期刊
IEEE TRANSACTIONS ON POWER ELECTRONICS
卷 29, 期 11, 页码 5905-5920出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2014.2300143
关键词
Battery model; battery storage system; online parameter estimation; particle swarm optimization (PSO); state of charge (SOC)
资金
- Innovation and Technology Fund of the Hong Kong Special Administrative Region, China [GHP/017/12SZ]
- National High-Technology Research and Development Program (863 Program) of China [2013AA01A212]
- Natural Science Foundation of China (NSFC) for Distinguished Young Scholars [61125205]
- NSFC [61332002, 61300044]
A modified particle swarm optimization algorithm for conducting near-real-time parameter estimation of an electrical model for lithium batteries is presented. The model comprises a dynamic capacitance for characterizing the nonlinear relationship between the battery electromotive force and the state-of-charge, and a resistor-capacitor network for characterizing the static and transient responses. The algorithm is confirmed by successfully determining all parameters in a predefined simulation model. It is also evaluated on a hardware test bed with two samples of 3.3-V, 40-Ah, Lithium Iron Phosphate (LiFePO4) battery driven under six different loading patterns. The intrinsic parameters are estimated by first processing 15-min samples of the battery terminal voltage and current. The whole process takes 2 min. Then, the voltage-current characteristics in the following 15 min are predicted. Results show that the extracted parameters can fit the first 15-min voltage samples with a maximum error of 16 mV and an average error of 3.8 mV. With the extracted parameters, the electrical model can predict voltage-current characteristics in the following 15 min with a maximum error of 31 mV and an average error of 15 mV. The algorithm is further verified by successfully determining the emulated variation of the output resistance.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据