Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 17, Issue 10, Pages 2777-2786Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2016.2525800
Keywords
Transportation; electric vehicles; discrete-event systems; computer simulation; optimization; computational intelligence
Categories
Funding
- CAPES
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Growing concerns with environmental issues have resulted in alternatives for urban mobility and public transportation. Among the available options, battery-powered electric buses have been considered in terms of flexibility, sustainability, and emission of pollutants. These buses have a plug-in recharge system that allows them to run without any extra power supply. However, battery recharging during operation is necessary, and the number and location of charging stations are important issues for system operation. This paper presents a discrete event simulation that evaluates bus energy consumption using a mathematical model that takes into account different load and friction forces. An optimization strategy that utilizes a bi-objective genetic algorithm is then associated with simulation (in a simulation optimization approach) to minimize both the number of charging stations and the average extra time stopped in the station to recharge. Information, including passenger demand, bus speed, distances, and route elevation profiles, has been obtained for Curitiba public transportation. The parameters of buses' mobility are based on real data acquired, filtered, and analyzed for six express routes from raw data provided by the agency that controls public transportation and urban development. Fast battery recharge with typical parameters is considered at charging stations. The results show different arrangements for the number of stations, their location, and delays in bus schedule, as well as the corresponding bus energy levels in different routes.
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