daf11cf8-bb8e-485d-9e84-9d46e840be3d20250123082510243wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON ELECTRONICS2415-15131109-944510.37394/232017http://wseas.org/wseas/cms.action?id=13363123202412320241510.37394/232017.2024.15https://wseas.com/journals/electronics/2024.phpUnveiling Impedance Response of Commercial Coin-type Lithium-ion Batteries Exposed to Overcharge and Over-discharge through Equivalent Circuit ModelingSalimErolDepartment of Chemical Engineering, Eskisehir Osmangazi University, Meselik Campus, 26040 Eskisehir, TURKEYThis study investigates the impedance spectroscopy response of commercial coin-type lithium-ion cells under overcharge and over-discharge conditions through equivalent circuit modeling. Electrochemical tests were conducted on battery cells, employing impedance spectroscopy to track variations across various charge states, including overcharge and over-discharge conditions. The impedance data highlighted distinct patterns during normal operation, prompting the application of an anomalous diffusion impedance model. Regression analysis of the model parameters offered valuable insights into the cells' electrochemical performance before and after exposure to overcharge and over-discharge.123120241231202420320722https://wseas.com/journals/electronics/2024/a445117-019(2024).pdf10.37394/232017.2024.15.22https://wseas.com/journals/electronics/2024/a445117-019(2024).pdf10.1109/tie.2014.2341576Chaoui, H., Golbon, N., Hmouz, I., Souissi, R., Tahar, S. "Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries." IEEE Transactions on Industrial Electronics 62, no. 3 (2015): 1610- 1618, https://doi.org/10.1109/TIE.2014.2341576. 10.1155/2023/4297545Nuroldayeva, G., Serik, Y., Adair, D. "State of Health Estimation Methods for Lithium-Ion Batteries." International Journal of Energy Research 2023.1 (2023): 4297545, https://doi.org/10.1155/2023/4297545. 10.1109/acc.2006.1657640McIntyre, M., T. Burg, D. Dawson, and B. Xian. "Adaptive state of charge (SOC) estimator for a battery." In 2006 American Control Conference, pp. 5-pp. IEEE, Minneapolis, MN, USA, 2006, https://doi.org/10.1109/ACC.2006.1657640. 10.1016/j.jpowsour.2012.10.060Lu, Languang, Xuebing Han, Jianqiu Li, Jianfeng Hua, and Minggao Ouyang. "A review on the key issues for lithium-ion battery management in electric vehicles." Journal of Power Sources 226 (2013): 272- 288, https://doi.org/10.1016/j.jpowsour.2012.10.06 0. 10.1016/j.jpowsour.2012.10.001Andre, Dave, Christian Appel, Thomas Soczka-Guth, and Dirk Uwe Sauer. "Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries." Journal of Power Sources 224 (2013): 20-27, https://doi.org/10.1016/j.jpowsour.2012.10.00 1. 10.1109/tec.2006.874229Chen, Min, and Gabriel A. Rincon-Mora. "Accurate electrical battery model capable of predicting runtime and IV performance." IEEE Transactions on Energy Conversion 21, no. 2 (2006): 504-511, https://doi.org/10.1109/TEC.2006.874229. 10.1016/s0378-7753(02)00558-xBuller, Stephan, Marc Thele, Eckhard Karden, and Rik W. De Doncker. "Impedance-based non-linear dynamic battery modeling for automotive applications." Journal of Power Sources 113, no. 2 (2003): 422-430, https://doi.org/10.1016/S0378- 7753(02)00558-X. 10.1016/j.apenergy.2015.05.102Barillas, Joaquín Klee, Jiahao Li, Clemens Günther, and Michael A. Danzer. "A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems." Applied Energy 155 (2015): 455-462, https://doi.org/10.1016/j.apenergy.2015.05.10 2. 10.16984/saufenbilder.765554Erol, Salim. "Process model development of lithium-ion batteries—an electrochemical impedance spectroscopy simulation." Sakarya University Journal of Science 24.6 (2020): 1191-1197, https://doi.org/10.16984/saufenbilder.765554. 10.37394/232017.2023.14.13Khaparde, A., Deshmukh, V., Sharma, V., Singh, U. “Li-Ion Battery Temperature Forecasting Method: Case-Study.” WSEAS Transactions on Electronics, 2023, 14: 112- 121, https://doi.org/10.37394/232017.2023.14.13. 10.1016/s0022-0728(00)00497-6Bisquert, Juan; Compte, Albert. “Theory of the electrochemical impedance of anomalous diffusion.” Journal of Electroanalytical Chemistry, 2001, 499.1: 112-120, https://doi.org/10.1016/S0022- 0728(00)00497-6. Garcés, E. F. M., Herrera, J., Mafla, G., Caiza, A.. “Artificial Neuronal Networks to Predict the Emissions of Carbon Dioxide (CO2) using a multilayer network with the LevenbergMarquadt training method.” WSEAS Transactions on Environment and Development, 2019, 16: 346-54, https://wseas.com/journals/articles.php?id=19 69 (Accessed Date: December 10, 2025).