Research on Two-Stage Parameter Identification for Various Lithium-Ion Battery Models Using Bio-Inspired Optimization Algorithms
Source abstract
Abstract
Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Microgrid Control and Optimization · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Marine Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
References (46)
Show source-provided references
1. Madani, S.S., Shabeer, Y., Allard, F., Fowler, M., Ziebert, C., Wang, Z., Panchal, S., Chaoui, H., Mekhilef, S., and Dou, S.X. (2025). A Comprehensive Review on Lithium-ion Battery Lifetime Prediction and Aging Mechanism Analysis. Batteries, 11.10.3390/batteries11040127 ↗
2. Advancing Energy Storage: The Future Trajectory of Lithium-ion Battery Technologies10.1016/j.est.2025.116511 ↗
3. On the Sustainability of Lithium Ion Battery Industry—A Review and Perspective10.1016/j.ensm.2020.12.019 ↗
4. Battery Management Solutions for Li-ion Batteries Based on Artificial Intelligence10.1016/j.asej.2023.102213 ↗
5. A Comprehensive Review of Lithium-ion Battery Components Degradation and Operational Considerations: A Safety Perspective10.1039/D5YA00065C ↗
6. Framework for Energy Storage Selection to Design the Next Generation of Electrified Military Vehicles10.1016/j.energy.2021.120695 ↗
7. He, X., Wu, Z., Bai, J., Zhu, J., Lv, L., and Wang, L. (2025). A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction. Appl. Sci., 15.10.3390/app15073592 ↗
8. Sungur, B., and Kaleli, A. (2024). State of Charge Estimation for Lithium-Ion Batteries Using Optimized Model Based on Optimal HPPC Conditions Created Using Taguchi Method and Multi-Objective Optimization. Appl. Sci., 14.10.3390/app14209245 ↗
9. Olano, J., Camblong, H., López-Ibarra, J.A., and Lie, T.T. (2025). Development of Energy Management Systems for Electric Vehicle Charging Stations Associated with Batteries: Application to a Real Case. Appl. Sci., 15.10.3390/app15168798 ↗
10. Akram, A.S., and Choi, W. (2025). Performance Enhancement of Second-Life Lithium-Ion Batteries Based on Gaussian Mixture Model Clustering and Simulation-Based Evaluation for Energy Storage System Applications. Appl. Sci., 15.10.20944/preprints202505.2428.v1 ↗
11. Chan, H.T.J., Rubeša-Zrim, J., Pichler, F., Salihi, A., Mourad, A., Šimić, I., Časni, K., and Veas, E. (2025). Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries. Appl. Sci., 15.10.3390/app15095078 ↗
12. A Comprehensive Review of Battery State of Charge Estimation Techniques
13. Open-circuit Voltage-based State of Charge Estimation of Lithium-ion Power Battery by Combining Controlled Autoregressive and Moving Average Modeling with Feedforward-Feedback Compensation Method10.1016/j.ijepes.2017.01.013 ↗
14. State of Charge Prediction of EV Li-ion Batteries Using EIS: A Machine Learning Approach10.1016/j.energy.2021.120116 ↗
15. Artificial Neural Networks, Gradient Boosting and Support Vector Machines for Electric Vehicle Battery State Estimation: A Review10.1016/j.est.2022.105384 ↗
16. An Electromechanical Coupling Model-based State of Charge Estimation Method for Lithium-ion Pouch Battery Modules10.1016/j.energy.2022.125019 ↗
17. Simplified Electrochemical Model to Account for Different Active/Inactive Cathode Compositions in Li-ion Batteries10.1016/j.est.2020.101579 ↗
18. Deep-Learning-Based Parameter Identification Enables Rationalization of Battery Material Evolution in Complex Electrochemical Systems10.1016/j.jocs.2022.101900 ↗
19. Global Parameter Sensitivity Analysis of Electrochemical Model for Lithium Ion Batteries Considering Aging10.1109/TMECH.2021.3067923 ↗
20. Low-Frequency Characteristics and Fractional-Order Impedance Model for Electrochemical Impedance Spectroscopy in Large-Capacity Lithium-Ion Batteries10.1016/j.est.2025.118222 ↗
21. A Novel Composite Fractional Order Battery Model with Online Parameter Identification and Truncation Approximation Calculation10.1016/j.energy.2025.135561 ↗
22. A Comprehensive Review of Fractional-Order Mathematical Models for Lithium-Ion Batteries: Historical Progress, Recent Advancements, and Future Outlooks10.1016/j.est.2025.117404 ↗
