RT conference output
T1 Electric Drive Assignment Strategies Optimization for Plugin Hybrid Urban Buses on Tailored Emissions Mapping
A1 Aragón Jurado, José Miguel
A1 Díaz Jiménez, Marina
A1 Dorronsoro Díaz, Bernabé
A1 Pavón Domínguez, Pablo
A1 Seredynski, M.
A1 Ruiz Villalobos, Patricia
A2 Ingeniería Informática
A2 Ingeniería Mecánica y Diseño Industrial
K1 Sustainable transportation
K1 plug-in hybrid vehicles
K1 digital elevation model
K1 urban mapping
K1 urban public transport
K1 genetic algorithms
K1 multi-objective optimization
AB Traffic congestion, noise and tailpipe emissions are negative externalities from transportation that need to be reduced to make cities more livable. Electric buses can help significantly reducing noise and tailpipe emissions. However, they provide nowadays limited range due to battery capacity, affecting their operating flexibility. Plug-in electric hybrid buses are a versatile solution that offer certain zero-emission capability, which de-pends on the battery capacity and how electric drive is distributed over a route. This is managed by an electric drive assignment system that determines the optimal locations to use the electric motor. Today's assignment systems are in their early development stages, and do not exploit the full potentials. This work introduces a novel combinatorial optimization problem to find optimal electric drive assignment strategies for a number of bus lines under the presence of zero- and restricted-emissions zones. The latter is a new concept introduced here where tailpipe emissions are allowed but restricted to a predefined limit. The goal is to maximize the electric range of the buses and minimize the total pollution emitted, while at the same time complying with the mandatory zero-emission zones and the restricted-emission zones. CCMOCell, a parallel Cooperative Co-evolutionary version of the well-known Multi-objective Cellular genetic algorithm, MOCell, is proposed to solve the problem, and solutions are compared against those found by MOCell itself, and validated using a heuristic from the literature, GreenK. Six lines from Barcelona's bus network (Spain), are considered, and real topological data obtained from administrations and digital elevation models are used. The two multi-objective algorithms reduced pollutant emissions by up to 20% with respect to GreenK. Also, CCMOCell outperformed it in terms of electric range by up to 6.26%.
PB IEEE
YR 2024
FD 2024-05
LK http://hdl.handle.net/10498/33071
UL http://hdl.handle.net/10498/33071
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026