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| The SOC Prediction Method of ELM Optimized by NTLBO Algorithm |
| HU Jian1, LIU Chao2 |
1. Information Technology Department, Zhejiang Institute of Economics and Trade, Hangzhou,Zhejiang 310018, China
2. Guizhou Space Appliance Co. Ltd., Guiyang, Guizhou 550009, China |
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Abstract In order to improve the prediction accuracy of state of charge (SOC), a prediction method of SOC based on optimized extreme learning machine ELM by the new teaching-learning-based optimization (NTLBO) algorithm was proposed. Firstly, Logistics chaos was employed to optimize the elite individuals in the population to improve the global optimization performance of the algorithm. Secondly, the input weights and hidden layer thresholds of the ELM model were optimized and adjusted by the improved TLBO algorithm, and the NTLBO-ELM prediction model was constructed to improve the generalization ability of the model. The NTLBO-ELM model was tested and verified on a lithium battery and compared with the other three models. The simulation results show that the proposed method has a small prediction error and good generalization ability, which verifies the validity of the model.
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Received: 25 June 2021
Published: 06 January 2022
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2022, Vol. 43 
