Fuzzy Logic Estimation of Coincidence Factors for EV Fleet Charging Infrastructure Planning in Residential Buildings

Salvador Carvalhosa (), José Rui Ferreira and Rui Esteves Araújo
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Salvador Carvalhosa: INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Centre for Power and Energy Systems, s/n R. Dr. Roberto Frias, 4200-465 Porto, Portugal
José Rui Ferreira: INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Centre for Power and Energy Systems, s/n R. Dr. Roberto Frias, 4200-465 Porto, Portugal
Rui Esteves Araújo: INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Centre for Power and Energy Systems, s/n R. Dr. Roberto Frias, 4200-465 Porto, Portugal

Energies, 2025, vol. 18, issue 17, 1-24

Abstract: As electric vehicle (EV) adoption accelerates, residential buildings—particularly multi-dwelling structures—face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings.

Keywords: electric vehicle (EV) charging; coincidence factor; electrical infrastructure optimization; fuzzy logic; electrical feeder sizing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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