Optimized Deep Convolutional Neural Networks Based State of Charge Estimation for Lithium-Ion Battery

Abstract

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-tosequence 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.

Chaotic games driven grey wolf optimization: optimal performance design and comprehensive analysis

“…GWO has gained much attention from the intelligent optimization algorithm community because of its simple and small number of parameters, clear and explicit algorithm structure, and easy-to-understand algorithm logic. Due to the GWO algorithm with a simple principle, the GWO is widely used in the global optimization problems of computer science [30], engineering science [31], and management science [32]. Negi et al [33] conducted a brief review of GWO, including the development and implementation of GWO, as well as the definition and distinction of several different types of GWO (such as standard GWO, binary GWO, improved GWO, improved GWO, improved discrete GWO, etc.).…”