Advancements in State-of-Charge (SOC) and State-of-Health (SOH) Estimation Techniques for Battery Management Systems
DOI:
https://doi.org/10.24113/ijoscience.v11i2.543Keywords:
State-of-charge (SOC), battery management systems (BMS), state-of-health (SOH), electric vehicles (EVs), SOC estimation methods, Kalman filter, data-driven techniques, battery health monitoring.Abstract
An overview of the crucial function that state-of-charge (SOC) estimate plays in Battery Management Systems (BMS) for electric vehicles (EVs) is given in the abstract. By tracking the amount of energy that is available, SOC estimate is crucial for guaranteeing effective and secure operation. This paper examines several ways that have been developed for SOC estimation, including as data-driven, model-based, and direct approaches, each with its own advantages and disadvantages. Model-based approaches strike a compromise between accuracy and complexity, but they need accurate battery models, whereas data-driven approaches rely on large training datasets. Despite difficulties in real-time application, emerging technologies like artificial intelligence algorithms and Kalman filters are improving the accuracy of SOC
estimation. The paper also examines state-of-health (SOH) assessment techniques that concentrate on battery performance and longevity, ranging from data-driven methods to electrochemical models. It is expected that forthcoming developments in cloud-based BMS systems and smart BMS applications would surmount present computational constraints and enhance battery management capabilities in electric vehicles.
Downloads
References
Di Luca G, Di Blasio G, Gimelli A, Misul DA. Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles. Energies. 2024; 17(1):202. https://doi.org/10.3390/en17010202.
Ren, Z.; Du, C. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries. Energy Rep. 2023, 9, 2993–3021. [Google Scholar] [CrossRef]
Li, W.; Rentemeister, M.; Badeda, J.; Jöst, D.; Schulte, D.; Sauer, D.U. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. J. Energy Storage 2020, 30, 101557. [Google Scholar] [CrossRef]
Yang, S.; Zhang, Z.; Cao, R.; Wang, M.; Cheng, H.; Zhang, L.; Jiang, Y.; Li, Y.; Chen, B.; Ling, H.; et al. Implementation for a cloud battery management system based on the CHAIN framework. Energy AI 2021, 5, 100088. [Google Scholar] [CrossRef].
Lin, J., Yan, G., & Wang, C. (2021, September). Li-ion battery state of health prediction based on long short-term memory recurrent neural network. In Journal of Physics: Conference Series (Vol. 2010, No. 1, p. 012133). IOP Publishing.
Kumar, R. R., Bharatiraja, C., Udhayakumar, K., Devakirubakaran, S., Sekar, S., & Mihet-Popa, L. (2023). Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications. Ieee Access.
Zhou, D.M.; Zhang, K.; Ravey, A.; Gao, F.; Miraoui, A. Online Estimation of Lithium Polymer Batteries State-of-Charge Using Particle Filter-Based Data Fusion with Multimodels Approach. IEEE Trans. Ind. Appl. 2016, 52, 2582–2595. [Google Scholar] [CrossRef]
Li, C.; Cui, N.; Wang, C.; Zhang, C. Simplified electrochemical lithium-ion battery model with variable solid-phase diffusion and parameter identification over wide temperature range. J. Power Sources 2021, 497, 229900. [Google Scholar] [CrossRef].
Zhou L, Lai X, Li B, Yao Y, Yuan M, Weng J, Zheng Y. State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends. Batteries. 2023; 9(2):131. https://doi.org/10.3390/batteries9020131.
Murnane, M., & Ghazel, A. (2017). A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries. Analog devices, 2, 426-436.
J. Lv, B. Jiang, X. Wang, Y. Liu, and Y. Fu, ‘‘Estimation of the state of charge of lithium batteries based on adaptive unscented Kalman filter algorithm,’’ Electronics, vol. 9, no. 9, p. 1425, Sep. 2020.
C. She, Z. Wang, F. Sun, P. Liu, and L. Zhang, ‘‘Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network,’’ IEEE Trans. Ind. Informat., vol. 16, no. 5, pp. 3345–3354, May 2020.
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.
