摘要
针对电池SOC与SOH估计结果相互影响,单独估计准确度不高的问题,该文提出了一种基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法。通过构建考虑老化和SOC的电池二阶RC等效电路模型,采用带遗忘因子的递推最小二乘法,在不同SOC和SOH的情况下,对电池的参数进行在线辨识,实现电池参数在线辨识与电池SOC和SOH估计的耦合。以锂离子电池自SOC=20%到恒流充电阶段结束所需时间为输入,电池SOH值为输出,训练GPR模型,实现电池SOH估计。将输出的SOH估计值与电池的额定容量相乘,得到电池的实际容量,更新二阶RC状态空间方程,采用扩展卡尔曼滤波算法对电池进行SOC估计,实现电池SOH估计和SOC估计之间的联合。采用牛津大学电池退化数据集和NASA随机使用电池数据集进行算法验证,结果表明,所提联合估计方法能够在电池的生命周期内较准确地跟随锂离子电池SOC和SOH的真实值。
The accuracy of state of charge(SOC)can be significantly affected by battery aging,leading to misguidance in the calibration of state of health(SOH).Existing studies often estimate SOC and SOH separately,neglecting their close relationship and resulting in reduced estimation accuracy.This paper proposes a joint estimation method for SOC and SOH based on the fusion of an equivalent circuit model and a data-driven model.The influence mechanism between battery SOC and SOH is revealed,mitigating their mutual influence and enhancing the accuracy of SOC and SOH estimation.Firstly,by constructing a second-order RC equivalent circuit model of the battery considering aging and SOC,the recursive least square method with a forgetting factor isused to identify battery parameters online under different SOC and SOH conditions.Secondly,the required time from 20%SOC to the end of the constant-current charging stage is extracted.Pearson and Spearman relationships between constant current charge time and SOH of lithium-ion batteries arecalculated.Thirdly,the actual time required from 20%SOC to the end of the constant-current charging phase of lithium-ion batteries is taken as input and battery SOH as output to train the GPR model offline.The trained GPR model is optimized by hyper parameters and used for SOH prediction.Finally,the estimated SOH output ismultiplied by the rated capacity of the cell to obtain the actual cell capacity,which is used to update the second-order RC state space equation.Based on the second-order RC equivalent circuit model,the battery SOC was estimated by the EKF.The Oxford University battery degradation data set and NASA random battery data set are used to verify the joint estimation method.The results show that the proposed method achieves low average MAE and RMSE for SOC estimation(typicallyless than 0.04).In aging experiments of Cell 1~Cell 8 and RW 3~RW 6 under different working conditions,the average MAE and average RMSE are stable.The actual initial SOC value is 1,and the initial value is set to 0.7 in this paper.With the decline in battery capacity,the joint estimate of battery SOC can follow the actual SOC more accurately.The joint estimation algorithm is robust and accurate.Meanwhile,the reservation-one method is used to verify the Gaussian process regression model.The MAE and RMSE predicted by SOH for Cell 1~Cell 8 are less than 0.5%,and the MAE and RMSE predicted by SOH for RW 3~RW 6 are about 0.05.All the predicted SOH values are in a narrow confidence interval.The following conclusions can be drawn from the simulation analysis:(1)Compared with the existing battery model,the dynamic second-order RC equivalent circuit model considering battery aging and SOC is constructed.In the case of battery aging,the voltage obtained by fitting the identified circuit parameters can track the actual voltage well.(2)The joint estimation method applies the real-time online modified battery parameters and battery SOH to ensure that the battery SOC is adjusted with battery aging.The SOC estimation is accurate.(3)The combined method applies the estimated SOC to ensure effective health feature extraction and improve the accuracy of SOH prediction.
作者
刘萍
李泽文
蔡雨思
王文
夏向阳
Liu Ping;Li Zewen;Cai Yusi;Wang Wen;Xia Xiangyang(School of Electrical and Information Engineering Changsha University of Science&Technology,Changsha 410114 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2024年第10期3232-3243,共12页
Transactions of China Electrotechnical Society
基金
湖南省科技创新人才计划科技创新团队资助项目(2021RC4061)。
关键词
锂离子电池
荷电状态
健康状态
高斯过程回归
带遗忘因子的递推最小二乘法
Lithium-ion battery
state of charge
state of health
Gaussian process regression
forgetting factor recursive least squares
