TY - CHAP
T1 - Energy Management Improvement Based on Fleet Digitalization Data Exploitation for Hybrid Electric Buses
AU - López, Jon Ander
AU - Herrera, Victor Isaac
AU - Camblong, Haritza
AU - Milo, Aitor
AU - Gaztañaga, Haizea
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The chapter focuses on a fleet energy management approach with the aim of reducing operation and maintenance costs. A state-of-the-art is presented for the different proposed fleet management approaches. In order to tackle the digitalization challenge of exploiting the large data volume of a fleet of vehicles, a methodology for improving electrified buses energy efficiency at fleet level is proposed. In addition, an energetic analysis of a fleet based on this methodology has been performed. The analyzed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultracapacitors. In the first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g., traffic jams, auxiliary consumption, and passenger variations) have been added to the routes to simulate “real” road and daily operation conditions. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behavior. Finally, a decision-making process is applied to improve the local energy management of the less-efficient bus.
AB - The chapter focuses on a fleet energy management approach with the aim of reducing operation and maintenance costs. A state-of-the-art is presented for the different proposed fleet management approaches. In order to tackle the digitalization challenge of exploiting the large data volume of a fleet of vehicles, a methodology for improving electrified buses energy efficiency at fleet level is proposed. In addition, an energetic analysis of a fleet based on this methodology has been performed. The analyzed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultracapacitors. In the first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g., traffic jams, auxiliary consumption, and passenger variations) have been added to the routes to simulate “real” road and daily operation conditions. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behavior. Finally, a decision-making process is applied to improve the local energy management of the less-efficient bus.
UR - http://www.scopus.com/inward/record.url?scp=85073206119&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-25446-9_14
DO - 10.1007/978-3-030-25446-9_14
M3 - Capítulo
AN - SCOPUS:85073206119
T3 - Springer Optimization and Its Applications
SP - 321
EP - 355
BT - Springer Optimization and Its Applications
PB - Springer International Publishing
ER -