基于DMM-EKF的超级电容器剩余使用寿命预测

Remaining Useful Life Prediction of Supercapacitors Based on DMM-EKF

超级电容器是一种新型的电化学储能器件,具有循环寿命长、功率密度高、工作温度广等优势,已在轨道交通、工业节能和电网调频等领域广泛应用。准确评估超级电容器的健康状态及剩余使用寿命,对优化储能管理系统,消除安全隐患至关重要。为此,提出一种基于衰退模式匹配(Decay mode matching,DMM)和扩展卡尔曼滤波(Extended Kalman filter,EKF)的预测方法,实现超级电容器剩余使用寿命的准确预测。在历史衰退数据库的基础上,采用豪斯多夫距离来选取与被预测超级电容器衰退模式最匹配的历史衰退过程,即相似衰退过程。基于双指数经验模型建立面向容量衰退的系统模型和量测模型,以相似衰退过程的双指数拟合结果作为初始参数,使用扩展卡尔曼滤波算法进行超级电容器的剩余使用寿命预测。试验结果表明,所提出方法具有较高的预测精度,且鲁棒性好,在两种不同噪声下不同工况单体预测误差均低于1.4%。

Supercapacitor is a new type of electrochemical energy storage device,which has the advantages of long cycle life,high power density and wide working temperature.It has been widely used in rail transit,industrial energy saving and power grid frequency modulation.Accurate evaluation of the state of health and remaining useful life of supercapacitors is very important to optimize the energy storage management system and eliminate potential safety hazards.Therefore,a prediction method based on decay mode matching(DMM)and extended Kalman filter(EKF)is proposed to accurately predict the remaining useful life of supercapacitors.Based on the historical decay database,the Hausdorff distance is used to select the historical decay process that best matches the predicted supercapacitor decay mode,that is,the similar decay process.Based on the dual exponential empirical model,the system model and measurement model for capacitance decay are established.Taking the dual exponential fitting results of similar decay process as the initial parameters,the extended Kalman filter algorithm is used to predict the remaining useful life of supercapacitors.The experimental results show that the proposed method has high prediction accuracy and good robustness,and the cell prediction error is less than 1.4%under two different noises and different working conditions.