Volume 18: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part I

Optimized Deep Convolutional Neural Networks Based State of Charge Estimation for Lithium-Ion Battery Qiao Wang, Min Ye, Meng Wei, Gaoqi Lian, Chenguang Wu



To address the problem of the accuracy decrease of state of charge estimation caused by sudden high current impact, this paper proposes a lithium-ion battery SOC estimation method based on optimized deep convolutional neural network. Firstly, the 18650 battery was tested under actual driving conditions to obtain experimental data, and the experimental data was preprocessed by moving window to fit the two dimensional convolutional neural networks. Secondly, the proposed method was trained and tested, and the model parameters were further optimized. Thirdly, the proposed method is compared with sequence-to-sequence methods such as long short term memory and gated recurrent unit, and the results verify the superiority of the proposed method. This article provides a method to the battery SOC estimation, which is more conducive to practical applications.

Keywords lithium-ion battery, battery management system, state of charge, deep convolutional neural networks

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