Abstract：A method based on bird swarm algorithm optimizing robust extreme learning machine is proposed to estimate the charge state of the battery. Robust extreme learning machine overcomes the shortcomings that extreme learning machine can not deal with the abnormal value, so the prediction accuracy of the network is improved. The parameters such as the number of hidden nodes and the adjustment factors of robust extreme learning machine are optimized by bird swarm algorithm, so the problems that the parameters such as the number of hidden nodes and the adjustment factors are difficult to be determined can be solved, which can further improve the convergence speed of the network and help to find the global optimal value. Several key parameters including current, voltage, temperature and internal resistance, which affect the SOC characteristics of the battery, are collected to model and test by ADVISOR software. Simulation results show that compared with other algorithms such as BPNN, RBFNN and FNN, BSA-RELM has a smaller error and higher prediction accuracy.
吴忠强,尚梦瑶,申丹丹,戚松岐,朱向东. 基于BSA-RELM的纯电动汽车锂离子电池SOC估计[J]. 计量学报, 2019, 40(4): 693-699.
WU Zhong-qiang,SHANG Meng-yao,SHEN Dan-dan,QI Song-qi,ZHU Xiang-dong. Estimation of SOC of Li-ion Battery in Pure Electric Vehicle by BSA-RELM. Acta Metrologica Sinica, 2019, 40(4): 693-699.
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2019, Vol. 40 
