_by":25,"title":"Figure 25","display":"","copyAsset":false,"role":"figure","size":13185,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of measured and simulated voltage responses of the 3RC ECM during GITT measurement.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/74f17eaa9dd89438340d393e.png"},{"id":105223399,"identity":"d5bc09df-f9ca-4833-b7b5-be30c92e0a39","added_by":"auto","created_at":"2026-03-23 16:05:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1620512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/de3a47de-9c5f-41c3-b1a3-f0d56d97335d.pdf"},{"id":96174626,"identity":"b07b8c4f-6e34-412f-be3b-8f1fb4e0e9ee","added_by":"auto","created_at":"2025-11-18 11:17:43","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":132961,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-7885669/v1/75841f9b92bf2142666b415f.png"}],"financialInterests":"","formattedTitle":"Comparison of Equivalent Circuit Model Parameterization Using GITT and EIS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLithium-ion (Li-ion) batteries represent a fundamental element of modern energy storage, offering a combination of high energy density, long cycle life, and stable electrochemical performance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Their role is particularly critical in applications that demand efficient and reliable power delivery, such as electric vehicles, avionics and stationary storage systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the continuous expansion of global energy storage demand, the necessity of accurate modeling of Li-ion battery behavior has become increasingly evident. Such models provide a basis for optimizing performance, ensuring operational safety and extending operation life, while also supporting advanced functionalities including diagnostics, control and predictive maintenance in battery management systems.\u003c/p\u003e\u003cp\u003eEquivalent Circuit Models (ECMs) are commonly applied to describe the behavior of electrochemical systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Lower-order structures, such as 1RC and 2RC models, are often sufficient for basic applications, but their accuracy decreases when several dynamic processes interact within the cell [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this work, 3RC configuration is adopted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The model consists of a series ohmic resistance representing instantaneous voltage drop and three parallel RC branches associated with fast interfacial processes, charge-transfer phenomena and slower diffusion effects. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe terminal voltage of the cell can be formulated as the sum of the open-circuit voltage and the dynamic overpotentials associated with the RC elements. For the 3RC structure shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the voltage response is given by\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:U\\left(t\\right)={U}_{\\text{O}\\text{C}\\text{V}}-{R}_{s}I\\left(t\\right)-\\sum\\:_{i=1}^{3}\\:{U}_{i}\\left(t\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eOCV\u003c/em\u003e\u003c/sub\u003e denotes the open-circuit voltage, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e represents the ohmic resistance, \u003cem\u003eI(t)\u003c/em\u003e is the applied current, and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(t)\u003c/em\u003e corresponds to the voltage drop across the i-th RC branch. The evolution of each polarization voltage \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(t)\u003c/em\u003e is governed by a first-order differential equation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\frac{d{U}_{i}\\left(t\\right)}{dt}=-\\frac{1}{{R}_{i}{C}_{i}}{U}_{i}\\left(t\\right)+\\frac{1}{{C}_{i}}I\\left(t\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewith the time constant of each branch defined as\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\tau\\:\\:\\left(t\\right)={R}_{i}{C}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis configuration captures polarization over multiple timescales and provides a more detailed description of cell dynamics than lower-order models.\u003c/p\u003e\u003cp\u003eIn the paper by Murarka et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the ECM was applied to assess Li-ion batteries in stationary energy storage systems. The model was used to evaluate performance and safety under different load conditions, highlighting its role in supporting state estimation and operational strategies for grid applications.