Professional Manufacturer of One Stop Solutions Provider for all kind of lithium battery 10 years more .

English

Introduction to the research technology of battery state of charge estimation algorithm based on UKF

by:Vglory      2021-05-07
Research on battery state-of-charge estimation algorithm based on UKF In hybrid electric vehicle battery management system, online estimation of SOC is very important. In view of the accuracy loss of the extended Kalman filter (EKF), which is widely used in power system SOC estimation algorithms in nonlinear systems, the trackless Kalman filter (UKF) is used to improve the estimation accuracy. An improved equivalent model of electromotive force battery is studied, the parameters and space state equation of the model are discussed, and the UKF is applied to estimate the state of charge of the battery. Experimental analysis shows that, compared with the real SOC value obtained by the open circuit voltage method, the estimation algorithm combining UKF and EMF battery equivalent model has higher accuracy. The estimation error is less than 5%, and the SOC estimation result is significantly better than EKF, which has high practical value. The power unit equivalent model is an important method for SOC estimation. In terms of modeling, the EMF model adopted in this paper takes into account the influence of temperature, polarization and other factors on the SOC estimation. When the temperature changes greatly, the voltage is appropriately compensated: in the algorithm. The proportional correction method is added to the symmetrical sampling of UKF. The problem of local effects is prevented; in the process of estimating the battery SOC, UKF is easier to implement than EKF, and higher state estimation accuracy can be obtained. It is foreseeable that, based on a suitable battery equivalent model, UKF will also have broad application space in other types of battery SOC estimation. Therefore, it is necessary to further realize the SOC estimation method engineering based on UKF. In the SOC estimation under the random current discharge state as shown in the figure below, the UKF method once again shows a strong error suppression capability. By comparing the two methods, the SOC curve can be obtained. The SOC estimation error is close to 7% from the peak of the EKF method, while the error of the peak UKF method is less than 5%. In contrast: In addition, during the entire evaluation process, the error of the EKF method has several violent fluctuations. Wrong UKF is relatively stable. So we understand. And compared with the true value curve of SOC obtained by ocv. Under the two discharge states, the UKF estimation of the power battery SOC has better accuracy and stability than EKF. Comparison of SOC estimation under random discharge state (a) Comparison of SOC estimation result (b) Comparison of SOC estimation result error Electric vehicles are a representative of new energy vehicles. Has become a new industry. As the power source of electric vehicles, power lithium batteries have been used more and more widely in practice. However, regarding many car battery management systems. The shortcomings of battery technology make it difficult to accurately estimate SOC. UKF has a good filtering effect on nonlinear systems. Here, we use the UKF algorithm and an improved EMF equivalent model to estimate the state of charge of the 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. Disclaimer: Some pictures and content of articles published on this site are from the Internet. If there is any infringement, please contact to delete. Previous: Introduction to the performance and application scope of ternary lithium ion batteries
Custom message
Chat Online
Chat Online
Leave Your Message inputting...
Sign in with: