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Giorgio Rizzoni

Researcher at Center for Automotive Research

Publications -  458
Citations -  17067

Giorgio Rizzoni is an academic researcher from Center for Automotive Research. The author has contributed to research in topics: Electric vehicle & Energy management. The author has an hindex of 61, co-authored 444 publications receiving 15245 citations. Previous affiliations of Giorgio Rizzoni include Ohio State University & University of Michigan.

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Journal ArticleDOI

Proton Exchange Membrane Fuel Cell System Model for Automotive Vehicle Simulation and Control

TL;DR: In this paper, a proton exchange membrane (PEM) fuel cell system model for automotive applications that includes an air compressor, cooling system, and other auxiliaries is described.
Proceedings ArticleDOI

An aging model of Ni-MH batteries for hybrid electric vehicles

TL;DR: In this article, an aging model suitable for applications in which the battery is used with no pre-defined cycles, as in the case of hybrid-electric vehicles, is presented.
Journal ArticleDOI

A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications

TL;DR: In this article, a new approach to battery life estimation for lithium-ion batteries used in plug-in hybrid electric vehicles (PHEVs) applications is presented, whose development is built upon a weighted ampere-hour throughput model of the battery, is based on the novel concept of severity factor map which captures and quantifies the battery damage caused by different operating conditions.
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A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management

TL;DR: In this article, a control-oriented battery pack model is proposed to predict the propagation of aging and its effect on the life span of battery systems, which is based on the interaction between dynamic system models of the electrical and thermal dynamics, and dynamic models of cell aging.
Journal ArticleDOI

Automotive engine diagnosis and control via nonlinear estimation

TL;DR: In this article, the authors explore a possible approach to the problem of designing control and diagnostic strategies for future generations of automotive engines based on the use of physical models to estimate unmeasured or unmeasurable variables and parameters.