\u003c/p\u003e\u003cp\u003eThe accuracy of an ECM strongly depends on proper parameter identification, as resistance and capacitance values vary with temperature, State-of-Charge (SOC) and capacity fade of the cell. Among the most widely applied methods are the Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical Impedance Spectroscopy (EIS). GITT provides high resolution of long-term polarization dynamics by analyzing voltage relaxation after current pulses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and can be implemented with relatively simple laboratory equipment. EIS, on the other hand, offers superior capability in separating processes over a broad frequency spectrum, allowing the identification of ohmic resistance, charge-transfer kinetics and diffusion-related behavior within a single measurement [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This makes EIS particularly powerful for detailed diagnostics and the construction of higher-order ECMs, although it requires specialized impedance analyzers and careful experimental design.\u003c/p\u003e\u003cp\u003eIn the paper [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] by LeBel et al., the GITT method was applied to nickel-manganese-cobalt (NMC) Li-ion cells to identify parameters of ECMs. The study focused on the development of 1RC and 2RC configurations, demonstrating that GITT can be used to generate accurate model response surfaces and improve parameter fidelity compared to pulse-based identification methods.\u003c/p\u003e\u003cp\u003eIn research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] by Meddings et al., EIS was used to parameterize ECMs of Li-ion cells. By fitting impedance spectra over a wide frequency range, the method captured ohmic, charge-transfer and diffusion processes, demonstrating its effectiveness for multi-RC models.\u003c/p\u003e\u003cp\u003eThis work aims to directly compare the use of GITT and EIS for parameterization of a 3RC ECM of Li-ion batteries. The study evaluates the accuracy and applicability of both approaches and discusses their advantages and limitations. To demonstrate practical relevance, the parameterized model is validated using measurement data obtained from the TU Brno Racing Formula Student electric vehicle during race.\u003c/p\u003e"},{"header":"Experimental","content":"\u003cp\u003eThe experiment was performed on Li-ion pouch cells with lithium-cobalt oxide (LCO) as the cathode material and graphite as the anode material. The tested cell was a Grepow 15.4 Ah battery with dimensions of 7.5 \u0026times; 79.5 \u0026times; 207 mm and a weight of 263 g. Both EIS and GITT measurements were conducted at temperatures of 22, 30, 40, and 50\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParameters of Grepow 7579207 battery.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePouch\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNominal Capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.4 Ah\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNominal Voltage\u003c/p\u003e\u003cp\u003eMaximum Voltage\u003c/p\u003e\u003cp\u003eMinimum Voltage\u003c/p\u003e\u003cp\u003eMaximum Current\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.8 V\u003c/p\u003e\u003cp\u003e4.35 V\u003c/p\u003e\u003cp\u003e3 V\u003c/p\u003e\u003cp\u003e10 C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGITT parametrization\u003c/h2\u003e\u003cp\u003eAs a first approach, the GITT was employed to parameterize the ECM. The applied protocol consisted of repeated discharge pulses with a duration of 5 minutes, each followed by a 1 hour relaxation period to allow the cell voltage to stabilize before the next step. Preliminary measurements confirmed that extended relaxation is necessary to ensure consistent extraction of RC parameters. The test was performed under controlled laboratory conditions, and the procedure is schematically illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sequence started with a Constant-Current (CC) charge at 0.2 C until the cell voltage reached 4.3 V, followed by a Constant-Voltage (CV) step at 4.3 V until the current decreased to 0.05 C, corresponding to 0.77 A. After a 1 hour rest, the discharge phase was initiated using CC pulses at 0.3 C for 5 minutes, each separated by 1 hour relaxation intervals. This cycle was repeated until the cut-off voltage of 3.0 V was reached, which marked the end of the experiment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn total, 38 pulses were obtained throughout the experiment. The final pulse was omitted from the analysis, since the cell reached the cut-off voltage and could no longer provide reliable data for RC parameter extraction. The overall duration of the test, not including the initial charging step, was approximately 41 hours.