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Aiming at the trajectory planning of autonomous vehicles in lane changing under muti-lane traffic scenarios, a multi-lane lane change decision and trajectory planning algorithm based on Logic Regression Algorithm and Gaussian Probability Model is proposed. Firstly, the target state (time and velocity) of vehicle during lane change is sampled and lateral trajectory (5th polynomial) and longitudinal trajectory (4th polynomial) are planned based on the target state; Secondly, cost evaluation function of trajectory is established and optimal trajectory is selected based on three aspects of safety, comfort and lane changing time.

Traditional testing approaches for fundamental battery materials focus on highly artificial test profiles, for example constant current (CC) or constant voltage (CV) testing. Additionally, the currents used for capacity and cycle tests are often very low. These profiles are not indicative of the types of current/voltage profiles that the battery will experience during actual vehicle operation. As a result, these simple tests may fail to sufficiently elicit the reduction in performance and failure modes that occur during more dynamic cycling. In this paper, we outline an approach in which vehicle-level modeling is applied to regulatory drive cycles in order to derive power vs. time requirements for an energy storage system. These requirements are used to identify segments of the regulatory drive cycles that present significant challenges to the battery. Finally, the most stressing portions of the drive cycle are used to determine limiting physical characteristics of batteries.

This paper deals with a new kind of driving module of solenoid valves which can be used in electronic diesel injection systems such as unit pumps, unit injectors and common rail injectors. A typical structure of solenoid driving circuitry and the so called Peak&Hold current waveform is introduced first. Then analysis of driving voltage on solenoid responding characteristics, which are the key factors for precise electronic controlling of injector valves, is carried out and summarized based on finite element model of solenoid valves. After that, the schematic drawing of a new kind of driving module is presented. The structure and parameter selection process of the two main parts of this module, the DC/DC converter and PWM(Pulse With Modulating) signal generator, are depicted in detail.

The arcing faults on cables of aerospace power systems represent a major safety concern for spacecraft. To ensure the normal operation of the Power Management and Distribution (PMAD) system, it is necessary that remedial control strategies are developed and implemented. Most of the research in this area deal mainly with arcing faults in terrestrial distribution power systems that operate as AC systems rather than DC systems. The paper investigates the application of a new digital signal processing technique – Fast Fourier Transform (FFT) based energy spectrum estimation – to analyze the recorded signals of DC arcing faults. The data is collected from the DC arcing experiment at the NASA Glenn research center. The voltage level range is from 50Vdc to 150Vdc. This paper presents a feature extraction approach to transform the signal representation from time-domain into frequency-domain. Results demonstrate the distinctive difference between the pre-fault signal and post-fault signal.

In spacecraft energy power system (EPS), the objective of fault protection is to detect and respond to spacecraft faults. Its purpose is to eliminate single point failures or their effects and to ensure the spacecraft system integrity under anomalous conditions. Also, it is important to keep the continuity of the power supply and at the same time increase the reliability of spacecraft Energy power system. One of most deadly faults is arcing faults, which are accompanied by very erratic waveform variations. The sustainable current level in the arc is not sufficient to be reliably detected by conventional means. Feeder current signal analysis provides a solution to this detection problem. A Fast Fourier Transform method is applied to decompose the monitored voltage and current signals into a series of detailed spectral components. The spectral energies are calculated and then employed to train a neural network to identify arcing faults accurately.