沈阳航空航天大学学报2025，Vol.42Issue(4)：75-82,8.DOI:10.3969/j.issn.2095-1248.2025.04.011
基于Transformer的锂离子电池荷电状态智能预测方法
A Transformer-based intelligent prediction method for lithium-ion battery state of charge
摘要
Abstract
The state of charge(SOC)of lithium-ion batteries is a critical parameter in the battery management system of new energy electric vehicles.To address the issue of insufficient SOC prediction accuracy for lithium-ion batteries under complex operating conditions,an intelligent SOC prediction method for electric vehicle lithium-ion batteries based on the Transformer neural network was proposed.Taking the Nissan Leaf battery as the research object,a charging and discharging test platform for new energy electric vehicle lithium-ion batteries was built to simulate the real energy demands of users and the dynamic changes in real-time energy needs.This platform dynamically adjusted the battery's charging and discharging strategies,collected multi-dimensional battery data,and preprocessed the data.Then,a SOC prediction framework based on the Transformer model was constructed,which extracted complex time series features through neural networks,achieved high-precision predictions of lithium-ion battery SOC.The experimental results indicate that the proposed method outperforms other networks in prediction accuracy,with a mean absolute error of less than 1.51%and a RMSE of less than 0.48%,validating the effectiveness and accuracy of this method.关键词
荷电状态/新能源电动汽车/电池管理系统/Transformer/锂离子电池Key words
state of charge/new energy electric vehicle/battery management system/Transformer/lithium-ion battery分类
信息技术与安全科学引用本文复制引用
牛乐天,杨绍杰,郭天一,张微..基于Transformer的锂离子电池荷电状态智能预测方法[J].沈阳航空航天大学学报,2025,42(4):75-82,8.基金项目
国家自然科学基金(项目编号:11902202). （项目编号:11902202）