\u003c/p\u003e\u003cp\u003eThe parameter identification process relies on analyzing the cell\u0026rsquo;s voltage response to controlled current pulses using the GITT. The ECM structure consists of a series resistance \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e and three RC pairs, enabling refined characterization of both fast and slow transient processes occurring within the cell. A critical element of the model is the series resistance \u003cem\u003eRs\u003c/em\u003e, which accounts for ohmic losses such as electrolyte resistance and contact resistances at the electrode interfaces [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Its value is determined from the instantaneous voltage drop at the start of each current pulse, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This immediate drop is attributed to the purely resistive components of the cell.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBeyond the ohmic contribution, the battery exhibits exponential relaxation behavior after each current step, reflecting charge-transfer and diffusion-related processes. To capture these effects, a 3RC ECM configuration is employed, where each RC pair represents a distinct transient mechanism. The first RC branch (\u003cem\u003eR1, C1\u003c/em\u003e) is typically associated with fast interfacial charge-transfer dynamics, the second branch (\u003cem\u003eR2, C2\u003c/em\u003e) with intermediate processes, and the third branch (\u003cem\u003eR3, C3\u003c/em\u003e) with slower diffusion and redistribution within the electrode material [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccurate determination of the ECM parameters requires an iterative optimization process that minimizes the difference between the simulated and measured voltage responses. Because the relaxation behavior of the cell is governed by several dynamic processes, each described by a characteristic time constant \u003cem\u003eτ\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e, the initial parameter values were empirically estimated and constrained within physically reasonable limits:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\tau\\:}_{min,i}\\le\\:{\\tau\\:}_{i}\\le\\:{\\tau\\:}_{max,i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese limits define the admissible range for the optimization algorithm, which aims to minimize the squared deviation between experimental and simulated data according to the following objective function:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\underset{{R}_{i},{C}_{i}}{min}\\:\\sum\\:_{k}\\:{\\left({U}_{\\text{exp}}\\left({t}_{k}\\right)-{U}_{\\text{sim\\:}}\\left({t}_{k}\\right)\\right)}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eexp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(t\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e denotes the experimentally recorded voltage at time step \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003esim\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(t\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e is the corresponding value generated by model. During the iterative fitting procedure, the resistance and capacitance of each RC branch are continuously adjusted until the simulated relaxation curve accurately reproduces the measured response.\u003c/p\u003e\u003cp\u003eThe resulting parameter set was validated through direct comparison between simulated and experimental voltage profiles, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The strong agreement between both confirms that the optimized 3RC model reliably captures the transient behaviour of the cell. The residual voltage error remains consistently low throughout the measurement sequence, indicating the robustness and precision of the applied parameter identification method.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEIS parametrization\u003c/h3\u003e\n\u003cp\u003eThe second method applied for ECM parameterization was EIS. Charging and discharging were performed at a current rate of 0.2 C This current rate was selected as an optimal balance between the overall duration of the experiment and the precision of the recorded data, considering that the tested cell is designed for high-current operation.\u003c/p\u003e\u003cp\u003eMeasurements were performed at defined intervals of 15% SOC, starting from the fully charged condition down to 10% SOC using CC discharge. To extend the characterization to lower SOC values, additional measurements were taken at 5% and 0% SOC. Since the cell was new and exhibited negligible voltage drop during low-current operation, the measurement at each step was time-limited rather than voltage-limited, with the 5% SOC step corresponding to approximately one hour of discharge.\u003c/p\u003e\u003cp\u003eImpedance data were collected over a frequency range of 30 mHz to 10 kHz with an excitation amplitude of 10 mV. Five logarithmically spaced points per frequency decade were recorded, and each spectrum represents the mean of three consecutive measurements to ensure repeatability and minimize random deviations.