23. A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium-Ion Batteries10.1186/s10033-020-00467-x ↗
24. Chen, C., and Wurzenberger, J. (2024). Parameterization of an Electrochemical Battery Model Using Impedance Spectroscopy in a Wide Range of Frequency, SAE International. SAE Technical Paper.10.4271/2024-01-2194 ↗
25. Battery Parameter Analysis Through Electrochemical Impedance Spectroscopy at Different State of Charge Levels10.3390/jlpea13020029 ↗
26. Nonlinear Electrochemical Impedance Spectroscopy for Lithium-ion Battery Model Parameterization10.1149/1945-7111/acada7 ↗
27. On the Use of Cramér-Rao Lower Bound for Least-Variance Circuit Parameters Identification of Li-ion Cells10.1016/j.est.2024.112223 ↗
28. Calibrated Electrochemical Impedance Spectroscopy and Time-Domain Measurements of a 7 kWh Automotive Lithium-Ion Battery Module with 396 Cylindrical Cells10.1002/batt.202200415 ↗
29. Kasper, M., Moertelmaier, M., Popp, H., Kienberger, F., and Al-Zubaidi R-Smith, N. (2025). Reconstruction of Electrochemical Impedance Spectroscopy from Time-Domain Pulses of a 3.7 kWh Lithium-Ion Battery Module. Electrochem, 6.10.3390/electrochem6020017 ↗
30. Chen, H., Bai, J., Wu, Z., Song, Z., Zuo, B., Fu, C., Zhang, Y., and Wang, L. (2025). Rapid Impedance Measurement of Lithium-Ion Batteries under Pulse Excitation and Analysis of Impedance Characteristics of the Regularization Distributed Relaxation Time. Batteries, 11.10.3390/batteries11030091 ↗
31. Zhao, Y., and Jossen, A. (2022). Comparative Study of Parameter Identification with Frequency and Time Domain Fitting Using a Physics-based Battery Model. Batteries, 8.10.3390/batteries8110222 ↗
32. Electrochemical Model Parameter Identification of a Lithium-Ion Battery Using Particle Swarm Optimization Method10.1016/j.jpowsour.2015.12.083 ↗
33. Multi-Objective Optimization of Lithium-Ion Battery Model Using Genetic Algorithm Approach10.1016/j.jpowsour.2014.07.110 ↗
34. Ge, D., Zhang, Z., Kong, X., and Wan, Z. (2022). Extreme Learning Machine Using Bat Optimization Algorithm for Estimating State of Health of Lithium-Ion Batteries. Appl. Sci., 12.10.3390/app12031398 ↗
35. Comparison of the Performances of Heuristic Optimization Algorithms PSO, ABC, and GA for Parameter Estimation in the Discharge Processes of Li-NMC Battery10.30521/jes.1094106 ↗
36. Lithium-Ion Battery Parameter Estimation Based on Variational and Logistic Map Cuckoo Search Algorithm10.1007/s00202-024-02580-9 ↗
37. A Real-Time State of Charge Estimation using Harris Hawks Optimization-based Filtering Approach for Electric Vehicle Power Batteries10.1002/er.7806 ↗
38. Improved Coyote Optimization Algorithm for Parameter Estimation of Lithium-Ion Batteries10.1177/09576509221147330 ↗
39. Camas-Náfate, M., Coronado-Mendoza, A., Vargas-Salgado, C., Águila-León, J., and Alfonso-Solar, D. (2024). Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms. Energies, 17.10.3390/en17040822 ↗
40. On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery10.1007/s44196-022-00114-4 ↗
41. Fathy, A., Yousri, D., Alharbi, A.G., and Abdelkareem, M.A. (2023). A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters. Sustainability, 15.10.3390/su15075667 ↗
42. Advanced Parameter Identification in Electric Vehicles Lithium-Ion Batteries with Marine Predators Algorithm-Based Optimization10.1155/er/8883900 ↗
43. Artificial Rabbits Optimization: A New Bio-inspired Meta-heuristic Algorithm for Solving Engineering Optimization Problems10.1016/j.engappai.2022.105082 ↗
44. Extreme Learning Machine Model with Honey Badger Algorithm based State-of-Charge Estimation of Lithium-Ion Battery10.1016/j.eswa.2023.121609 ↗
45. The Analysis of Electrode Impedances Complicated by the Presence of a Constant Phase Element10.1016/S0022-0728(84)80324-1 ↗
46. A Novel Statistical Approach for Comparing Meta-Heuristic Stochastic Optimization Algorithms According to the Distribution of Solutions in the Search Space10.1016/j.ins.2019.03.049 ↗