ALBERT

Description: High-quality frequency selective surfaces (FSSs) are important for electromagnetic signal absorption/filtration. Usually, they are made from wave-transparent composite materials covered with a thin metal layer. Current machining methods show some disadvantages when performing fabrication on the structure. Based on its flexibility and uncontactable processing characteristics, nanosecond laser etching of aluminum-plated composite materials applied to FSSs was investigated. To observe the influence of the laser light incident angle, etching of a series of square areas with different incident angles was performed. Thereafter, an image processing method, named the image gray variance (IGV), was employed to perform etching quality evaluation analysis. The observed microscopic pictures of experimental samples were consistent with those of the IGV evaluation. The potential reasons that might affect the etching quality were analyzed. Following all the efforts above, an incident angle range of ±15° was recommended, and the best etching result was obtained at the incident angle of 10°. To observe the influence of the laser pulse overlap and focal spot size on the etched area border uniformity and on the potential damage to the base materials, a theoretical equation was given, and then its prediction of area border edge burrs fluctuation was compared with the experiments. Furthermore, SEM pictures of etched samples were examined. Based on the study, a processing window of the laser pulse overlap and focal spot size was recommended. To conclude, optimal etching results of the FSS materials could be guaranteed by using the right laser operating parameters with the nanosecond laser.

Description: Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.