On-line Lithium-Ion Battery State-of-Power Prediction By Twice Recursive Method Based On Dynamic Model
To be published on April 2, 2019 by SAE International in United States
State-of-Power (SoP) prediction of Li-ion battery is necessary in battery management system for electric vehicles in order to deal with limited conditions, prevent overcharge and over discharge situations, increase the life of battery and provide effective battery operation. This article suggests a method to on-line predict the 10-s discharge and 10-s charge peak power of Li-ion battery cell by twice recursions. First based on dynamic battery model we use the first recursion based on least square method to get parameters which are influenced by the state of charge of Li-ion battery, temperature, etc. The dynamic model is derived according to equivalent circuit model of battery. Current and voltage are inputted online into the battery model. By recursion based on least square method the parameters are updated real time. Moreover, when we use recursive method to get real-time parameters, we add a extra proper parameter to abandon old data, which increase the real-time capability of state-of-power prediction. Next we derive the formula of 10-s resistance by the second recursion. By assuming constant current input and using the dynamic model we get the present dynamic voltage. Then by the second recursion we get the 10-s dynamic voltage and derive the formula of 10-s resistance. The variables of the formula is the parameters which we get directly from the first recursion. Without using the parameters to calculate direct current resistance, polarization resistance or capacitance of battery there is no need using circuit model and resistance or capacitance values to predict 10s-resistance. It reduces much calculation amount and improve the calculation speed. This method is validated with data from NEDC tests of Li-ion battery cell. The 10-s resistance values are predicted accurately. The method is suitable for application in battery management system of electric vehicles.