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

doi:10.16182/j.issn1004731x.joss.201803025

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.

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

CLC Number:

TM912

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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