On-line Lithium-Ion Battery State-of-Power Prediction by Twice Recursive Method Based on Dynamic Model
Published April 2, 2019 by SAE International in United States
Annotation of this paper is available
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.
CitationWang, 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.
- 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,” in Proceedings of the American Control Conference, 2012, 592-597.
- “Freedom CAR Battery Test Manual for Power-Assist Hybrid Electric Vehicles,” United States Idaho National Engineering & Environmental Laboratory, 2003
- Hu, Y., “Prediction Status of Peak Power of Battery on HEV,” Harbin University of Science and Technology, 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., “Peak Power Prediction Method for Power Battery,” CN104267354B, 2017.03.01.
- 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.