City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis

Alternative powertrains are rapidly increasing in popularity in city buses. Hence, it is vitally important to understand the factors affecting the performance of the powertrains in order to operate them on appropriate routes and as efficiently as possible. To that end, this paper presents an exhaust...

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Main Authors: Klaus Kivekäs, Antti Lajunen, Jari Vepsäläinen, Kari Tammi
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/11/7/1755
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author Klaus Kivekäs
Antti Lajunen
Jari Vepsäläinen
Kari Tammi
author_facet Klaus Kivekäs
Antti Lajunen
Jari Vepsäläinen
Kari Tammi
author_sort Klaus Kivekäs
collection DOAJ
description Alternative powertrains are rapidly increasing in popularity in city buses. Hence, it is vitally important to understand the factors affecting the performance of the powertrains in order to operate them on appropriate routes and as efficiently as possible. To that end, this paper presents an exhaustive driving cycle and passenger load sensitivity analysis for the most common city bus powertrain topologies. Three-thousand synthetic cycles were generated for a typical suburban bus route based on measured cycles and passenger numbers from the route. The cycles were simulated with six bus models: compressed natural gas, diesel, parallel hybrid, series hybrid, hydrogen fuel cell hybrid, and battery electric bus. Twenty reference cycles featuring various types of routes were simulated for comparison. Correlations between energy consumption and the various driving cycle parameters and passenger loads were examined. Further analysis was conducted with variance decomposition. Aggressiveness and stop frequency had the highest correlation with the consumption. The diesel bus was the most sensitive to aggressiveness. The parallel hybrid had a lower statistical dispersion of consumption than the series hybrid on the suburban route. On the varied routes, the opposite was true. The performance of the parallel hybrid powertrain deteriorated significantly on cycles with high aggressiveness and stop frequency. In general, the high correlation between aggressiveness and energy consumption implies that particular attention must be paid to limiting high-speed accelerations of city buses.
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spelling doaj.art-a0ba75eec77947648875a0db8c9b7b902022-12-22T04:23:33ZengMDPI AGEnergies1996-10732018-07-01117175510.3390/en11071755en11071755City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity AnalysisKlaus Kivekäs0Antti Lajunen1Jari Vepsäläinen2Kari Tammi3Department 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, FinlandAlternative powertrains are rapidly increasing in popularity in city buses. Hence, it is vitally important to understand the factors affecting the performance of the powertrains in order to operate them on appropriate routes and as efficiently as possible. To that end, this paper presents an exhaustive driving cycle and passenger load sensitivity analysis for the most common city bus powertrain topologies. Three-thousand synthetic cycles were generated for a typical suburban bus route based on measured cycles and passenger numbers from the route. The cycles were simulated with six bus models: compressed natural gas, diesel, parallel hybrid, series hybrid, hydrogen fuel cell hybrid, and battery electric bus. Twenty reference cycles featuring various types of routes were simulated for comparison. Correlations between energy consumption and the various driving cycle parameters and passenger loads were examined. Further analysis was conducted with variance decomposition. Aggressiveness and stop frequency had the highest correlation with the consumption. The diesel bus was the most sensitive to aggressiveness. The parallel hybrid had a lower statistical dispersion of consumption than the series hybrid on the suburban route. On the varied routes, the opposite was true. The performance of the parallel hybrid powertrain deteriorated significantly on cycles with high aggressiveness and stop frequency. In general, the high correlation between aggressiveness and energy consumption implies that particular attention must be paid to limiting high-speed accelerations of city buses.http://www.mdpi.com/1996-1073/11/7/1755city buspowertraindriving cyclepassenger loadenergy consumption
spellingShingle Klaus Kivekäs
Antti Lajunen
Jari Vepsäläinen
Kari Tammi
City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
Energies
city bus
powertrain
driving cycle
passenger load
energy consumption
title City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
title_full City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
title_fullStr City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
title_full_unstemmed City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
title_short City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis
title_sort city bus powertrain comparison driving cycle variation and passenger load sensitivity analysis
topic city bus
powertrain
driving cycle
passenger load
energy consumption
url http://www.mdpi.com/1996-1073/11/7/1755
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AT anttilajunen citybuspowertraincomparisondrivingcyclevariationandpassengerloadsensitivityanalysis
AT jarivepsalainen citybuspowertraincomparisondrivingcyclevariationandpassengerloadsensitivityanalysis
AT karitammi citybuspowertraincomparisondrivingcyclevariationandpassengerloadsensitivityanalysis