摘要
鉴于锂电池健康管理在新能源电动车运行和维护中的重要性,针对锂电池容量衰退所表现的非高斯性和非线性问题,以及无迹粒子滤波算法UPF在处理协方差矩阵时可能得到无效结果的问题,提出一种基于改进无迹粒子滤波方法IUPF的锂电池寿命预测方法。本方法从数据驱动角度出发,选用双指数模型进行容量预测,基于NASA PCoE的锂电池数据集完成相关的实验验证。实验结果表明,在估计误差、平均绝对误差和均方根误差三顶指标上,IUPF算法性能均优于UPF算法,可以实现对锂电池容量的精确预测和剩余使用寿命的有效估计。
In view of the importance of lithium battery health management in the operation and main-tenance of new energy electric vehicles,aiming at the non-Gaussian and nonlinear problems of lithium battery capacity decline and the problem that Unscented Particle Filter(UPF)algorithm may get invalid results when processing covariance matrix,a lithium battery life prediction method based on Improved Unscented Particle Filter(IUPF)method is proposed.From the data-driven point of view,the method selects the double exponential model to predict the capacity,and completes the relevant experimental verification based on the lithium battery data set of NASA PCoE.The experimental results show that the performance of IUPF algorithm is better than that of UPF algorithm in estimation error,average absolute error and root mean square error,and it can accurately predict the capacity of lithium batteries and effectively estimate the remaining usful life.
作者
胡小惠
李丽敏
王海
杨雪崟
孙思伟
刘楷琛
HU Xiaohui;LI Limin;WANG Hai;YANG Xueyin;SUN Siwei;LIU Kaichen(School of Electronics and Information,Xi'an Polytechnic University,Xi'an 710600,China)
出处
《微处理机》
2023年第6期27-30,共4页
Microprocessors
关键词
锂电池
容量预测
无迹粒子滤波
RUL预测
Lithium battery
Capacity prediction
Unscented particle filtering
RUL prediction
