摘要
锂离子电池的荷电状态(state of charge,SOC)是新能源电动汽车电池管理系统中的关键参数。针对复杂运行环境下锂离子电池SOC预测精度不足等问题,提出了一种基于Transformer神经网络的电动汽车锂离子电池SOC智能预测方法。以日产Leaf电池为研究对象,搭建了新能源电动汽车锂离子电池充放离子电测试平台,模拟用户的真实能量需求及实时能量需求的动态变化,动态调整电池的充放电策略,采集多维度电池数据并进行预处理。构建基于Transformer模型的SOC预测框架,通过神经网络提取复杂时间序列特征,实现了对锂离子电池SOC的高精度预测。实验结果表明,提出的方法在预测精度上优于其他网络,其平均绝对误差低于1.51%,均方根误差(root mean square error,RMSE)低于0.48%,验证了该方法的有效性和准确性。
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.
作者
牛乐天
杨绍杰
郭天一
张微
NIU Letian;YANG Shaojie;GUO Tianyi;ZHANG Wei(College of Aerospace Engineering,Shenyang Aerospace University,Shenyang 110136,China;Liaoning General Aviation Academy,Shenyang 110136,China;Department of Mechanical and Aerospace Engineering,University of California San Diego,La Jolla 92093,USA)
出处
《沈阳航空航天大学学报》
2025年第4期75-82,共8页
Journal of Shenyang Aerospace University
基金
国家自然科学基金(项目编号:11902202)。
