Sustainable Energy for Smart Cities. First EAI International Conference, SESC 2019, Braga, Portugal, December 4–6, 2019, Proceedings

Research Article

Towards Smart Railways: A Charging Strategy for On-Board Energy Storage Systems

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  • @INPROCEEDINGS{10.1007/978-3-030-45694-8_3,
        author={V\^{\i}tor Morais and Jo\"{a}o Afonso and Ant\^{o}nio Martins},
        title={Towards Smart Railways: A Charging Strategy for On-Board Energy Storage Systems},
        proceedings={Sustainable Energy for Smart Cities. First EAI International Conference, SESC 2019, Braga, Portugal, December 4--6, 2019, Proceedings},
        proceedings_a={SESC},
        year={2020},
        month={6},
        keywords={Railway power systems On-board energy storage systems Fuzzy Logic Controllers Genetic algorithms Meta-heuristics Energy efficiency},
        doi={10.1007/978-3-030-45694-8_3}
    }
    
  • Vítor Morais
    João Afonso
    António Martins
    Year: 2020
    Towards Smart Railways: A Charging Strategy for On-Board Energy Storage Systems
    SESC
    Springer
    DOI: 10.1007/978-3-030-45694-8_3
Vítor Morais1,*, João Afonso2, António Martins1
  • 1: University of Porto
  • 2: University of Minho
*Contact email: v.morais@fe.up

Abstract

The huge power requirements of future railway transportation systems require the usage of energy efficient strategies towards a more intelligent railway system. With the usage of on-board energy storage systems, it is possible to increase the energy efficiency of railways. In this paper, a top-level charging controller for the on-board energy storage system is proposed based on a fuzzy logic controller. As an optimization procedure to increase the energy efficiency of such charging controller, a genetic algorithm meta-heuristic is used to automatically tune the fuzzy rules weight. To validate the proposed controller, two sets of rules were defined, one considering only known rules and the other also considering all possible combinations of rules. As global results, the reduction of regenerated energy reached 30%, and the net energy consumption reduction is near 10%.