锂离子电池单体的功率预测估计

he estimations on the state of charge (SOC) and the state of power (SOP) of lithium-ion battery have formed the core functions of the Battery Management System (BMS) in EVs. The peak power estimation of the battery can be used to evaluate the charging and discharging power limiting capability of the battery pack under different states of charge, so as to ensure that the car can work in the best performance area. Based on the single cell model, we proposed a peak power prediction algorithm. The peak power obtained by this method has the characteristics of high precision and fast computing speed. Firstly, we built the equivalent circuit model of the cell and identified the parameters of the model using the recursive least square (RLS) method. Then, we predicted the peak power of the battery cell, taking into account of the influence of the SOC, terminal voltage, current and power limitation. Finally, the peak power prediction algorithm was validated by the Urban Dynamometer Driving Schedule (UDDS) test and the New European Driving Cycle (NEDC) test.