基于改进在线核极限学习机的蓄电池SOC预测

doi:10.16182/j.issn1004731x.joss.201803025

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (3): 969-975.doi: 10.16182/j.issn1004731x.joss.201803025

基于改进在线核极限学习机的蓄电池SOC预测

孙玉坤^1,2, 李曼曼¹, 黄永红¹

1.江苏大学电气信息工程学院,江苏镇江 212013;
2.南京工程学院电力工程学院,江苏南京 211167

收稿日期:2016-03-25 出版日期:2018-03-08 发布日期:2019-01-02
作者简介:孙玉坤(1958-),男,江苏靖江,博士,教授,博导,研究方向为电机智能控制等;李曼曼(1990-),女,江苏徐州,硕士生,研究方向为车载复合电源SOC估算与数字控制系统。
基金资助:
国家自然科学基金(51377074)

SOC Prediction of Battery Based on Improved Online Kernel Extreme Learning Machine

Sun Yukun^1,2, Li Manman¹, Huang Yonghong¹

1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;
2.School of Electrical Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Received:2016-03-25 Online:2018-03-08 Published:2019-01-02

摘要/Abstract

摘要： 为对蓄电池荷电状态(SOC)进行准确、快速的在线预测,提出一种改进的在线核极限学习机方法(IO-KELM),以电池工作电压、电流和表面温度为输入量,电池SOC为输出量建立预测模型。IO-KELM采用Cholesky分解将核极限学习机(KELM)从离线模式扩展到在线模式,使网络输出权值随新样本的逐次加入递推求解更新,以简单的四则运算替代复杂的矩阵求逆,提高了网络的泛化能力和在线学习效率。仿真实验表明,相比于KELM及直接在线建模的KELM算法(DO-KELM),IO-KELM具有更高的预测精度、更强的鲁棒性及更快的计算速度。

关键词: 蓄电池, 荷电状态, 核极限学习机, Cholesky分解, 在线预测

Abstract: In order to conduct an accurate and fast online prediction for the state of charge (SOC) of battery, an improved online kernel extreme learning machine (IO-KELM) algorithm is proposed. In this work, a prediction model is presented with charge voltage, current and surface temperature as inputs and SOC of battery as output. The IO-KELM adopts Cholesky factorization to extend the kernel extreme learning machine (KELM) from offline mode to online mode. Meanwhile, the output weights of the network are updated by successive join of the new samples, and the matrix inverse operation is replaced with arithmetic. Hence, the generalization ability and the computational efficiency of the model are improved. Compared with KELM and direct online-KELM (DO-KELM) algorithm, simulation results indicate that the IO-KELM has higher prediction accuracy, stronger robustness and faster calculation speed.

Key words: battery, state of charge (SOC), kernel extreme learning machine (KELM), Cholesky factorization, online prediction

中图分类号:

TM912

孙玉坤, 李曼曼, 黄永红. 基于改进在线核极限学习机的蓄电池SOC预测[J]. 系统仿真学报, 2018, 30(3): 969-975.

Sun Yukun, Li Manman, Huang Yonghong. SOC Prediction of Battery Based on Improved Online Kernel Extreme Learning Machine[J]. Journal of System Simulation, 2018, 30(3): 969-975.

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基于改进在线核极限学习机的蓄电池SOC预测

SOC Prediction of Battery Based on Improved Online Kernel Extreme Learning Machine

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