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

Authors

Yukun Sun, 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;
Manman Li, 1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; ;
Yonghong Huang, 1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; ;

Abstract

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.

Recommended Citation

Sun, Yukun; Li, Manman; and Huang, Yonghong (2019) "SOC Prediction of Battery Based on Improved Online Kernel Extreme Learning Machine," Journal of System Simulation: Vol. 30: Iss. 3, Article 25.
DOI: 10.16182/j.issn1004731x.joss.201803025
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/25

First Page

969

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201803025

Last Page

975

CLC

TM912

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201803025