International Journal of Frontiers in Engineering Technology, 2021, 3(5); doi: 10.25236/IJFET.2021.030511.
Qinfeng Zhao, Yanping Cai and Xingjun Wang
Rocket Force University of Engineering, XiAn, China
It is of great significance to accurately determine the health status of lithium-ion batteries. To address the problem that the prediction of a single limit learning machine algorithm is prone to jumping, the method of using artificial fish swarm optimization to optimize the limit learning machine is proposed to try its best to predict the model of the remaining life of lithium-ion batteries. Firstly, the isovoltage discharge time is extracted as an indirect health factor, then the limit learning machine is optimised using the artificial fish swarm algorithm to build an indirect prediction model for the remaining life of Li-ion batteries, and finally a validation evaluation is carried out based on the NASA dataset B0005-B0006. The experimental results show that the proposed model predicts stable prediction results with high accuracy and small error in prediction results.
Lithium-ion batteries; Artificial fish swarming algorithms; Extreme learning machines; Remaining useful life
Qinfeng Zhao, Yanping Cai and Xingjun Wang. AFSA-ELM Based Prediction of the Remaining Useful Life of Lithium Batteries. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 5: 91-96. https://doi.org/10.25236/IJFET.2021.030511.
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