Abstract
Temperature has an important effect on the battery model. A dual-polarization equivalent circuit model considering temperature is established to quantify the effect of temperature, and the initial parameters of the model are identified through experiments. To solve the defect of preset noise, the H-infinity filter algorithm is used to replace the traditional extended Kalman filter algorithm, without assuming that the process noise and measurement noise obey Gaussian distribution. To eliminate the influence of battery aging on SOC estimation, and considering the different time-varying characteristics of the battery states and parameters, the dual time scale double H-infinity filter is used to jointly estimate the revised SOC and available capacity. The simulation results at two temperatures show that, compared with the single time scale, the double time scale double H-infinity filter reduces the simulation time by nearly 90% under the premise that the accuracy is almost unchanged, which proves that the proposed joint estimation algorithm has the dual advantages of high precision and high efficiency.
Highlights
An accurate state of charge (SOC) and available capacity estimation is an important function of a battery management system (BMS)
The fractional-order model established by Hu et al [5] has high accuracy, and the SOC estimation error obtained under different working conditions can be kept within 0.02
The results show that the model error, SOC estimation error, and available capacity estimation error are in acceptable range whether single time scale or dual time scale
Summary
An accurate state of charge (SOC) and available capacity estimation is an important function of a battery management system (BMS). In terms of SOC estimation of the lithium-ion battery, model-based Kalman filter series are the most widely used algorithms at present. Based on the fractional-order model, references [11,12] used a series of Kalman filter algorithms to realize the accurate estimation of SOC.
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