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A Method for Peak Power Prediction of Series-Connected Lithium-ion Battery Pack Using Extended Kalman Filter

Guangzhong Dong, Zonghai Chen, and Jingwen Wei
Department of Automation, University of Science and Technology of China (USTC), Hefei, China
Abstract—Rechargeable battery systems are key components of applications in on-board storage for Micro-grids and electric vehicles. One of the most important evaluation indexes for energy storage system is the peak power capability information, which is used to evaluate the instantaneous power capability of battery systems to release or absorb electrical energy. To give out an accurate peak power capability estimation method for series-connected lithium-ion battery pack, this paper first proposed an extended Kalman filter based state-of-charge estimation method. Then the estimated state-of-charges and predicted terminal voltages of the cells in a series-connected lithium-ion battery pack are regarded as the constraints of peak power capability. Finally, the proposed method is verified by experiments conducted on a 6-series LiFePO4 battery pack. 
 
Index Terms—battery storage, peak power capability, modeling, state estimation

Cite: Guangzhong Dong, Zonghai Chen, and Jingwen Wei, "A Method for Peak Power Prediction of Series-Connected Lithium-ion Battery Pack Using Extended Kalman Filter," International Journal of Mechanical Engineering and Robotics Research, Vol. 6, No. 2, pp. 134-139, March 2017. DOI: 10.18178/ijmerr.6.2.134-139
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