基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法

Battery energy storage is a powerful target for carbon neutrality. Accurate estimation of its state of energy (SOE) and state of power (SOP) is the key and foundation for the effective and reliable operation of battery energy storage. It is challenging to determine the precise values of SOE and SOP as recessive state quantities due to the intricacy of the electrochemical reaction process in batteries. Therefore, a model-based joint estimation method of SOE and SOP is suggested in this paper. Recursive least squares are utilized to create an online parameter identification technique using the Thevenin equivalent circuit model, and accurate model parameters are achieved. To address the prediction problem under constant power demand, a multi-step power prediction method is proposed to enhance the prediction accuracy of SOP. An additional joint estimation approach of SOE and SOP is suggested in conjunction with the expanded Kalman filter algorithm. The feasibility of the algorithm is verified by experiments. The findings demonstrate that, even in the presence of significant starting errors, the suggested method's maximum voltage and SOE prediction errors are both less than 2%, resulting in precise SOP prediction.