This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
On-line Lithium-Ion Battery State-of-Power Prediction by Twice Recursive Method Based on Dynamic Model
Technical Paper
2019-01-1311
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
State-of-Power (SoP) prediction of Li-ion battery is necessary in battery management system for electric vehicles in order to deal with limited conditions, prevent overcharge and over discharge situations, increase the life of the battery and provide effective battery operation. This article suggests a method to on-line predict the 10-s charge and discharge peak power of Li-ion battery by twice recursions. First with the dynamic battery model we use the first recursion based on a least square method to get parameters which are influenced by the state of charge of Li-ion battery and temperature, etc. The dynamic model is an equivalent circuit model. Current and voltage are input online into the battery model. By recursive least square method the parameters are updated in real time. Moreover, when we use a recursive method to get real-time parameters, we add an extra proper factor to abandon old datum, which increases the real-time capability of state-of-power prediction. By assuming a constant current input and using the dynamic model we get the present dynamic voltage. Then by the second recursion, we derive the formula of 10-s resistance and calculate the SoP which can last for 10 seconds. The variables of the formula are the parameters which we get directly from the first recursion. Without using the parameters to calculate ohmic resistance, polarization resistance or capacitance of battery, it reduces much calculation amount and improves the calculation speed. This method is validated with datum from NEDC tests of Li-ion battery. The 10-s resistance values are predicted accurately. The method is suitable for the application in the battery management system of electric vehicles.
Recommended Content
Authors
Citation
Wang, X., Dai, H., and Wei, X., "On-line Lithium-Ion Battery State-of-Power Prediction by Twice Recursive Method Based on Dynamic Model," SAE Technical Paper 2019-01-1311, 2019, https://doi.org/10.4271/2019-01-1311.Also In
References
- Chen , Z. , Xiong , R. , and Cao , J. Particle Swarm Optimization-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles Considering Uncertain Driving Conditions Energy 96 197 208 2016
- Sun , F. , Xiong , R. , and He , H. Estimation of State-of-Charge and State-of-Power Capability of Lithium-Ion Battery Considering Vary Health Conditions Journal of Power Sources 259 166 176 2014
- Anderson , R. D. , Zhao , Y. , Wang , X. et al. Real Time Battery Power Capability Estimation Proceedings of the American Control Conference 2012 592 597
- 2003
- Hu , Y. 2012
- Waag , W. , Fleischer , C. , and Sauer , D. Adaptive On-Line Prediction of the Available Power of Lithium-Ion Batteries Journal of Power Sources 242 548 559 2013
- Dong , T.K. , Kirchev , A. , Mattera , F. , Kowal , J. , and Bultel , Y. Dynamic Modeling of Li-Ion Batteries Using an Equivalent Electrical Circuit Journal of the Electrochemical Society 158 3 A326 2011
- Hu , X. , Li , S. , and Peng , H. A Comparative Study of Equivalent Circuit Models for Li-Ion Batteries Journal of Power Sources 198 359 367 2012
- Xiong , R. , He , H. , Sun , F. et al. Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach Energies 5 12 1455 1469 2012
- Plett , G. High-Performance Battery-Pack Power Estimation Using a Dynamic Cell Model IEEE Transactions on Vehicular Technology 53 1586 1593 2004
- Sun , F. , Xiong , R. , He , H. et al. Model-Based Dynamic Multi-Parameter Method for Peak Power Estimation of Lithium-Ion Batteries Applied Energy 96 3 378 386 2012
- Dai , H. , Sun , Z. , and Wei , X. Estimation of Internal States of Power Lithium-ion Batteries Used on Electric Vehicles by Dual Extended Kalman Filter Chinese Journal of Mechanical Engineering 45 06 95 101 2009
- Cheng , Z. , Sun , X. , and Cheng , S. Method for Estimation of State of Charge and Power Prediction of Lithium-Ion Battery Transactions of China Electrotechnical Society 32 15 180 189 2017
- Schmidt A P , Bitzer M , Árpád W. , et al. Experiment-Driven Electrochemical Modeling and Systematic Parameterization for a Lithium-Ion Battery Journal of Power Sources 2010 195 15 5071 5080
- Zhu , C , Li , X , Wei , G , and Pei , L.
- Liu , L. , Wang , L.Y. , Chen , Z. , Wang , C. et al. Integrated System Identification and State-of-Charge Estimation of Battery Systems IEEE Transactions on Energy Conversion 28 1 12 23 2013
- Zhang , C.P. , Zhang , C.N. , and Li , J.Q. Research on Peak Power Estimation for Traction Battery Pack Journal of System Simulation. 22 6 1524 1527 2010