Abstract
Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance Qk and observes the noise covariance Rk , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance Q?,k and observes the noise covariance R?,k. Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use.
Highlights
In recent years, with the increase of the number of retired batteries, the cascade utilization of power batteries has attracted increasingly more attention
The echelon-use battery refers to the lithium iron phosphate powered lithium battery used in EV when the capacity attenuates to less than 80%, and more than 20%, which is used for the power backup and energy storage of the communication base station
The objective of this study is to propose an effective state of power (SOP) estimation method for echelon-use batteries and analyze the influence of the degradation of the performance of such batteries and the state of charge (SOC) estimation on the SOP estimation
Summary
With the increase of the number of retired batteries, the cascade utilization of power batteries has attracted increasingly more attention. The use of the estimation method based on the equivalent circuit model has increased. That is, based on the ADEKF algorithm used to estimate the SOC, ohmic internal resistance, and actual capacity, the optimal estimation is used to predict the working voltage and ohmic internal resistance to estimate the echelon-use battery SOP. The objective of this study is to propose an effective SOP estimation method for echelon-use batteries and analyze the influence of the degradation of the performance of such batteries and the SOC estimation on the SOP estimation. (3) To improve the accuracy of SOP estimation, an adaptive Kalman filter algorithm is adopted to estimate the parameters of echelon-use batteries in real time based on process noise covariance Qθ,k and observing the noise covariance Rθ,k , aiming at the performance attenuation of the echelon-use battery and the inaccuracy of actual capacity and ohmic internal resistance. (4) The impact of echelon-use battery SOC and working voltage on the SOP estimation is presented
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