The battery charged state estimation algorithm based on UKF research techniques is introduced

Source: 2020 - 04 - 14 20:01 hits: once the battery charged state estimation algorithm based on UKF research in a hybrid electric vehicle battery management system, online SOC estimation is very important. Based on the nonlinear is widely used in power system SOC estimation algorithm in the system of extended kalman filtering ( EKF) Without accuracy loss problem, use the trackless kalman filtering ( UKF) To improve the estimation precision. Is studied in this paper an improved the electromotive force of battery equivalent model, the parameters of the model and state space equations is discussed, and the UKF is applied to estimate the battery charged state. Experimental analysis shows that compared with the open circuit voltage method to get the real value of SOC, UKF and EMF battery equivalent model combining the estimation algorithm has high accuracy. Estimation error is less than 5%, SOC estimation are obviously better than the EKF, has high practical value. Power unit equivalent model is an important part in SOC estimation method. In terms of modeling, this paper adopts the EMF model considered the factors such as temperature, polarization effects on SOC estimation. When the temperature change is bigger, the appropriate compensation for voltage: on the algorithm. In proportion to the symmetric sampling of UKF to join correction methods. Avoid the local effect; In the process of the battery SOC estimation, UKF than EKF are easier to implement, can gain higher precision of state estimation. Predictably, the battery equivalent model based on suitable, UKF in other types of battery SOC estimation also has a broad application space. Therefore, it is necessary to further realize the SOC estimation method based on UKF engineering. In random discharge current state as shown in the figure below SOC estimation, the UKF method again showed strong ability of error suppression. By comparing the two methods, can get SOC curve. SOC estimation error by the EKF method of peak close to 7%, and the error generated by the peak UKF method is less than 5%, in contrast, in addition, in the process of the whole assessment, EKF method error produced by several violent fluctuation, and the error UKF is relatively stable. So we know. And get the real value of the SOC and ocv curve are compared. In the condition of two kinds of discharge, power battery SOC UKF estimated than EKF has better accuracy and stability. The comparison of SOC estimation random discharge conditions ( a) The comparison of SOC estimation results ( b) SOC estimation results error of the electric car, as the representative of new energy vehicles. Has become a new industry. Power battery as the power source of the electric car, get more and more extensive application in practice. However, for many car battery management system. The defect of battery technology is difficult to accurately estimate the SOC. UKF for nonlinear system has good filtering effect. Here, we use the UKF algorithm and improved the EMF equivalent model to estimate the battery charged condition. And compared with the EKF method. Experimental proof. UKF and EMF equivalent model of the combination of SOC estimation is improved effectively the accuracy and reliability.