Energy Uncertainty Analysis of Electric Buses

Jari Vepsäläinen, Antti Ritari, Antti Lajunen, Klaus Kivekäs and Kari Tammi
Additional contact information
Jari Vepsäläinen: Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland
Antti Ritari: Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland
Antti Lajunen: Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland
Klaus Kivekäs: Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland
Kari Tammi: Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland

Energies, 2018, vol. 11, issue 12, 1-29

Abstract: Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

Keywords: energy consumption; electric bus; uncertainty; sensitivity analysis; correlated inputs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/12/3267/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/12/3267/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:12:p:3267-:d:185094

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().