Energy Uncertainty Analysis of Electric Buses

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-mak...

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Main Authors: Jari Vepsäläinen, Antti Ritari, Antti Lajunen, Klaus Kivekäs, Kari Tammi
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/11/12/3267
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author Jari Vepsäläinen
Antti Ritari
Antti Lajunen
Klaus Kivekäs
Kari Tammi
author_facet Jari Vepsäläinen
Antti Ritari
Antti Lajunen
Klaus Kivekäs
Kari Tammi
author_sort Jari Vepsäläinen
collection DOAJ
description 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.
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spelling doaj.art-2c063e7be7f84c4091b6593e45b9d6c02022-12-22T04:00:30ZengMDPI AGEnergies1996-10732018-11-011112326710.3390/en11123267en11123267Energy Uncertainty Analysis of Electric BusesJari Vepsäläinen0Antti Ritari1Antti Lajunen2Klaus Kivekäs3Kari Tammi4Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, FinlandDepartment of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, FinlandDepartment of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, FinlandDepartment of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, FinlandDepartment of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, FinlandUncertainty 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.https://www.mdpi.com/1996-1073/11/12/3267energy consumptionelectric busuncertaintysensitivity analysiscorrelated inputs
spellingShingle Jari Vepsäläinen
Antti Ritari
Antti Lajunen
Klaus Kivekäs
Kari Tammi
Energy Uncertainty Analysis of Electric Buses
Energies
energy consumption
electric bus
uncertainty
sensitivity analysis
correlated inputs
title Energy Uncertainty Analysis of Electric Buses
title_full Energy Uncertainty Analysis of Electric Buses
title_fullStr Energy Uncertainty Analysis of Electric Buses
title_full_unstemmed Energy Uncertainty Analysis of Electric Buses
title_short Energy Uncertainty Analysis of Electric Buses
title_sort energy uncertainty analysis of electric buses
topic energy consumption
electric bus
uncertainty
sensitivity analysis
correlated inputs
url https://www.mdpi.com/1996-1073/11/12/3267
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AT anttiritari energyuncertaintyanalysisofelectricbuses
AT anttilajunen energyuncertaintyanalysisofelectricbuses
AT klauskivekas energyuncertaintyanalysisofelectricbuses
AT karitammi energyuncertaintyanalysisofelectricbuses