\u003c/p\u003e\u003cp\u003eFollowing the impedance measurements, the obtained spectra were subjected to numerical fitting based on a 3RC ECM. The fitting and optimization process was implemented to accurately identify the RC components corresponding to individual electrochemical processes. The primary objective of this procedure was to minimize the deviation between the experimentally measured impedance \u003cem\u003eZ\u003c/em\u003e\u003csub\u003e\u003cem\u003eexp\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(ω)\u003c/em\u003e and the impedance predicted by the simulation \u003cem\u003eZ\u003c/em\u003e\u003csub\u003e\u003cem\u003esim\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(ω)\u003c/em\u003e. The 3RC configuration (depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003e) comprises a series resistance \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e and three parallel RC branches, each representing a distinct electrochemical process characterized by a specific time constant. The total complex impedance of the model is given by [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{Z}_{sim}\\left(\\omega\\:\\right)={R}_{S}+\\frac{{R}_{1}}{1+j{\\omega\\:R}_{1}{C}_{1}}+\\frac{{R}_{2}}{1+j{\\omega\\:R}_{2}{C}_{2}}+\\frac{{R}_{3}}{1+j{\\omega\\:R}_{3}{C}_{3}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ej\u003c/em\u003e is the imaginary unit and \u003cem\u003eω\u0026thinsp;=\u0026thinsp;2πf\u003c/em\u003e represents the angular frequency.\u003c/p\u003e\u003cp\u003eTo identify optimal parameter values, an iterative optimization algorithm was implemented. The objective of the procedure is to minimize the normalized squared error between the measured and simulated impedance magnitudes across all frequencies, as defined by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{\\chi\\:}^{2}=\\sum\\:_{k=1}^{N}\\frac{\\left|{Z}_{\\text{exp}}\\left({\\omega\\:}_{k}\\right)-{Z}_{\\text{sim\\:}}\\left({\\omega\\:}_{k}\\right)\\right|}{{{Z}_{\\text{exp}}\\left({\\omega\\:}_{k}\\right)}^{2}}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the total number of frequency points. The algorithm employs a hybrid search strategy that combines a global random initialization of the parameters with a local refinement stage based on the Nelder\u0026ndash;Mead simplex method. During the randomization phase, initial parameter values (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) are automatically generated within predefined numerical intervals using random functions. These intervals define implicit boundaries for each parameter, ensuring that the search is constrained within physically meaningful ranges and preventing divergence or trapping in non-physical minima:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:{R}_{i,min\\:}\\le\\:{R}_{i\\:}\\le\\:{R}_{i,max\\:},\\:{C}_{i,min\\:}\\le\\:{C}_{i\\:}\\le\\:{C}_{i,max\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe algorithm iteratively updates all parameters to achieve the best fit between the experimental and simulated Nyquist spectra. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the fitted curve accurately reproduces both the high-frequency semicircle related to charge-transfer resistance and the low-frequency tail associated with diffusion processes. The low residual error confirms that the identified 3RC parameters provide an accurate and physically consistent representation of the electrochemical behavior of the tested cell.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn this section, both approaches, GITT and EIS, are compared in terms of their ability to parameterize the 3RC ECM. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the fitted parameters as a function of the SOC, allowing a direct evaluation of the consistency and physical relevance of each method. The comparison highlights how the two identification techniques capture different aspects of the cell\u0026rsquo;s electrochemical response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEIS and GITT parameterizations exhibit similar OCV profiles, while noticeable differences appear in the RC elements. The EIS parameters remain stable with nearly constant resistances and moderate capacitances. In contrast, the GITT parameters show higher values and stronger SOC dependence, indicating that they capture additional transient and diffusion effects not reflected in the EIS results.