汽车工程学报2026，Vol.16Issue(5)：805-817,13.DOI:10.3969/j.issn.2095‒1469.2026.05.08
基于DBO-BP神经网络增强EKF算法的锂电池荷电状态估计
State-of-Charge Estimation for Lithium Batteries Based on a DBO-BPNN-Enhanced EKF Algorithm
摘要
Abstract
To address the sensitivity to initial weights and thresholds in traditional Back Propagation(BP)neural networks for lithium battery state of charge(SOC)estimation,this paper proposes a BP neural network optimization method based on the Dung Beetle Optimizer(DBO)algorithm,which is integrated with the Extended Kalman Filter(EKF)algorithm to enhance SOC estimation accuracy.First,a second-order RC equivalent circuit model for the lithium battery is constructed,and the battery parameters are identified through hybrid pulse power characterization(HPPC)experiments.On this basis,the DBO algorithm is used to optimize the BP neural network offline,thereby providing online compensation for the SOC estimation error of the EKF algorithm.To validate the effectiveness of the proposed method,simulation experiments were conducted under the dynamic stress test(DST)and federal urban driving schedule(FUDS)conditions.The simulation results demonstrate that,compared to the traditional BP-EKF algorithm,the proposed method exhibits significant advantages in SOC estimation accuracy and convergence,enabling more accurate tracking of SOC changes.Specifically,the root mean square error(RMSE)and mean absolute error(MAE)of the proposed method are reduced by 45%and 48%,respectively.In addition,the improvement in accuracy is more significant under high environmental noise.The results confirm that the proposed DBO-BP-EKF algorithm effectively overcomes the limitations of traditional methods,providing a more reliable technical solution for lithium battery SOC estimation.关键词
锂电池/荷电状态估计/EKF算法/蜣螂优化算法Key words
lithium battery/state of charge estimation/EKF algorithm/dung beetle optimization algorithm分类
交通工程引用本文复制引用
余曼,张凯,刘建峰,赵轩..基于DBO-BP神经网络增强EKF算法的锂电池荷电状态估计[J].汽车工程学报,2026,16(5):805-817,13.基金项目
国家自然科学基金项目(52472397) （52472397）
陕西省重点研发计划项目(2024GX-YBXM-260) （2024GX-YBXM-260）
西安市"揭榜挂帅"制技术攻关项目(24JBGS0006) （24JBGS0006）
咸阳市重点研发计划项目(L2023-ZDYF-QYCX-032) （L2023-ZDYF-QYCX-032）
长安大学中央高校基本科研业务费专项资金资助(300102254101) （300102254101）
