基于BSA-RELM的纯电动汽车锂离子电池SOC估计

doi:10.3969/j.issn.1000-1158.2019.04.24

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摘要提出一种基于鸟群算法优化鲁棒极限学习机的锂离子电池荷电状态估计算法。鲁棒极限学习机克服了极限学习机不能处理异常值的缺点,提高了网络的预测准确率。利用鸟群算法优化鲁棒极限学习机的隐层节点数和调节因子等参数,解决隐层节点数和调节因子等参数难以确定的问题,可进一步提高网络的收敛速度,且利于寻找全局最优值。利用ADVISOR软件采集影响电池荷电状态的主要参数:电流、电压、温度和内阻等进行建模和测试。仿真结果表明,采用鸟群算法优化鲁棒极限学习机比BPNN、RBFNN和FNN的估计误差更小,具有更高的预测精度。

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	吴忠强
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	申丹丹
	戚松岐
	朱向东

关键词 ：计量学, 荷电状态, 锂离子电池, 纯电动汽车, 鸟群算法, 鲁棒极限学习机

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.

Key words： metrology SOC Li-ion battery PEV BSA robust extreme learning machine

收稿日期: 2018-01-24 发布日期: 2019-06-10

PACS:

TB971

基金资助:河北省自然科学基金(F2016203006)

通讯作者: 吴忠强 E-mail: mewzq@163.com

作者简介: 吴忠强(1966-),男,上海人,博士,燕山大学教授,从事新能源发电系统控制研究。Email:mewzq@163.com

引用本文:

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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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2019.04.24 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2019/V40/I4/693

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