Abstract
The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.
Highlights
In recent years, with the escalating energy crisis and environmental problems, low-pollution, high-efficiency electric vehicles (EVs) have become a hot spot in the automotive industry
Conditions, value voltage value and the value estimated by the algorithm and the algorithm were of the battery terminal voltage estimated by the adaptive unscented Kalman filter (AUKF) algorithm is closer to the true value than the subjected to error by analysis
The established model was verified under pulsed discharge conditions; the model error is 0.8%, which provides an accurate model for State of charge (SOC) estimation using AUKF algorithm
Summary
With the escalating energy crisis and environmental problems, low-pollution, high-efficiency electric vehicles (EVs) have become a hot spot in the automotive industry. The commonly used SOC estimation methods for lithium batteries include the ampere-hour integration method, the open circuit voltage method, the neural network method, the particle filter algorithm and the Kalman Filter (KF) method. EKF algorithm is used for SOC estimation in battery management systems (BMSs), and has achieved good results in SOC estimation based on equivalent circuit model [22,23,24] This method solves the nonlinear problem, it ignores high-order terms and increases linear errors, which may cause the filter to diverge. Reference [25] used the unscented Kalman filter (UKF) to perform an unscented transformation on a nonlinear system without ignoring higher-order terms, which improved the accuracy of the estimation.
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