中圖分類號(hào)：TP13 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.222834 中文引用格式： 李暢,，王琪,，姜佳怡. 基于levy飛行優(yōu)化BOA-BP網(wǎng)絡(luò)的電池SOC估計(jì)[J]. 電子技術(shù)應(yīng)用，2023,，49(4)：88-91. 英文引用格式： Li Chang,，Wang Qi，Jiang Jiayi. Battery SOC estimation based on Levy flight optimization of BOA-BP network[J]. Application of Electronic Technique,，2023,，49(4)：88-91.
Battery SOC estimation based on Levy flight optimization of BOA-BP network
Li Chang，Wang Qi,，Jiang Jiayi
(College of Electronic Information Engineering,， Xi′an Technological University， Xi′an 710021,， China)
Abstract： At present, the power output of electric vehicles is mainly derived from power batteries, whose State of Charge (SOC) represents the remaining power of batteries. Accurate estimation of SOC is of great significance for the safety of battery use . Butterfly Optimization Algorithm (BOA) was improved and used to optimize BP neural network to estimate SOC of power battery, which solved the problems of long training time, slow convergence, low accuracy and easy to fall into local optimal solution. At the same time, the global search speed is improved, voltage and current are selected as input variables, SOC as output variables, and the weight and threshold of neural network are adjusted according to the size of error. Simulation results show that the error rate of SOC estimation results obtained after optimization is controlled within 1.1%, and this method has better robustness and faster optimization speed.
Key words : charged state estimation,；Butterfly optimization algorithm；BP neural network,；Levy flight
