
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
A Novel Energy Management Strategy in Electric Vehicle Based on H∞ Self-Gain Scheduled for Linear Parameter Varying Systems

A Novel Energy Management Strategy in Electric Vehicle Based on H∞ Self-Gain Scheduled for Linear Parameter Varying Systems
The present paper exhibits a real time assessment of a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, is based on a self-gain scheduled controller, which guarantees the H∞ performance for a class of linear parameter varying (LPV) systems. Assuming that the duty cycle of the involved DC-DC converters are considered as the variable parameters, that can be captured in real time, and forwarded to the controller to ensure both; the performance and robustness of the closed-loop system. The subsequent controller is therefore time-varying and it is automatically scheduled according to each parameter variation. This algorithm has been validated through experimental results provided by a tailor-made test bench including both the HESS and the vehicle traction emulation system. The experimental results demonstrate the overall stability of the system, where the proposed LPV supervisor successfully accomplishes a power frequency splitting in an adequate way, respecting the dynamic of the sources. The proposed solution provides significant performances for different speed levels.
[PHYS.MECA.THER] Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], [SPI.NRJ]Engineering Sciences [physics]/Electric power, 621, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 620, 629, [SPI.AUTO] Engineering Sciences [physics]/Automatic, [PHYS.MECA.THER]Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], [PHYS.MECA.MEFL] Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [SPI.NRJ] Engineering Sciences [physics]/Electric power
[PHYS.MECA.THER] Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], [SPI.NRJ]Engineering Sciences [physics]/Electric power, 621, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 620, 629, [SPI.AUTO] Engineering Sciences [physics]/Automatic, [PHYS.MECA.THER]Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], [PHYS.MECA.MEFL] Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [SPI.NRJ] Engineering Sciences [physics]/Electric power
8 Research products, page 1 of 1
- 2021IsAmongTopNSimilarDocuments
- 2021IsAmongTopNSimilarDocuments
- 2002IsAmongTopNSimilarDocuments
- 2016IsAmongTopNSimilarDocuments
- 2011IsAmongTopNSimilarDocuments
- 2016IsAmongTopNSimilarDocuments
- 2012IsAmongTopNSimilarDocuments
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).14 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
