基于IUPF算法与可变参数电池模型的SOC估计方法

SOC estimation method based on IUPF algorithm and variable parameter battery model

针对常用电池模型参数固定和适用范围有限的问题,建立受温度和SOC影响的可变参数的Thevenin模型,并利用实验设计(DOE)方法和最小二乘法对模型参数进行辨识.针对系统噪声较大时影响算法估计精度的问题,提出了一种改进的无迹卡尔曼粒子滤波(IUPF)算法.将系统状态噪声和量测噪声两者同时引入到采样点中,对其进行对称采样处理,同时将其引入到算法计算过程中以保证算法的精度.在可变参数Thevenin模型基础上采用的IUPF算法,在保证模型适用范围的同时减小了噪声对系统估计精度的影响.实验及仿真结果表明,基于IUPF算法与可变参数电池模型的SOC估计方法在解决现有电池模型适用范围有限、保证模型精度的同时,在多个温度下对SOC有较高的估算精度.尤其在系统状态噪声、量测噪声影响较大时,算法估算精度有了明显提高,且对由模型参数所带来的扰动具有良好的鲁棒性.

Aiming at the problem that the parameters of commonly used battery model are fixed and the scope of application is limited,the variable parameter Thevenin model affected by the temperature and the state-of-charge（ SOC） is established. The model parameters are identified by the design of experiment（ DOE） method and the least squares method. To solve the problem that the estimation accuracy of the algorithm is affected when the system noise is larger,an improved unscented Kalman particle filter（ IUPF） algorithm is proposed. The system state noise and the measurement noise are simultaneously introduced into the sample point,the noises are symmetrically sampled and imported into the process of the algorithm calculation to ensure the accuracy of the algorithm. The IUPF algorithm adopted based on variable parameter Thevenin model reduces the impacts of noises on the system estimation accuracy while ensuring the scope of the model. The experimental and simulation results show that the SOC estimation method based on IUPF algorithm and variable parameter battery model can keep a higher estimation accuracy over a large temperature range,while solving the problem that the scope of application is limited as well as keeping the accuracy of the model. Especially when the system state noise and measurement noise impact seriously,the accuracy of the model is improved,and the method has better robustness to the disturbance caused by the model parameters.