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Yves Dube

Researcher at Université du Québec à Trois-Rivières

Publications -  63
Citations -  2406

Yves Dube is an academic researcher from Université du Québec à Trois-Rivières. The author has contributed to research in topics: Electric vehicle & Battery (electricity). The author has an hindex of 22, co-authored 62 publications receiving 1837 citations. Previous affiliations of Yves Dube include Université du Québec.

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A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures

TL;DR: In this paper, the effects of cold temperatures on the capacity/power fade of Li-ion battery technology are discussed, along with thermal strategies and the ideal approach to cold-temperature operation.
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Characterization and modeling of a hybrid electric vehicle lithium-ion battery pack at low temperatures

TL;DR: The simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV.
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Lithium-Ion Battery Aging Experiments at Subzero Temperatures and Model Development for Capacity Fade Estimation

TL;DR: This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures, and develops and adds a preliminary single-cell electrothermal model to establish a thermal strategy capable of predicting how the cell ages.
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New multi-physics approach for modelling and design of alkaline electrolyzers

TL;DR: In this article, a multi-physics model is used for the design and diagnosis of the alkaline electrolyzers, based on a new approach that allows to choose precisely the design parameters of a new electrolyzer even if it is not commercially available and predicting energy consumption, efficiency and rate of hydrogen production, taking into account to their physical state and various operating conditions.
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Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering

TL;DR: A low-sampling-rate and nonintrusive appliance loads monitoring (NIALM), which required only few tuning parameters and which is little sensitive to the grid power noise, is presented, based on the subtractive clustering and the maximum likelihood classifier.