Penerapan Genetic Algorithm (GA) untuk Optimasi Parameter Model Thevenin Baterai Lithium

Muhammad Aminuddin; Khenza Atthaya Namira Yulianto; Moethia Faridha; Saiful Karim

doi:10.33365/jtikom.v6i2.1064

Muhammad Aminuddin Universitas Islam Kalimantan Muhammad Arysad Al Banjari Banjarmasin
Khenza Atthaya Namira Yulianto Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin
Moethia Faridha Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin
Saiful Karim Universitas Islam Kalimantan Muhammad Arysad Al Banjari Banjarmasin

DOI: https://doi.org/10.33365/jtikom.v6i2.1064

Keywords: Model Baterai Thevenin, Identifikasi Parameter, Baterai, Algoritma Genetika, Estimasi Tegangan

Abstract

Accurate battery modeling is essential for improving the performance of state estimation algorithms such as State of Charge (SoC) and State of Health (SoH). The first-order Thevenin model is widely used due to its simplicity and its ability to capture the dynamic voltage behavior of lithium-based batteries. The accuracy of this model strongly depends on the selection of the internal resistance ( ), polarization resistance ( ), and polarization capacitance ( ). However, these parameters are often assumed to be constant, causing the battery model to lose adaptability when the cell experiences voltage dynamics during operation. To address this limitation, this study proposes the use of a Genetic Algorithm (GA) to adaptively optimize the model parameters. The GA is designed to update the parameters when the initial values begin to produce increasing voltage estimation errors, ensuring that the parameters remain representative of the battery’s actual condition. Meanwhile, offline analysis and Recursive Least Squares (RLS) are employed as comparison and validation methods. Experimental results demonstrate that the parameters optimized using GA significantly improve terminal voltage estimation accuracy, achieving a minimum RMSE of 0.005 V, outperforming both the offline method (RMSE 0.0145 V) and RLS (RMSE 0.0143 V). These findings confirm that GA effectively generates dynamic and accurate parameters, enabling the Thevenin model to better represent the battery’s behavior under varying operating conditions.

Keywords: Thevenin Battery Model, Parameter Identification, Battery, Genetic Algorithm, Voltage Estimation

References

M.-K. Tran dkk., “Environmental and Economic Benefits of a Battery Electric Vehicle Powertrain with a Zinc–Air Range Extender in the Transition to Electric Vehicles,” Vehicles, vol. 2, no. 3, hlm. 398–412, Jun 2020, doi: 10.3390/vehicles2030021.

M.-K. Tran dkk., “A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters,” Journal of Energy Storage, vol. 43, hlm. 103252, Nov 2021, doi: 10.1016/j.est.2021.103252.

M. Elmahallawy, T. Elfouly, A. Alouani, dan A. M. Massoud, “A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction,” IEEE Access, vol. 10, hlm. 119040–119070, 2022, doi: 10.1109/ACCESS.2022.3221137.

A. E. Jayasinghe, N. Fernando, S. Kumarawadu, dan L. Wang, “Review on Li-ion Battery Parameter Extraction Methods,” IEEE Access, vol. 11, hlm. 73180–73197, 2023, doi: 10.1109/ACCESS.2023.3296440.

S. Panchal, K. Gudlanarva, M.-K. Tran, R. Fraser, dan M. Fowler, “High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles,” Energies, vol. 13, no. 7, hlm. 1638, Apr 2020, doi: 10.3390/en13071638.

Y. Wang dkk., “12-A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems,” Renewable and Sustainable Energy Reviews, vol. 131, hlm. 110015, Okt 2020, doi: 10.1016/j.rser.2020.110015.

X. Li, D. Fan, X. Liu, S. Xu, dan B. Huang, “State of health estimation for lithium-ion batteries based on improved bat algorithm optimization kernel extreme learning machine,” Journal of Energy Storage, vol. 101, hlm. 113756, Nov 2024, doi: 10.1016/j.est.2024.113756.

