Evaluation of Motor Characteristics for Hybrid Electric Vehicles Using the Hardware-in-the-Loop Concept

Oh, Sung Chul

doi:10.1109/tvt.2005.847228

Cited by 106 publications

(31 citation statements)

References 13 publications

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“…During calibration, the peak noise on individual sampled RTS/RTDS power angle measurements was up to ±0. 6 • despite the careful use of screened twisted-pair cables and differential inputs throughout the instrumentation/measurement circuits. The noise on the power angle measurement occurs partly because the current I N can be measured up to 125 A; therefore, a 29 A flow represents only 23% of the full-scale range.…”

Section: Calibration and Loop Latencymentioning

confidence: 99%

Architecture of a Network-in-the-Loop Environment for Characterizing AC Power-System Behavior

Roscoe

Mackay

Burt

et al. 2010

IEEE Trans. Ind. Electron.

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Abstract-This paper describes the method by which a large hardware-in-the-loop environment has been realized for threephase ac power systems. The environment allows an entire laboratory power-network topology (generators, loads, controls, protection devices, and switches) to be placed in the loop of a large power-network simulation. The system is realized by using a realtime power-network simulator, which interacts with the hardware via the indirect control of a large synchronous generator and by measuring currents flowing from its terminals. These measured currents are injected into the simulation via current sources to close the loop. This paper describes the system architecture and, most importantly, the calibration methodologies which have been developed to overcome measurement and loop latencies. • and 1%) are defined mainly by the controllability of the synchronous generator.

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Section: Calibration and Loop Latencymentioning

confidence: 99%

Architecture of a Network-in-the-Loop Environment for Characterizing AC Power-System Behavior

Roscoe

Mackay

Burt

et al. 2010

IEEE Trans. Ind. Electron.

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“…The growth of environmental awareness is today among the most constringent drivers that the researchers and the manufacturers have to consider when designing environmental friendly solutions for drive trains, including electric, hybrid and fuel cell Vehicles. Nevertheless, most of the development and the evaluation of the new solutions still follow classical scheme that includes modeling and testing components and drivetrain under standard driving cycles or in the best case, using preregistered real world driving cycle [1], [2], [3], [4], [5] and [6]. This methodology of design does not include the variability of the driving conditions generated by the two major factors that are the driver behavior and the infrastructure influence (elevation, turns, speed limitation, traffic jam, traffic lights, .…”

Section: Introductionmentioning

confidence: 99%

H&HIL: A Novel Tool to Test Control Strategy with Human and Hardware In the Loop

Jeanneret¹,

Ndiaye²,

Gillet³

et al. 2017

2017 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Abstract-With this work, the authors try to make HIL simulation more realistic with the introduction of the Human driver in the loop. To succeed in this objective we develop a set of tools to connect easily a wide variety of real or virtual devices together : of course the driver but also racing gamer joystick, real engine, virtual drivetrain, virtual driver environment ...The detailed approach represent a step forward before testing control strategy or new powertrain on a real vehicle. First results showed a good effectiveness and modularity of the tool.

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“…In this way, major systems components (such as engines in automotive applications) can be evaluated and control systems designed without the expense and complexities of a whole system empirical development programme [30]. However, the utilization of real hardware introduces the significant issue of sensor and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

Gladwin

Stewart

2011

Engineering Applications of Artificial Intelligence

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Abstract-In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-theloop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

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Evaluation of Motor Characteristics for Hybrid Electric Vehicles Using the Hardware-in-the-Loop Concept

Cited by 106 publications

References 13 publications

Architecture of a Network-in-the-Loop Environment for Characterizing AC Power-System Behavior

Architecture of a Network-in-the-Loop Environment for Characterizing AC Power-System Behavior

H&HIL: A Novel Tool to Test Control Strategy with Human and Hardware In the Loop

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

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