Model Identification And SOC Estimation Research Of Nickel-Hydrogen Power Battery

Abstract:This paper put the Ni-MH batteries as experimental object and designed a battery constant exile electric circuit based on MCU C8051f020. For the study of power battery discharge characteristics,10sections Ni MH battery that rated voltage is1.2V and rated capacity is30Ah were used to make discharge experiments at room temperature, and studied estimation methods of State of Charge(SOC) that reflect the performance and working State of power battery on this basis. View of the Kalman filtering algorithm rely too much on the model in estimating SOC,this study used the subspace identification algorithm to determine the order number, in order to choose more appropriate battery model. According to the result of identification, the selected subjects is one order model, based on this, selected the model of the empirical formula. And least squares method combined with open circuit voltage method is used to identify the unknown parameters of the model, the equivalent model of the maximum error is0.12V. In estimating SOC, To the square root UKF algorithm assume the noise covariance is a constant that can produce error when use it to estimate SOC. According to the principle of feedback, this article made improvements, put model output residuals of each moment as the new rate to estimate the noise covariance of the moment corresponding, make it new with time, adaptability, which reduces the estimation error. And the improved algorithm were simulated with a model that is a general and nonlinear model. The result showed that the improvement is effective. Last the improved algorithm was applied to the battery SOC estimation. The error is less than1.5%compared with the standard values that geted with AH method. So get the adaptive square root UKF that is a new, higher estimation precision algorithm.