The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field of electric vehicles. In this paper, a fuzzy energy management strategy on the state-of-charge (SOC) estimation of power battery is proposed. Particle filter (PF) algorithm is used to estimate SOC of power battery, then estimated result is regarded as the input variable of fuzzy energy management controller, and the energy distribution result is obtained after fuzzy logic operation. The simulation results show that the SOC estimation result of the PF algorithm is closer to the actual value of power battery SOC. When the SOC estimation result of PF is embedded into the fuzzy controller for joint simulation, it is found that the charge and discharge current, and SOC consumption of the power battery are reduced, which shows the algorithm’s effectiveness. It also provides a specific reference value for the further study of the power supply control strategy of hybrid electric vehicles.

In this paper, the corona discharge process of the bar-plate gap at −1 kV DC voltage is simulated using a two-dimensional axisymmetric plasma module. We analyze the variation of air negative corona discharge current, and the distribution morphology of microparticles in different discharge stages in detail. The significance of plasma chemical reactions at some typical time and the distribution characteristics of heavy particles are investigated according to reaction rates. Results show that, in the current rising stage, the collision ionization reactions (e.g., R1 and R2) and electron adsorption reaction (e.g., R3) play a major role, which lead to the increase in charged particles and the formation of an electron avalanche. In the current drop stage, all reaction rates decreased, except for collision ionization and electron attachment, partial charge transfer reactions (e.g., R8, R10, R11, and R14), and composite reactions (e.g., R16, R17, and R18), which come into play and gradually reduce the number of charged ions in the gap. In the current stabilizing stage, the main chemical reactions are composite reactions (e.g., R16 and R17), then the corona discharge ends. For the heavy particle distribution, O2+ and O4+ are the main positive ions, O2− is the most abundant negative ions, and the neutral particles are mainly O.

In order to reduce the impact of the environment on the accuracy and sensitivity of detection, and to meet the requirements of concealment from detection and being lightweight, a technology for detecting flying metal objects based on photoelectric composite sensors is proposed. The method first analyzes the target’s characteristics and detection environment, and then compares and analyzes the methods for detecting typical flying metal objects. On the basis of the traditional eddy current model, the photoelectric composite detection model that meets the requirements of detecting flying metal objects was studied and designed. For the problems of the short detection distance and the long response time of the traditional eddy current model, the performance of the eddy current sensor was improved to meet the requirements of detection through optimizing the detection circuit and coil parameter model. Meanwhile, to meet the goal of being lightweight, an infrared detection array model applicable to flying metal bodies was designed, and simulation experiments of composite detection based on the model were conducted. The results show that the flying metal body detection model based on photoelectric composite sensors met the requirements of distance and response time for detecting flying metal bodies and may provide an avenue for exploring the composite detection of flying metal bodies.