Alexander Kuznietsov

A wireless framework for movement activity monitoring of sprinters

The most important factor for success in short distance track and field running is an optimal combination of the step frequency and step length. Evaluation and analysis of step parameters allows enhancing the overall performance and avoiding injuries. This paper presents a low-cost, lightweight wireless solution. The developed system derives information about step parameters from accelerometer and gyroscope measurement results and fulfills a real-time analysis of sprinters activity. The described instrument supports coaches and help to optimize the training process.

Model predictive control of a voltage source inverter with compensation of deadtime effects

Dead time elimination of voltage source inverters (VSI) becomes critical for modern current controlled applications with increased requirements regarding the magnitude accuracy and harmonic distortion of an output waveform. Recently many approaches are used to compensate the dead time both in open or close loop control systems. All these schemes require additional circuits or computational effort. This paper presents a model predictive control method with embedded dead time elimination of VSIs. The classical elimination open loop scheme is combined with model predictive control (MPC) realized in state space. The results of simulation carried out with MATLAB/SIMULINK and confirming the theoretical prepositions show the effectiveness of the method for current control of VSI with passive loads.

Motor drive control of a full-electric vehicle using generalized predictive control algorithm

In this paper, the generalized predictive control (GPC) algorithm is introduced to control a permanent magnet synchronous motor (PMSM) drive of a full-electric vehicle (EV). For this aim, the CARIMA model of PMSM is derived and the theoretical basics of GPC are described. Following, the GPC algorithm is extended by consideration of the voltage constraints, given by the vehicle battery. In conclusion, the simulation results of an electric vehicle, driven by GPC controlled PMSM, are compared to the field oriented control based EV drive.

Multivariable generalized predictive control of a synchronous motor drive used in an electric vehicle

In this paper, the multivariable generalized predictive control (GPC) algorithm is introduced to control a permanent magnet synchronous motor (PMSM) drive of a full-electric vehicle (EV). For this aim, the CARIMA model of PMSM is derived and the theoretical basics of multivariable GPC are described. Following, the multivariable GPC algorithm is extended by consideration of voltage constraints, given by the vehicle battery. In conclusion, the simulation results of an electric vehicle, driven by multivariable GPC controlled PMSM, are compared to the results of the conventional field oriented control (using PI controllers) based EV drive.

A scaled autonomous vehicle platform to develop and investigate driver assistance and control algorithm

This paper describes an intelligent 1:10 scaled vehicle. The car possesses the capability to drive autonomously performing several typical maneuvers like parallel parking, lane keeping, obstacle detection and avoidance. The developed platform can be utilized for developing and testing of new vehicle control algorithms as well as for teaching purposes.

Model based online detection of inter-turn short circuit faults in PMSM drives under non-stationary conditions

In this paper, a model based fault detecting algorithm for detection of inter-turn short circuits in permanent magnet synchronous motors (PMSMs) is presented. Based on the PMSM model, the algorithm compares predicted values of the current space vector with the measurements to calculate an error vector, which will be used to detect the fault. A significant advantage of the developed algorithm is the ability to detect an inter-turn short circuit also during transients, i. e. at variable speed and while changes of dq-axis current space vector. Furthermore, the algorithm does not use any frequency transformation, which results in low computational cost and avoids high storage requirement.

On-board diagnostics of Li-Ion battery packs for electric vehicles using a combination of spectroscopy and identification methods

An accurate parameter estimation becomes one of most important goals of modern battery management systems in electrical vehicles and smart grids. Actually, only state of the charge is used to analyse the battery conditions. However, information about internal resistance and capacity as well as open circuit voltage allows optimizing the battery operation mode either while charging or discharging and therefore increase the lifetime of the battery. Hence the battery parameters like capacity or the internal resistance are not constant and are changing due to environmental influences like temperature or cell ageing, the need for online identification methods becomes more and more important. The key idea of the proposed paper lies in the application of impedance spectroscopy methods without injection of an additional current and using the transient signals during operation of an electric vehicle. The accuracy of estimation with three typical dynamic load profiles is analysed. It is shown that the open circuit voltage impacts the results of an impedance spectroscopy in ultra-low frequency range. To compensate this impact and to enhance the estimation accuracy the classical impedance spectroscopy is combined with an open circuit voltage estimation algorithm in time domain. The simulation results as well as experiments with a real battery pack show the correctness of a proposed approach.

Position control of a permanent magnet synchronous motor using generalized predictive control algorithm

In this paper, the generalized predictive control (GPC) algorithm is introduced to control the position of a permanent magnet synchronous motor (PMSM). For this aim, the CARIMA model of PMSM is derived and the theoretical basics of multivariable GPC are described. In order to reduce computational burden, the GPC algorithm is extended by approach of using Laguerre functions. In conclusion, the HiL simulation results of PMSM position control, given by GPC algorithm are compared to the results of the conventional field oriented control (using PI controllers).

Development of an electric vehicle as a framework for interdisciplinary problem based learning curriculums

This paper presents a research project carried out at the University of applied Sciences, Mittelhessen. The main goal of the project was a development of an own electric vehicle made by students teams under supervision of university staff as well as a design of a learning platform for supporting existing courses in form of problem based learning (PBL) experiments.

Distributed data acquisition and control system for an scaled autonomous vehicle

In this paper we present an autonomous car with distributed data processing. The car is controlled by a plurality of independent sensors. For the lane detection, a camera is used, which detects the lane marks with a Hough transformation. Once the camera detects these, one of them is calculated to be followed by the car. This lane is processed in connection with the information of the other sensors of the car. These sensors check the route for obstructions or allow the car to scan a parking space and to park on the roadside if the gap is large enough. The car is built in 1:10 scale, and shows excellent results on a test track.