The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.
The lithium-ion batteries of an electric vehicle belong to a high-voltage direct-current system. The high-voltage insulation performance of electric vehicles is very important for their safe operation. To solve the problems of slow response and the poor estimation accuracy of the insulation resistance under complex vehicle working conditions, a real-time insulation resistance detection method based on the variable forgetting factor least squares algorithm is proposed in this paper. Based on the low-frequency signal injection method and considering the influence of the Y capacitor, the corresponding circuit model and the mathematical model of the reflected wave voltage are established, and the mathematical model is linearized by a first-order Taylor expansion. By analyzing the influence of the forgetting factor on model parameter identification and setting appropriate shutdown criteria, the least squares algorithm with a variable forgetting factor is designed to quickly and accurately estimate the insulation resistance and Y capacitance. The experimental test results show that the proposed method can quickly track the changes in the insulation resistance and Y capacitance under the condition of noise interference and that the root mean square error of the estimation resistor is within 0.012.
State of charge (SOC) is a key parameter for lithium-ion battery management systems. The square root cubature Kalman filter (SRCKF) algorithm has been developed to estimate the SOC of batteries. SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables. After these points are propagated by nonlinear functions, the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions. SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition, guarantees the nonnegative quality of the covariance matrix, and avoids the divergence of the filter. Simulink models and the test bench of extended Kalman filter (EKF), Unscented Kalman filter (UKF), cubature Kalman filter (CKF) and SRCKF are built. Three experiments have been carried out to evaluate the performances of the proposed methods. The results of the comparison of accuracy, robustness, and convergence rate with EKF, UKF, CKF and SRCKF are presented. Compared with the traditional EKF, UKF and CKF algorithms, the SRCKF algorithm is found to yield better SOC estimation accuracy, higher robustness and better convergence rate.
Photovoltaic (PV) power generation forecasting models require a large amount of meteorological data, which may include irrelevant and redundant information. As the volume of data increases, the dataset is likely to contain a significant amount of irrelevant and redundant information. This paper proposes a method for reducing dimensionality based on PCC-GRA-PCA method, which aims to simplify the model and reduce computational complexity. Firstly, the dimension reduction method analyzes the feature importance of various meteorological elements by using Pearson Correlation Coefficient (PCC) and Grey Relation Analysis (GRA), which can achieve the preliminary dimension reduction of data by selecting the most relevant features. Next, the data is processed using Principal Component Analysis (PCA) to achieve a secondary dimension reduction of meteorological data through feature transformation. Finally, a photovoltaic power prediction model has been established using the OVMD-tSSA-LSSVM algorithm. After analysis, it was found that the prediction model showed improvements in R2, MAE, RMSE, and MAPE after PCC-GRA-PCA dimensionality reduction compared to the prediction model before dimensionality reduction, as well as the prediction model after LDA and PCA dimensionality reduction. This demonstrates the effectiveness of reducing data dimensionality.
Abstract The objective of this study is to investigate the impact of accumulated pollutants on the surface of a 1000 kV AC transmission line in a specific location in China on its audible noise generation. The line with diatomaceous earth, kaolin, and other pollutants attached to it is modeled by using finite element software, and the study results indicate that the conductivity of the material plays a significant role in influencing the audible noise. In addition, diatomaceous earth has the least effect on the amplitude and lateral attenuation of audible noise, and sodium chloride and carbon powder have the greatest effect. The research in this paper can be used for the reason that dirt particles affect the magnitude of audible noise and these findings offer valuable insights for the management of audible noise.
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