An overview of State of Charge (SOC) and State of Health (SOH) estimation methods of Li-ion batteries

Abstract

Battery Management System (BMS) is an essential component for lithium-ion battery-based devices. It provides a variety of functionalities that help improve the overall lifespan of the battery, including states estimation algorithms. An accurate estimation of the battery State Of Health (SOH) and State Of Charge (SOC) is a crucial task that an advanced battery management system should perform. This paper aims to outline the most relevant battery model types that were used in literature for Electric Vehicle (EV) applications. An overview of the estimation algorithms that estimate the battery state of charge and state of health are presented and simulations of some methods are also illustrated in order to test their accuracy.

Battery management system | State Of Health | State Of Charge | Lithium-ion

References

1. A., F., J., A. D., K., P., & Longo S. and Wild, M. (2016). A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur. Renewable and Sustainable Energy Reviews, 56, 1008-1021.
   
2. A., L., A., S., Venet, P., A., H., M., O.-B., & S., P. (2016). Practical online estimation of lithium-ion
   battery apparent series resistance for mild hybrid vehicles. IEEE Transactions on Vehicular Technology, 65, 4505–4511.
   
3. Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Mierlo, J. V., & Bossche., P. V. (2016). Critical
   review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, 56 , 572-587
   
4. Burgos, C., Saez, D., Orchard, M. E., & Cardenas, R. (2015). Fuzzy modelling for the state-of-charge estimation of lead-acid batteries. Journal of Power Sources , 355-366.
   
5. Chang, W.-Y. (2013). The State of Charge Estimating Methods for Battery: A Review. International Scholarly Research Notices, 7.
   
6. Chen, X., X., S. W., Z., C., & A., K. ( 2012). Sliding Mode Observer for State of Charge Estimation
   Based on Battery Equivalent Circuit in Electric Vehicles. Australian Journal of Electrical and
   Electronics Engineering, 225–234.
   
7. E. Kamal, A. E. (2015). State of charge estimation based on extended Kalman filter algorithm for
   Lithium-Ion battery. 23rd Mediterranean Conference on Control and Automation (MED).
   
8. Fan, G., & Canova, X. L. (2017). A Reduced-Order Electrochemical Model of Li-Ion Batteries for
   Control and Estimation Applications. IEEE Transactions on Vehicular Technology, 67(1),
   76-91.
   
9. Fleischer, C., W., W., Z., B., & D.U., S. (2013). Adaptive on-line state of available power prediction of lithium-ion batteries. Journal Power Electron, 13, 516–527.
   
10. G., F., X., L., & M., C. (2018). A Reduced-Order Electrochemical Model of Li-Ion Batteries for
    Control and Estimation Applications. IEEE Transactions on Vehicular Technology, 67(1),
    76–91.
    
11. Kailong, L., Kang, L., Qiao, P., & Cheng, Z. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering, 14(1),
    47-64.
    
12. Kempera, P., Li, S. E., & Kum, D. (2015). Simplification  dimensional battery model using
    dynamic profile of lithium concentration. Journal of Power Sources, 286, 510-525.
    
13. Kim, J. ( 2014). Fuzzy Logic-Controlled Online State-of- Health (SOH) Prediction in Large Format LiMn2O4 Cell for Energy Storage System (ESS) Applications
    
14. L, P. G. (2011). Recursive approximate weighted total least squares estimation of battery cell total capacity. Journal of Power Sources, 2319– 2331.
    
15. Mathew, M., Janhunen, S., Rashid, M., Long, F.,  Fowler, M. (2018). Comparative Analysis of
    Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems.
    Energies, 11, 1490.
    
16. Moura, S. J., Argomedo, F. B., Klein, R., & Krstic, A. M. (2016). Battery State Estimation for a Single Particle Model With Electrolyte Dynamics. IEEE Transactions on Control Systems
    Technology, 25(2), 453–468.
    
17. Nacer, M., Ahmed, B., & Naamane, A. (2012). Battery Models for Estimation of State of Charge by Sliding Mode Observer. Journal of Smart Innovation, Systems and Technologies, 133-
    149.
    
18. Ning, B., Xu, J., Cao, B., Wang, B., & Xu, G. ( 2016 ). A Sliding Mode Observer SOC Estimation
    Method Based on Parameter Adaptive Battery Model. Energy Procedia, 619 – 626
    
19. O., G., P., V., S., J., & Molinas, M. ( 2017). Battery modeling and Kalman filter-based State-of-
    Charge estimation for a race car application. IEEE 14th International Conference on
    Networking, Sensing and Control (ICNSC). .
    
20. Rincon-Mora, E. C. (2006). Accurate electrical batter model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion, 21(2), 504-511.
    
21. Sepasi, S., Ghorbani, R., & Liaw, B. Y. (2014). Improved extended Kalman filterfor state of charge
    estimation of battery pack. Journal of Power Sources , 368-376.
    
22. Sun, F., Hu, X., Zou, Y., & Li, S. (2011). Adaptive unscented Kalman filtering for state of charge
    estimation of a lithium-ion battery for electric vehicles. Energy, 36, 3531-3540.
    
23. Ungurean, L., Cârstoiu, G., & Groza, M. V. (2016). Battery state of health estimation: a structured
    review of models, methods and commercial devices. International Journal of Energy Research, 41(2), 151-181.
    
24. Wassiliadisa, N., Adermanna, J., Frericksb, A., Pakb, M. Reitera, C., Lohmannb, B., & Lienkampa., M. (2018). Revisiting the dual extended Kalman filter for battery state-of-charge and state-ofhealth estimation: A use-case life cycle analysis. Journal of Energy Storage, 73-87.
    
25. Yu, J., Mo, B., Tang, D., Yang, J., Wan, J., & Liu, J. (2017). Indirect State-of-Health Estimation for
    Lithium-Ion Batteries under Randomized Use. Energies.