BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250804T173036EDT-12580UZu4i@132.216.98.100 DTSTAMP:20250804T213036Z DESCRIPTION:\n Title: Sharing Sustainable Mobility in Smart Cities\n\n\n \n Ab stract:\n \n\n Many cities worldwide are embracing electric vehicle (EV) sha ring as a flexible and sustainable means of urban transit. However\, it re mains challenging for the operators to charge the fleet due to limited or costly access to charging facilities. In this work\, we focus on answering the core question - how to charge the fleet to make EV sharing viable and profitable. Our work is motivated by the recent setback that struck San D iego\, California\, where car2go ceased its EV sharing operations. We inte grate charging infrastructure planning and vehicle repositioning operation s that were often considered separately in the literature. More interestin gly\, our modeling emphasizes the operator-controlled charging operations and customers’ EV picking behavior\, which are both central to EV sharing but were largely overlooked. Motivated by the actual data of car2go\, our model explicitly characterizes how customers endogenously pick EVs based o n energy levels\, and how the operator dispatches EV charging under a targ eted charging policy. We formulate the integrated model as a nonlinear opt imization program with fractional constraints. We then develop both lower- and upper-bound formulations as mixed-integer second order cone programs\ , which are computationally tractable with small optimality gap. Contrary to car2go’s practice\, we find that the viability of EV sharing can be enh anced by concentrating limited charger resources at selected locations. Ch arging EVs in a proactive fashion (rather than car2go’s policy of charging EVs only when their energy level drops below 20%) can boost the profit by 10.7%. Given the demand profile in San Diego\, the fleet size may reduce by up to 34% without incurring significant profit loss. Moreover\, suffici ent charger availability is crucial when collaborating with a public charg er network. Finally\, increasing the charging power relieves the charger r esource constraint\, whereas extending per-charge range or adopting unmann ed repositioning improves profitability. In summary\, our work demonstrate s a data-verified and high-granularity modeling approach. Both the high-le vel planning guidelines and operational policies can be useful for practit ioners. We also highlight the value of jointly managing demand fulfilment and EV charging.\n\n \n Speaker\n \n\n Wei Qi is an an assistant professor at the Desautels Faculty of Management\, 9IÖÆ×÷³§Ãâ·Ñ. His research inte rests include optimization\, smart-city analytics and operations managemen t\, energy and transportation systems operations management (e.g. energy s torage\, electric vehicles\, renewables).\n\n DTSTART:20200214T203000Z DTEND:20200214T213000Z LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue Sherbrooke Ouest SUMMARY:Wei Qi (9IÖÆ×÷³§Ãâ·Ñ) URL:/mathstat/channels/event/wei-qi-mcgill-university- 320270 END:VEVENT END:VCALENDAR