Currently, there is an increasing interest for driving cycles (DCs) that truly represent the driving pattern of a given region aiming to evaluate the energy efficiency of electric vehicles and identify strategies of energy optimization. However, it has been observed increasing differences in the energy consumption reported using type-approval DCs and the observed in the vehicles under real conditions of use. This work compared the Micro-trips, Markov-chains and the MWD-CP methods in their ability of constructing DCs that represent local driving patterns. For this purpose, we used a database made of 138 time series of speed obtained monitoring during eight months a fleet of 15 transit buses operating on roads with different levels of service, traffic and road grades, under normal conditions of use. Then, we used 16 characteristic parameters, such as mean speed or positive kinetic energy, to describe the driving pattern of the buses’ drivers monitored. Subsequently, we implemented three of the most widely used methods to construct DCs using this common database as input data. Finally, we evaluated the degree of representativeness of the local driving pattern contained in each of the obtained DCs. Toward that end, we defined that a DC represents a driving pattern when its characteristic parameters are equal to the characteristic parameters of the driving pattern. Therefore, we used as criteria of representativeness the relative differences between paired characteristic parameters and observed that the MWD-CP method produced the DC that best represents the driving pattern in the region where the buses were monitored, followed by the DC produced by the Micro-trips method.
|Número de páginas||12|
|Publicación||International Journal of Sustainable Energy Planning and Management|
|Estado||Published - 1 ago 2019|
All Science Journal Classification (ASJC) codes
- Geografía, planificación y desarrollo
- Energías renovables, sostenibilidad y medio ambiente
- Ingeniería energética y tecnologías de la energía