Innovative Approaches for Improving State of Health Estimation Accuracy in Lithium ION Batteries
DOI:
https://doi.org/10.24113/ijoscience.v11i1.542Keywords:
Battery Management System, Lithium Ion Battery, State of Health (SOH), Kalman Filter, Hybrid Algorithm, Electric Vehicles (EVs), Renewable Energy.,Abstract
Urgent environmental action is required due to the increasing effects of resource depletion, greenhouse gas emissions, and climate change, especially in the transportation sector. Battery technology has advanced significantly as a result of the change to hybrid and electrified vehicle powertrains, especially with Lithium Ion Batteries (LIBs). Due to their high specific energy
Downloads
References
Yang, F., Xu, Y., Su, L., Yang, Z., Feng, Y., Zhang, C., & Shao, T. (2024). State of charge and state of health estimation of lithium-ion battery packs with inconsistent internal parameters using dual extended Kalman filter. Journal of Electrochemical Energy Conversion and Storage, 21(1), 011004.
Fahmy, H. M., Hasanien, H. M., Alsaleh, I., Ji, H., & Alassaf, A. (2024). State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach. Journal of Energy Storage, 88, 111557.
Wang, C., Su, Y., Ye, J., Xu, P., Xu, E., & Ouyang, T. (2024). Enhanced state-of-charge and state-of-health estimation of lithium-ion battery incorporating machine learning and swarm intelligence algorithm. Journal of Energy Storage, 83, 110755.
Li, D., Zhimao, M., Mingsheng, C., & Jin, D. (2021). Influence factors and identification of state-of-health for traction battery. In IOP Conference Series: Materials Science and Engineering (Vol. 1043, No. 5, p. 052037). IOP Publishing.
Bhangu B S, Bentley P and Stone D A 2005 IEEE Trans. Veh.Technol. 54 783–94.
Jiayi T 2016 Science and Technology Outlook. 26 267–8.
Weng, C., Sun, J., & Peng, H. (2014). A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. Journal of power Sources, 258, 228-237.
N. Xue, "Design and optimization of Li-ion batteries for electric-vehicle applications", 2014.
A. Hamidi, L. Weber and A. Nasiri, "EV charging station integrating renewable energy and second-life battery", Proc. Int. Conf. Renew. Energy Res. Appl. (ICRERA), pp. 1217-1221, Oct. 2013.
R. R. Kumar, C. Bharatiraja, K. Udhayakumar, S. Devakirubakaran, K. S. Sekar and L. Mihet-Popa, "Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications," in IEEE Access, vol. 11, pp. 105761-105809, 2023, doi: 10.1109/ACCESS.2023.3318121.
Park, J.; Lee, M.; Kim, G.; Park, S.; Kim, J. Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH. Energies 2020, 13, 2138. https://doi.org/10.3390/en13092138
Yang, F., Xu, Y., Su, L., Yang, Z., Feng, Y., Zhang, C., & Shao, T. (2024). State of charge and state of health estimation of lithium-ion battery packs with inconsistent internal parameters using dual extended Kalman filter. Journal of Electrochemical Energy Conversion and Storage, 21(1), 011004.
Fahmy, H. M., Hasanien, H. M., Alsaleh, I., Ji, H., & Alassaf, A. (2024). State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach. Journal of Energy Storage, 88, 111557.
Wang, C., Su, Y., Ye, J., Xu, P., Xu, E., & Ouyang, T. (2024). Enhanced state-of-charge and state-of-health estimation of lithium-ion battery incorporating machine learning and swarm intelligence algorithm. Journal of Energy Storage, 83, 110755.
Li, D., Zhimao, M., Mingsheng, C., & Jin, D. (2021). Influence factors and identification of state-of-health for traction battery. In IOP Conference Series: Materials Science and Engineering (Vol. 1043, No. 5, p. 052037). IOP Publishing.
de Castro, R., Moura, S., Esteves, R., & Corzine, K. (2024). Guest Editorial Introduction to the Special Section on Next Generation Zero-Emission Vehicles. IEEE Transactions on Vehicular Technology, 73(4), 4526-4529.
Camboim, M. M., & Giesbrecht, M. (2024). Online state of health estimation of lithium-ion batteries through subspace system identification methods. Journal of Energy Storage, 85, 111091.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sunil Yadav, Prof. Amit Kumar Asthana
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
