摘要
高精度的电池荷电状态估计是电动汽车电池管理系统的关键技术之一,其估计精度直接影响能量管理效率和汽车的续航里程。传统的滤波方法基于模型来估计电池SOC,但难以建立锂离子电池精确的数学模型。针对此问题,提出一种基于高斯过程回归的无迹卡尔曼滤波(UKF)锂离子电池SOC估计方法,使用高斯过程回归在有限的训练数据下建立等效电路模型的测量方程,在UKF和高斯过程回归之间建立关联。该模型能够充分联合利用现有实验数据和被预测实时状态数据,实现SOC估计。结果表明,与传统UKF相比,基于高斯过程回归的UKF算法具有较高精确性。
The high-precision state-of-charge(SOC)estimation of battery power capacity is the key technology associated with a battery management system,and its estimation accuracy directly influences the energy management efficiency and endurance mileage of electric vehicles.The traditional filter estimation method uses an estimation model and does not consider the accuracy model of a Li-ion battery.To solve this problem,an unscented Kalman filter(UKF)estimation method based on Gaussian process regression(GPR)is presented.GPR can be used to establish a measurement equation for an equivalent circuit model with limited training data,resulting in the connection of UKF and GPR.The proposed model optimally uses the data obtained via the tests and the current to estimate the SOC.The experimental results and comparative analysis of the UKF estimation method based on Gaussian process regression demonstrate the high prediction accuracy of the proposed algorithm during SOC estimation.
作者
魏孟
李嘉波
李忠玉
叶敏
徐信芯
WEI Meng;LI Jiabo;LI Zhongyu;YE Min;XU Xinxin(Highway Maintenance Equipment National Engineering Laboratory,Changan University,Xi'an 710064,Shaanxi,China;Henan Gaoyuan Highway Maintenance Technology Co.Ltd.,Xinxiang 453000,Henan,China)
出处
《储能科学与技术》
CAS
CSCD
2020年第4期1206-1213,共8页
Energy Storage Science and Technology
基金
国家自然科学基金青年项目(51805041)
河南省交通运输厅科技计划项目(2019J3)。
关键词
动力电池
荷电状态
高斯过程回归
UKF
power battery
state of charge
Gaussian process regression
UKF
