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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.
Papers
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A near–optimal rule–based energy management strategy for medium duty hybrid truck
TL;DR: In this article, a rule-based energy management strategy for a medium duty hybrid truck with a clutched clutch is presented. But the performance of the proposed energy management control strategy is studied by using a proposed longitudinal vehicle model of a pre-transmission parallel medium duty pickup with a clutch.
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A lithium-ion battery model including electrical double layer effects
TL;DR: In this article, the authors examined the effect of the electrical double layer on the performance of a lithium ion battery electrochemical cell and derived an expression for the current-voltage relationship in the electroneutral liquid within the separator pores.
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A new interpretation of the fault-detection filter Part 2. The optimal detection filter
TL;DR: In this article, Park and Rizzoni (1993) obtained closed-form expressions for detection filters; the structure of all detection filters for a given fault direction was defined, and the necessary conditions for the existence of the optimal detection filter were obtained, and a numerical solution technique was shown to be feasible by virtue of the uniqueness of the detection filter gains.
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Design and Validation of a Control-Oriented Model of a Diesel Engine with Two-Stage Turbocharger
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PEV Charging Control Considering Transformer Life and Experimental Validation of a 25 kVA Distribution Transformer
TL;DR: The aging model makes it possible to develop charging control strategies that protect the transformer system while maximizing overall PEV charging quality, and makes use of load prediction algorithms using data-driven models that are based on actual electricity consumption data.