Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Mouftah, Hussein T.
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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (7/7 displayed)

  • 2020Conic optimisation for electric vehicle station smart charging with battery voltage constraints48citations
  • 2020The impact of domestic electric vehicle charging on electricity networkscitations
  • 2020Coordinated electric vehicle charging to reduce losses without network impedances15citations
  • 2020The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems164citations
  • 2019Mitigating the impact of personal vehicle electrification: a power generation perspective19citations
  • 2018Mitigating the Impact of Personal Vehicle Electrification: a Power Generation Perspective19citations
  • 2018Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis22citations

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Chart of shared publication
Deakin, M.
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Mcculloch, Md
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Morstyn, T.
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Mcculloch, M.
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Apostolopoulou, D.
3 / 4 shared
Chart of publication period
2020
2019
2018

Co-Authors (by relevance)

  • Deakin, M.
  • Mcculloch, Md
  • Morstyn, T.
  • Mcculloch, M.
  • Apostolopoulou, D.
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article

Conic optimisation for electric vehicle station smart charging with battery voltage constraints

  • Deakin, M.
  • Crozier, C.
  • Mcculloch, Md
  • Morstyn, T.

Abstract

This paper proposes a new convex optimisation strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage which occurs as the battery is charged. However, battery voltage rise is an important consideration, since it imposes limits on the maximum charging power. This is particularly relevant for DC fast charging, where the maximum charging power may be severely limited, even at moderate state of charge levels. First, a reduced order battery circuit model is developed, which retains the nonlinear relationship between state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as second-order cone constraints. These constraints are integrated with a linearised power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging. The resulting optimisation is a second-order cone program, and thus can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive.

Topics

  • optimisation
  • implementation
  • bottleneck
  • constraint
  • voltage
  • fee
  • electric vehicle
  • timetable
  • electric vehicle charging
  • accumulator
  • cone
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  • Taiacgg
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