N. Shi, Z. Chen, M. Niu, Z. He, Y. Wang, dan J. Cui, “State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification,” Journal of Energy Storage, vol. 45, hlm. 103518, Jan 2022, doi: 10.1016/j.est.2021.103518.

M. Hossain, M. E. Haque, dan M. T. Arif, “Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: A comparative analysis,” Journal of Energy Storage, vol. 51, hlm. 104174, Jul 2022, doi: 10.1016/j.est.2022.104174.

M. Auch, T. Kuthada, S. Giese, dan A. Wagner, “Influence of Lithium-Ion-Battery Equivalent Circuit Model Parameter Dependencies and Architectures on the Predicted Heat Generation in Real-Life Drive Cycles,” Batteries, vol. 9, no. 5, hlm. 274, Mei 2023, doi: 10.3390/batteries9050274.

C. Ma dkk., “A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries,” Processes, vol. 12, no. 10, hlm. 2166, Okt 2024, doi: 10.3390/pr12102166.

H. Y. Pai, Y. H. Liu, dan S. P. Ye, “Online estimation of lithium-ion battery equivalent circuit model parameters and state of charge using time-domain assisted decoupled recursive least squares technique,” Journal of Energy Storage, vol. 62, hlm. 106901, Jun 2023, doi: 10.1016/j.est.2023.106901.

M. Aminuddin, O. Wahyunggoro, dan A. I. Cahyadi, “Improving Battery Model Accuracy Through Parameter Identification Using RLS and Pulse Test Analysis,” dalam 2024 10th International Conference on Smart Computing and Communication (ICSCC), Jul 2024, hlm. 438–442. doi: 10.1109/ICSCC62041.2024.10690426.

C. Ge, Y. Zheng, dan Y. Yu, “State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm,” Journal of Energy Storage, vol. 55, hlm. 105474, Nov 2022, doi: 10.1016/j.est.2022.105474.

X. Zhao, T. Wang, L. Li, dan Y. Cheng, “Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF,” WEVJ, vol. 15, no. 11, hlm. 494, Okt 2024, doi: 10.3390/wevj15110494.

C. G. Manriquez-Padilla, I. Cueva-Perez, A. Dominguez-Gonzalez, D. A. Elvira-Ortiz, A. Perez-Cruz, dan J. J. Saucedo-Dorantes, “State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles,” Sensors, vol. 23, no. 6, hlm. 2924, Mar 2023, doi: 10.3390/s23062924.

T. Al Rafei, N. Yousfi Steiner, dan D. Chrenko, “Genetic Algorithm and Taguchi Method: An Approach for Better Li-Ion Cell Model Parameter Identification,” Batteries, vol. 9, no. 2, hlm. 72, Jan 2023, doi: 10.3390/batteries9020072.

Z. Chang dkk., “Review of SOC estimation methods for lithium battery based on EKF,” dalam 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Jun 2021, hlm. 1319–1326. doi: 10.1109/IMCEC51613.2021.9482085.

E. A. Ortiz, J. Tello-Maita, D. Celeita, dan A. Marulanda Guerra, “Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators,” Energies, vol. 18, no. 1, hlm. 109, Des 2024, doi: 10.3390/en18010109.

B. Guo, H. Sun, Z. Zhao, dan Y. Liu, “Online Parameter Identification for Fractional Order Model of Lithium Ion Battery via Adaptive Genetic Algorithm,” dalam 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China: IEEE, Mei 2023, hlm. 1227–1232. doi: 10.1109/DDCLS58216.2023.10166251.

A. S dan L. N, “Effect of Input Data and Parameters of Genetic Algorithm on Lithium-Ion Battery Modeling,” dalam 2023 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India: IEEE, Jul 2023, hlm. 524–528. doi: 10.1109/AIC57670.2023.10263820.

S. S. Suriya Narayanan dan S. Thangavel, “Open Circuit Voltage Equation parameter identification of Lithium Ion Battery Model using Genetic Algorithm,” dalam 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India: IEEE, Feb 2020, hlm. 1122–1127. doi: 10.1109/ICICT48043.2020.9112455.