\u003c/p\u003e\u003cp\u003eThe validation input profile, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, was derived from real racing data of the TU Brno Racing Student Formula electric vehicle during a dynamic lap segment between 630 s and 660 s. The profile captures realistic operating conditions with rapidly changing current loads and a gradual temperature increase. Both current and temperature were used as input variables for the ECM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBoth modeling approaches provide an accurate representation of the measured voltage response under dynamic load conditions. The simulated profiles follow the experimental signal closely, capturing both the magnitude and time evolution of voltage variations with deviations limited to several tens of millivolts. This consistency demonstrates that the ECMs parameterized by both EIS and GITT methods are able to describe the electrochemical behavior of the cell with a high degree of accuracy within the considered operating range, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe quantitative agreement was evaluated using the root mean square error (RMSE), defined as\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{M}\\text{S}\\text{E}=\\sqrt{\\frac{1}{\\text{N}}\\sum\\:_{k=1}^{\\text{N}}\\left({U}_{sim,i}-\\:{U}_{exp,i}\\right)\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhich quantifies the average deviation between the simulated and measured voltages. The EIS based model achieved an RMSE of 9.86 mV, while the GITT based model reached 9.61 mV. Both models exhibit very good predictive accuracy, with the GITT derived parameters providing a slightly better description of the cell voltage response, particularly during transient periods associated with diffusion controlled processes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth parameterization approaches provided consistent and repeatable results across the entire SOC range. The EIS based model produced stable parameter sets with small variations in resistance and capacitance values throughout the measurement range. In contrast, the GITT based model produced higher absolute resistance and capacitance values, with more pronounced changes observed in the lower-frequency RC branches. These differences reflect the distinct nature of the two identification methods, as EIS captures frequency-domain behavior while GITT is based on time-domain voltage relaxation.\u003c/p\u003e\u003cp\u003eThe validation with a real dynamic current profile from the TU Brno Racing Student Formula confirmed a very good agreement between the simulated and measured voltage responses. The EIS model achieved an RMSE of 9.86 mV, while the GITT model reached 9.61 mV. Although the difference between the two models is minor, the GITT model showed a slightly better performance during transient operating conditions, where current gradients are dominant.\u003c/p\u003e\u003cp\u003eOverall, the EIS based approach enables more detailed insight into the battery state through separation of individual electrochemical processes, but it is a more complex and demanding method for ECM parameterization, whereas the GITT based approach provides a more accessible alternative that does not require specialized equipment and allows straightforward creation of ECMs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the BUT Specific Research Program (project No. FEKT-S-23-8286).\u003c/p\u003e\u003cp\u003eCompeting Interests\u003c/p\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis work was supported by the BUT specific research program (project No. FEKT S 23-8286).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKebede AA, Kalogiannis T, Van Mierlo J, Berecibar M (2022) Renew Sustain Energy Rev 159:112213\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasan MM, Haque R, Jahirul MI, Rasul MG, Fattah IMR, Hassan NMS, Mofijur M (2025) J Energy Storage 120:116511\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmuch R, Wagner R, H\u0026ouml;rpel G, Placke T, Winter M (2018) Nat Energy 3:267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulkarni A, Nadeem A, Di Fonso R, Zheng Y, Teodorescu R (2024) J Energy Storage 91:112029\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantoni F, De Angelis A, Moschitta A, Carbone P, Galeotti M, Cin\u0026agrave; L, Giammanco C, Di Carlo A (2024) J Energy Storage 82:110389\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicodemo N, Di Rienzo R, Lagnoni M, Bertei A, Baronti F (2024) J Energy Storage 99:113257\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun J, Liu Y, Kainz J (2025) J Energy Storage 114:115707\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIurilli P, Brivio C, Wood V (2021) J Power Sources 505:229860\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeddings N, Heinrich M, Overney F, Lee JS, Ruiz V, Napolitano E, Seitz S, Hinds G, Raccichini R, Gaberšček M, Park J (2020) J Power Sources 480:228742\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJackey R, Saginaw M, Sanghvi P, Gazzarri J, Huria T, Ceraolo M (2013) SAE Technical Paper Series. