This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Dynamic Correction Strategy for SOC Based on Discrete Sliding Mode Observer
Technical Paper
2019-01-1312
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Battery state estimation is one of the most important decision parameters for lithium battery energy management. It plays an important role in improving battery energy utilization, ensuring battery safety and enhancing system reliability. This paper is proposed to provide a dynamic correction of SOC in the full working condition, including static condition and dynamic condition. Based on the Coulomb-counting method, the current SOC value of the battery is calculated. Under the static conditions, the open circuit voltage of the battery is used to directly collect the initial SOC. Under the dynamic working conditions, the open circuit voltage of the battery is estimated by the sliding mode observer. Based on the deviation between the calculated and estimated values of the open circuit voltage, the current coefficient of the Coulomb-counting method is dynamically corrected by PI strategy. The research shows that the proposed strategy can effectively correct the deviation of SOC in the initial process and the discharge process, and avoid the problem that the existing “open circuit voltage + Coulomb-counting” method cannot dynamically correct the SOC deviation. When an upward deviation or a downward deviation occurs, the proposed method can quickly eliminate the deviations.
Recommended Content
Authors
Citation
Huang, D., Zhao, J., Zhu, Z., Li, C. et al., "Dynamic Correction Strategy for SOC Based on Discrete Sliding Mode Observer," SAE Technical Paper 2019-01-1312, 2019, https://doi.org/10.4271/2019-01-1312.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 |
Also In
References
- Kong , S.N. , Moo , C.S. , Chen , Y.P. et al. Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries Applied Energy 86 9 1506 1511 2009
- Hannan , M.A. , Lipu , M.S.H. , Hussain , A. et al. A Review of Lithium-Ion Battery State of Charge Estimation and Management System in Electric Vehicle Applications: Challenges and Recommendations Renewable and Sustainable Energy Reviews 78 834 854 2017
- Cheng , K.W.E. , Divakar , B.P. , Wu , H. et al. Battery-Management System (BMS) and SOC Development for Electrical Vehicles IEEE Transactions on Vehicular Technology 60 1 76 88 2011
- Li , Y. , Wang , L , Liao , C. et al. State-of-Charge Estimation of Lithium-Ion Battery Using Multi-State Estimate Technic for Electric Vehicle Applications Vehicle Power and Propulsion Conference 2013 1 5
- Xu , J. , Gao , M. , He , Z. et al. State of Charge Estimation Online Based On Ekf-Ah Method for Lithium-Ion Power Battery International Congress on Image and Signal Processing 2009 1 5
- Unterrieder C , Priewasser R , Marsili S. et al. Battery State Estimation Using Mixed Kalman/Hinfinity, Adaptive Luenberger and Sliding Mode Observer Vehicle Power and Propulsion Conference 2013 1 6
- Shiqiang , L , Zipeng , Z , Fang , W. et al. Experiment and Research on Cycle Life of Hybrid Electric Vehicle Traction Battery System 2018
- Yoshio , M. , Brodd , R.J. , and Kozawa , A. Lithium-Ion Batteries New York Springer 2010 804 807
- Chen , Q. , Jiang , J. , Liu , S. et al. A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries Journal of Power Electronics 16 3 1131 1140 2016