Driving cycle synthesis, aiming for realness, by extending real-world driving databases

In order to transform conventional buses into electric ones, exact knowledge of the energy consumption vehicles is essential. Furthermore, for a proper design transition and avoid inefficiencies excessive costs, this information must be adjusted real operating scenarios. However, recurring problem in context lack data address all these issues. Previous studies have focused on use standard driving cycles or synthesis from single route. This paper presents methodology extending real-world databases perform massive simulations, thereby narrowing confidence interval estimates. As case study, method was applied municipal bus operator’s database project assess feasibility retrofitting diesel an bus. The proposed framework useful generating valid research distribution powertrain optimization, as well support public transport operators manufacturers.