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Research on SOC estimation algorithm of power battery based on UKF

by:dcfpower     2021-04-02
u003cpu003e The online estimation of power battery state of charge (SOC) is of great significance to the battery management system of hybrid electric vehicles. Aiming at the accuracy loss of the extended Kalman filter (EKF), which is widely used in the estimation algorithm of power battery SOC, in the application of nonlinear systems, the Unscented Kalman Filter (UKF) is used to improve the estimation accuracy. An improved electromotive force (EMF) battery equivalent model is studied, the parameters of the model and the space state equation are discussed, and the UKF is applied to the model to estimate SOC. It can be seen from the experimental analysis that compared with the real SOC value obtained by the open circuit voltage method, the UKF combined with the EMF battery equivalent model has higher accuracy in the estimation algorithm, and the estimation error is less than 5%, and the SOC estimation result is significantly better than EKF. Has high practical value. u003c/pu003eu003cpu003eu003cpu003e The power battery equivalent model is of great significance to SOC estimation. In terms of modeling, the EMF model used here takes into account the influence of factors such as temperature and polarization on the SOC estimation. When the temperature changes greatly, the voltage is appropriately compensated: in the algorithm. In UKF, the proportional correction method is added to the symmetrical sampling. The problem of local effects is avoided; UKF is easier to implement than EKF in the estimation of battery SOC, and can achieve higher state estimation accuracy. It is foreseeable that, based on a suitable battery equivalent model, UKF also has a broad application space for SOC estimation of other types of batteries. Therefore, it is necessary to further realize the engineering of SOC estimation method based on UKF. u003c/pu003eu003c/pu003eu003cpu003e In the SOC estimation under the random current discharge state in the figure below, the UKF method once again reflects its strong error suppression ability. Compare the two methods to get the SOC curve. The peak SOC estimation error produced by the EKF method is close to 7%. Relatively speaking, the peak error generated by the UKF method is less than 5%. In addition, in the entire estimation process, the error generated by the EKF method has appeared several times more severe Fluctuations, while the errors produced by UKF are relatively stable. From this we can see. Contrast with OCV. The SOC true value curve obtained by the SOC curve, in the two discharge states, the application of UKF to estimate the power battery SOC has better accuracy and stability than EKF. u003c/pu003eu003cpu003eu003c/pu003eu003cpu003e SOC estimation comparison under random discharge state (a) SOC estimation result comparison (b) SOC estimation result error comparisonu003c/pu003eu003cpu003eu003cpu003eu003c/pu003e u003c/pu003eu003cpu003e Electric vehicles are the representative of new energy vehicles. Has become a new industry. As the power source of electric vehicles, power batteries are becoming more and more widely used in practical applications. However, for many vehicle battery management systems. The technical defects of the battery will make it difficult to accurately estimate the battery SOC. UKF has a good filtering effect on nonlinear systems. Here, the UKF algorithm and the improved EMF equivalent model are used to estimate the SOC of the power battery. And compared with the EKF method. The experiment proved. The combination of UKF and EMF equivalent models effectively improves the accuracy and reliability of SOC estimation. u003c/pu003eu003c/pu003e
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