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Estimating the Power Limit of a Lithium Battery Pack by Considering Cell Variability
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2015 by SAE International in United States
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Power limit estimation of a lithium-ion battery pack can be employed by a battery management system (BMS) to balance a variety of operational considerations, including optimization of pulse capability while avoiding damage and minimizing aging. Consideration of cell-to-cell performance variability of lithium-ion batteries is critical to correct estimation of the battery pack power limit as well as proper sizing of the individual cells in the battery. Further, understanding of cell variability is necessary to protect the cell and other system components (e.g., fuse and contactor, from over-current damage). In this work, we present the use of an equivalent circuit model for estimation of the power limit of lithium-ion battery packs by considering the individual cell variability under current or voltage constraints. We compare the power limit estimation by using individual cell characteristics compared to the estimate found using only max/min values of cell characteristics. Finally, we consider how the estimation capability and variability will vary with the total number of the cells in the battery pack.
CitationJin, Z., Zhang, Z., Aliyev, T., Rick, A. et al., "Estimating the Power Limit of a Lithium Battery Pack by Considering Cell Variability," SAE Technical Paper 2015-01-1181, 2015, https://doi.org/10.4271/2015-01-1181.
- Idaho National Engineering & Environmental Laboratory. “Battery Test Manual for Plug-in Hybrid Electric Vehicles”; Assistant Secretary for Energy Efficiency and Renewable Energy (EE) Idaho Operations Office: Idaho Falls, ID, USA, 2010
- Xiong, R., He, H., Sun F., and Zhao, K., “Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach,” Energies, 2012, 5, 1455-1469; doi:10.3390/en5051455
- Plett, G., “Battery Management System Algorithms for HEV Battery State-of-Charge and State-of-Health Estimation,” Advanced Materials and Methods for Lithium-Ion Batteries, ISBN 978-81-7895-279-6.
- Xiong, R., He, H., Sun F., Liu F., and Liu Z., “Model-based State of Charge and Peak Power Capability Joint Estimation of Lithium-Ion Battery in Plug-in Hybrid Electric Vehicles,” Journal of Power Sources 229: 159-169, 2013.
- Suthar, B., Ramadesigan V., et al., “Optimal Control and State Estimation of Lithium-Ion Batteries Using Reformulated Models,” Proceedings of American Control Conference, 2013.
- Sampathnarayanan B., Serrao L., et al., “Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles,” Proceedings of the ASME 2009 Dynamic Systems and Control Conference, 2009.
- Di Cariano, S., Bernardini, D., Bemporad, A., and Kolmanovsky, I., “Stochastic MPC with Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management,” IEEE Transactions On Control Systems Technology, 2013.
- hu Xiasong, Li Shengbo, Peng Huei, “A comparative Study of Equivalent Circuit Models for Li-ion Batteries” Journal of Power Source 198 (2012) 359-367
- Hu, Y., Yurkovich, S., Guezennec, Y., and Yurkovich, B.J., “A technique for dynamic battery model identification in automotive applicants using linear parameter varying structures”, Control Engineering Practice, vol. 17, pp. 1190-1201, 2009.
- Subramanian, V.R. et al, “Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions”, Journal of the Electrochemistry Society 156, no. 4 (2009): A260-A271.
- Wu, S-L., et al. “High Rate Capability of Li(Ni1/3Mn1/3Co1/3)O2 Electrode for Li-ion Batteries.” Journal of the Electrochemistry Society 159, no. 4 (2012): A438-A444.