Elektromos hajtásláncok fékpadi méréseinek vizsgálata: Analysis of electric drivetrain testbench measurements

This article describes how to evaluate measurement data collected from an electric motor test bench, using a MATLAB function, which is specially designed for this purpose. We can examine many parameters of an electric motor when we are making test bench measurements, for us the primary goal is to determine the complete efficiency map of the motors. The most important aspect of creating the function was the faster and more efficient data processing, while the function is also suitable for automatic comparison of individual measurement results. After evaluating the measurement data, it is easy to determine which electric motor is more suitable for the given load cycle. It is due to the evaluation function, we selected the drive motor and determined the optimal gear ratio for the energy efficient vehicle of the SZEnergy Team. Kivonat A cikk elektromos motor próbapadon gyűjtött mérési adatok kiértékelését mutatja be, egy erre a célra készített MATLAB függvény felhasználásával. Egy elektromos motor vizsgálatakor számos paramétert vizsgálhatunk, számunkra az elsődleges cél a motorok teljes hatásfokmezőjének meghatározása. A függvény megalkotása során a mérési adatok gyorsabb és hatékonyabb feldolgozása volt a legfőbb szempont, továbbá alkalmas az egyes mérési eredmények automatikus összehasonlítására is. A mérési eredmények kiértékelése után könnyen meghatározhatjuk, hogy adott terhelési ciklusra mely elektromos motor alkalmasabb. A kiértékelő függvény segítségével választottunk hajtó motort és határoztuk meg az optimális áttételt többek közt a SZEnergy Team energiahatékony járművéhez is. &nbsp

A közlekedés energiahatékonyságának és környezetterhelésének kérdései az elektromos járműhajtás és az autonóm közlekedési rendszerek tükrében: Energy efficiency and ecological footprint of transport in the context of electric vehicle drive and autonomous transport systems

The current developments in automotive and traffic management systems raise the question of how we can reduce the ecological footprint of today's and future mobility needs. The study examines the technical background of developments, including development trends and their known and putative limitations. It contrasts the technical approach with economic investments and the ecological footprint of products over their full life cycle, analyzed in a complex way.  Kivonat A jelenlegi járműipari és a közlekedésirányítási rendszerek fejlesztése kapcsán felmerül a kérdés, hogy a környezetterhelést (illetve ökológiai lábnyomot) hogyan tudjuk csökkenteni a mai és jövőbeni mobilitási igényeket figyelembe véve. A tanulmány megvizsgálja a fejlesztések műszaki hátterét, beleértve a fejlesztési tendenciákat, valamint az ismert és vélt korlátokat. A műszaki szemléletet szembeállítja a gazdasági befektetéssel és a komplexen vizsgált, teljes termék életciklusra vetített ökológiai lábnyommal

Data-driven linear parameter-varying modelling of the steering dynamics of anautonomous car

Developing automatic driving solutions and driver support systems requires accurate vehicle specific models to describe and predict the associated motion dynamics of the vehicle. Despite of the mature understanding of ideal vehicle dynamics, which are inherently nonlinear, modern cars are equipped with a wide array of digital and mechatronic components that are difficult to model. Furthermore, due to manufacturing, each car has its personal motion characteristics which change over time. Hence, it is important to develop data-driven modelling methods that are capable to capture from data all relevant aspects of vehicle dynamics in a model that is directly utilisable for control. In this paper, we show how Linear Parameter-Varying (LPV) modelling and system identification can be applied to reliably capture personalised model of the steering system of an autonomous car based on measured data. Compared to other nonlinear identification techniques, the obtained LPV model is directly utilisable for powerful controller synthesis methods of the LPV framework

Identification of the nonlinear steering dynamics of an autonomous vehicle

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results

Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory

Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory

Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy

In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage