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In order to transform conventional buses into electric ones, exact knowledge of the energy consumption of the vehicles is essential. Furthermore, for a proper design of the transition and to avoid inefficiencies and excessive costs, this information must be adjusted to real operating scenarios. However, a recurring problem in this context is the lack of data to address all these issues. Previous studies have focused on the use of standard driving cycles or on the synthesis of cycles from a single route. This paper presents a methodology for extending real-world driving databases to perform massive simulations, thereby narrowing the confidence interval of estimates. As a case study, the method was applied to a municipal bus operator&#x2019;s database in a project to assess the feasibility of retrofitting a diesel to an electric bus. The proposed framework is useful for generating a valid database for research on energy consumption distribution and powertrain optimization, as well as to support public transport bus operators and manufacturers.

Driving Cycle Synthesis, Aiming for Realness, by Extending Real-World Driving Databases