Transportation is a crucial cog within the cog-wheel of our economies and modern lifestyles. Unfortunately, both the rising cost of energy production and the increasing demand for transportation pose the challenge of minimizing the energy consumption of automobiles. This paper proposes an offline driver behavior adaptation approach (eco-driving) for trains. An optimal driving behavior policy is computed using Simulated Annealing optimization search over a collection of real driving behavior data (realistic policy). Empirical findings show that if drivers would follow the recommended optimal policy, then an energy saving of up to 50 % is a realistic upper bound potential.


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    Titel :

    Realistic optimal policies for energy-efficient train driving


    Beteiligte:


    Erscheinungsdatum :

    01.10.2014


    Format / Umfang :

    482646 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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