JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 34 Issue 4
July 2019
Article Contents
WANG Zhen and LU Jingui. Application of improved ACO-BP neural network in estimation of SOC of lithium ion battery[J]. Journal of Light Industry, 2019, 34(4): 81-86. doi: 10.3969/j.issn.2096-1553.2019.04.012
Citation: WANG Zhen and LU Jingui. Application of improved ACO-BP neural network in estimation of SOC of lithium ion battery[J]. Journal of Light Industry, 2019, 34(4): 81-86. doi: 10.3969/j.issn.2096-1553.2019.04.012 shu

Application of improved ACO-BP neural network in estimation of SOC of lithium ion battery

  • Received Date: 2019-02-07
  • Aiming at the problem that SOC of lithium ion batteries is easy to fall into local optimum by single BP neural network, ant colony algorithm was introduced and combined with BP neural network model,and an improved ACO-BP neural network was proposed to estimate battery SOC.Inertia correction algorithm was used to add inertia amount when correcting the weight threshold to improve BP neural network. ACO algorithm was improved using the improved global pheromone updating rules to solve the problem of premature convergence.The improved ACO-BP neural network was applied to estimate SOC of 18650 lithiumion power battery. The results showed that the relative error of the improved ACO-BP neural network in estimating SOC could be controlled within ±1.957% and the MAPE was 0.897%. The accuracy and stability of the improved ACO-BP neural network were obviously better than those of single BP neural network and ACO-BP neural network.
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