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4271/2013-01-1547\u003c/span\u003e\u003cspan address=\"10.4271/2013-01-1547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng J, Boukhnifer M, Diallo D, Wang T (2020) Appl Sci 10:1009\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurarka M, Purohit PR, Rakshit D, Verma A (2022) J Clean Prod 377:134279\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeBel FA, Messier P, Sari A, Trov\u0026atilde;o JPF (2022) J Energy Storage 54:105303\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng C, Lu W (2020) J Power Sources 473:228613\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Jia Y, Yang N, Lu Y, Shi M, Ren X, Lu D (2022) J Energy Storage 52:104980\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLekouaghet B, Merrouche W, Bouguenna E, Taghezouit B, Benghanem M (2024) J Power Sources 624:235615\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu E, Sun L, Lyu D (2025) J Power Sources 659:238310\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePizarro-Carmona V, Castano-Sol\u0026iacute;s S, Cort\u0026eacute;s-Carmona M, Fraile-Ardanuy J, Jimenez-Bermejo D (2021) Expert Syst Appl 172:114647\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMesbahi T, Khenfri F, Rizoug N, Chaaban K, Bartholome\u0026uuml;s P, Le Moigne P (2016) Electr Power Syst Res 131:195\u0026ndash;204\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoravč\u0026iacute;k D (2025) Brno University of Technology, Faculty of Electrical Engineering and Communication. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.vut.cz/studenti/zav-prace/detail/168655\u003c/span\u003e\u003cspan address=\"https://www.vut.cz/studenti/zav-prace/detail/168655\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"monatshefte-fur-chemie-chemical-monthly","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mccm","sideBox":"Learn more about [Monatshefte für Chemie - Chemical Monthly](https://www.springer.com/journal/706)","snPcode":"706","submissionUrl":"https://www.editorialmanager.com/mccm/","title":"Monatshefte für Chemie - Chemical Monthly","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Li-ion battery, Galvanostatic Intermittent Titration Technique, Electrochemical Impedance Spectroscopy, Equivalent Circuit Model, State of Charge","lastPublishedDoi":"10.21203/rs.3.rs-7885669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7885669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate parameterization of Equivalent Circuit Models (ECMs) is essential for reliable state estimation and dynamic simulation of lithium-ion (Li-ion) batteries. These models provide a representation of the electrochemical processes governing cell behavior and are widely applied across various fields, including electric mobility, stationary energy storage, avionics and industrial applications. This work investigates two commonly employed approaches for ECM parameter identification: Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical Impedance Spectroscopy (EIS). Both methods were applied to Li-ion cells under controlled laboratory conditions and the parameters of a 3RC model were extracted and analyzed. The study compares time-domain and frequency-domain parameterization, addressing their methodological differences, measurement requirements and resulting model characteristics. To demonstrate practical applicability, the identified models were validated using real racing data from the TU Brno Racing Formula Student electric vehicle.\u003c/p\u003e","manuscriptTitle":"Comparison of Equivalent Circuit Model Parameterization Using GITT and EIS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 11:17:38","doi":"10.21203/rs.3.rs-7885669/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-09T15:56:36+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T06:27:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T07:35:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Monatshefte für Chemie - Chemical Monthly","date":"2025-10-19T14:27:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"monatshefte-fur-chemie-chemical-monthly","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mccm","sideBox":"Learn more about [Monatshefte für Chemie - Chemical Monthly](https://www.springer.com/journal/706)","snPcode":"706","submissionUrl":"https://www.editorialmanager.com/mccm/","title":"Monatshefte für Chemie - Chemical Monthly","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"910bddfc-2d7f-4e91-b638-4efc597e9299","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:02:17+00:00","versionOfRecord":{"articleIdentity":"rs-7885669","link":"https://doi.org/10.1007/s00706-026-03438-5","journal":{"identity":"monatshefte-fur-chemie-chemical-monthly","isVorOnly":false,"title":"Monatshefte für Chemie - Chemical Monthly"},"publishedOn":"2026-03-16 15:59:01","publishedOnDateReadable":"March 16th, 2026"},"versionCreatedAt":"2025-11-18 11:17:38","video":"","vorDoi":"10.1007/s00706-026-03438-5","vorDoiUrl":"https://doi.org/10.1007/s00706-026-03438-5","workflowStages":[]},"version":"v1","identity":"rs-7885669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7885669","identity":"rs-7885669","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}