Development of Urban Electric Bus Drivetrain

The development of the drivetrain for a new series of urban electric buses is presented in the paper. The traction and design properties of several drive variants are compared. The efficiency of the drive was tested using simulation calculations of the vehicle rides based on data from real bus lines in Prague. The results of the design work and simulation calculations are presented in the paper

On extended Kalman filters with augmented state vectors for the stator flux estimation in SPMSMs

The demand for highly dynamic electrical drives, characterized by high quality torque control, in a wide variety of applications has grown tremendously during the past decades. Direct torque control (DTC) for permanent magnet synchronous motors (PMSM) can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. In order to overcome problems associated with the integration of the back-emf, the use of state observers has been advocated in the literature. Several types of state observers have been conceived and implemented for PMSMs, especially the Extended Kalman Filter (EKF) has received much attention. In most reported applications however the EKF is only used to estimate the speed and rotor position of the PMSM in order to realize field oriented current control in a rotor reference frame. Far fewer publications mention the use of an EKF to estimate the stator flux linkage vector in order to apply DTC. Still the performance of the EKF in the estimation of the stator flux linkage vector has not yet been thoroughly investigated. In this paper the performance of the EKF for stator flux linkage is studied and simulated. The possibilities to improve the estimation by augmenting the state vector and the consequences of these alterations are explored. Important practical aspects for FPGA implementation are discussed

A Scalable, FPGA-Based Implementation of the Unscented Kalman Filter

Autonomous aerospace systems may well soon become ubiquitous pending an increase in autonomous capability. Greater autonomous capability means there is a need for high-performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a field-programmable gate array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the unscented Kalman filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to simplify the development process, and a non-application-specific part, implemented in hardware as a parameterisable ‘black box’ module (i.e. IP core) to increase performance. Simulation results demonstrating a possible nanosatellite application of the design are presented; implementation (synthesis, timing, power) details are also presented

High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

Software Defined Radio Implementation of Carrier and Timing Synchronization for Distributed Arrays

The communication range of wireless networks can be greatly improved by using distributed beamforming from a set of independent radio nodes. One of the key challenges in establishing a beamformed communication link from separate radios is achieving carrier frequency and sample timing synchronization. This paper describes an implementation that addresses both carrier frequency and sample timing synchronization simultaneously using RF signaling between designated master and slave nodes. By using a pilot signal transmitted by the master node, each slave estimates and tracks the frequency and timing offset and digitally compensates for them. A real-time implementation of the proposed system was developed in GNU Radio and tested with Ettus USRP N210 software defined radios. The measurements show that the distributed array can reach a residual frequency error of 5 Hz and a residual timing offset of 1/16 the sample duration for 70 percent of the time. This performance enables distributed beamforming for range extension applications.Comment: Submitted to 2019 IEEE Aerospace Conferenc