摘要
锂离子电池健康状态估计对于确保其安全使用和优化电动汽车电池组的能量管理至关重要。针对现有方法在预测精度方面的不足,文章提出了一种结合改进自适应噪声互补集合经验模态分解(ICEEMDAN)、融合鱼鹰优化和柯西变异的麻雀搜索算法(OCSSA)以及深度极限学习机(DELM)的混合模型。利用ICEEMDAN算法分解电池容量衰退数据,分离高频和低频分量。通过混沌映射、鱼鹰优化和柯西变异改进麻雀搜索算法(SSA)提高参数优化精度。利用OCSSA优化DELM模型参数,增强预测准确性。为验证所提出方法的有效性,文章基于美国国家航空和宇宙航行局(NASA)和先进寿命周期工程中心(CALCE)的数据集进行测试,实验结果证明该模型具有高预测精度和稳定性,模型预测的平均绝对误差及均方根误差均在1%以内,并且在预测未来多步时,仍然保持高的预测精度。
Lithium-ion battery health estimation is critical for ensuring their safe use and optimizing the energy management of electric vehicle battery packs.To overcome the shortcomings of existing methods in prediction accuracy,a hybrid model combining improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),osprey-cauchy-sparrow search algorithm(OCSSA),and deep extreme learning machine(DELM)is proposed.ICEEMDAN algorithm is used to decompose the battery capacity decay data and separate the high-frequency and low-frequency components.The sparrow search algorithm(SSA)is improved by chaotic mapping,osprey optimi-zation and cauchy variation to improve the parameter optimization accuracy.OCSSA optimizes DELM model parameters to enhance prediction accuracy.In order to verify the effectiveness of the proposed method,this paper tests the data set of the national aeronautics and space administration(NASA)and the center for advanced life cycle engineering(CALCE).The experimental results show that the model has high prediction accuracy and stability,and the average absolute error and root mean square error of the model prediction are within 1%.Moreover,high prediction accuracy is still maintained when predicting multiple steps in the future.
作者
刘轶鑫
解志鹏
雷奥
杨佳
刘鹏飞
LIU Yixin;XIE Zhipeng;LEI Ao;YANG Jia;LIU Pengfei(China FAW Science and Technology Innovation Base,China FAW Group Company Limited,Changchun 130012,China;College of Automotive Engineering,Jilin University,Changchun 130025,China)
出处
《汽车实用技术》
2025年第4期1-8,26,共9页
Automobile Applied Technology
基金
吉林省长春市重大科技专项课题(20210301027GX)。
关键词
锂离子电池
健康状态预测
混合模型
参数优化
lithium-ion battery
state of health prediction
hybrid model
parameter optimization
作者简介
刘轶鑫(1987-),男,高级工程师,研究方向为新能源汽车动力电池管理系统,E-mail:liuyixin@faw.com.cn;通信作者:解志鹏(2002-),男,硕士研究生,研究方向为新能源汽车,E-mail:2981373096@qq.com。
