光伏农业系统中储能电池的荷电状态估计

Estimation of State of Charge of Storage Batteries in Photovoltaic Agricultural Systems

光伏农业是将光储直流微电网作为一种新式能源结构与智慧农业有效结合,不仅能对农业负荷提供可靠供给,而且降低农业生产成本。储能电池作为光储直流微电网中关键组成部分,准确估计储能电池的荷电状态(SOC)对系统运行尤为重要。针对储能电池SOC估计精度难以提高的问题,提出以二阶RC电路作为电池等效模型,采用带有可变遗忘因子的递推最小二乘(VFFRLS)算法完成电池模型参数在线辨识,同时联合对系统噪声协方差实时更新的自适应扩展卡尔曼滤波(AEKF)算法实现电池SOC估计。结果表明,该方法具有较高的精度和鲁棒性,提高了微电网储能系统的运行效率。

Photovoltaic agriculture is an effective combination of photovoltaic storage DC microgrid as a new type of energy structure and smart agriculture,which not only provides a reliable supply to the agricultural loads,but also reduces the cost of agricultural production.As a key component of photovoltaic DC microgrid,accurate estimation of the state of charge(SOC)of the storage battery is particularly important for system operation.Aiming at the difficulty of improving SOC estimation accuracy of energy storage batteries,a second-order RC circuit is proposed as the battery equivalent model,and a recursive least squares(VFFRLS)algorithm with variable forgetting factor is used to complete the online identification of battery model parameters.At the same time,the adaptive extended Kalman filter(AEKF)algorithm which updates the noise covariance in real time is used to estimate the SOC.The results show that this method has high precision and robustness,and improves the operation efficiency of the microgrid energy storage system.