Selecting sustainable electric bus powertrains using multipreference evolutionary algorithms
Document Type
Journal Article
Publication Date
2018
Subject Area
infrastructure - vehicle, economics - operating costs, mode - bus, place - urban, operations - performance, technology - alternative fuels, technology - emissions
Keywords
Decision making, electric vehicles, life cycle analysis, multi-objective optimization, physical programming, urban bus
Abstract
Constant improvement of vehicle technologies towards more efficient powertrains and reduced pollutant emissions, frequently leads to the increase of the vehicle or fuel costs, compromising its viability. Multi-objective optimization methods are commonly used to solve such problems, finding optimal trade-off solutions relatively conflicting objectives. Nevertheless, vehicle driving performance, is often disregarded from the optimization process or considered only as a fixed constraint. This may raise some issues, which are discussed in this paper: (a) vehicle dynamics are not improved, (b) trade-off optimal solutions are not distinguishable, (c) interesting solutions near constraints limits won´t be considered if constraints are not marginally relaxed.
This paper proposes a method to optimize three electric-drive vehicle options for an urban bus, a battery electric (BEV), a fuel cell hybrid (FC-HEV) and a plug-in hybrid (FC-PHEV), aiming minimum carbon footprint, maximum financial indicator and simultaneously improved driving performance (speed, acceleration, and electric range). The carbon footprint is assessed by a life cycle (LC) approach, considering the impact of the fuel production and use, and vehicle embodied materials; while the financial assessment considers the vehicle and fuel costs. The spherical pruning multi-objective differential evolution algorithm (spMODE-II) is used in the optimization, considering different preference regions within the problem constraints and objectives. The vehicle solutions optimality and suitability are compared with other multi-objective algorithm, NSGA-II.
The FC-HEV achieved the lowest LC emissions (547 g/km), and the FC-PHEV the maximum financial gain (0.19 $/km), while the BEV achieved the best trade-off of solutions.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Ribau, J.P., Vieira, S.M., & Silva, C.M. (2018). Selecting sustainable electric bus powertrains using multipreference evolutionary algorithms. International Journal of Sustainable Transportation, Vol. 12, pp. 592-612.
