Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data
The imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/2/71 |
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| author | Gordon Witham Daniel Swierc Anna Rozum Lutz Eckstein |
| author_facet | Gordon Witham Daniel Swierc Anna Rozum Lutz Eckstein |
| author_sort | Gordon Witham |
| collection | DOAJ |
| description | The imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage profiles, as heavy-duty vehicles experience significantly differing loads depending on their field of operation. Real driving data, representing the demands of different application scenarios, offers great potential for digital replication of driving conditions at different stages of simulation and physical validation. Application- and vehicle-specific longitudinal requirements during operation are particularly relevant for the dimensioning of powertrain components. Road gradient and mass estimation assist in the description of these operating conditions, allowing for detailed modeling of the real load conditions. An incorporation of real driving data instead of solely relying on standardized cycles has the potential of tailoring components to the target lead users and applications. While some operating conditions can be recorded by vehicle manufacturers, these are usually not accessible by third parties. In this paper, the authors present an innovative methodology of estimating vehicle parameters for the generation of representative driving profiles for implementation into a consecutive powertrain design process. The approach combines the measurement of real driving data with state estimation. The authors show that the presented methodology enables the generation of driving profiles with less than 25% deviation from the original data set. |
| format | Article |
| id | doaj-art-38d3357cff394d40941dd5c668d1b4bb |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-38d3357cff394d40941dd5c668d1b4bb2025-08-20T02:45:30ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011627110.3390/wevj16020071Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle DataGordon Witham0Daniel Swierc1Anna Rozum2Lutz Eckstein3Institute for Automotive Engineering (ika), RWTH Aachen University, Steinbachstr. 7, 52074 Aachen, GermanyInstitute for Automotive Engineering (ika), RWTH Aachen University, Steinbachstr. 7, 52074 Aachen, GermanyInstitute for Automotive Engineering (ika), RWTH Aachen University, Steinbachstr. 7, 52074 Aachen, GermanyInstitute for Automotive Engineering (ika), RWTH Aachen University, Steinbachstr. 7, 52074 Aachen, GermanyThe imperative for electrification of road transport, driven by global climate targets, underscores the need for innovative powertrain systems in heavy-duty vehicles. When developing new electric drive modules, individual operational requirements need to be considered instead of generalized usage profiles, as heavy-duty vehicles experience significantly differing loads depending on their field of operation. Real driving data, representing the demands of different application scenarios, offers great potential for digital replication of driving conditions at different stages of simulation and physical validation. Application- and vehicle-specific longitudinal requirements during operation are particularly relevant for the dimensioning of powertrain components. Road gradient and mass estimation assist in the description of these operating conditions, allowing for detailed modeling of the real load conditions. An incorporation of real driving data instead of solely relying on standardized cycles has the potential of tailoring components to the target lead users and applications. While some operating conditions can be recorded by vehicle manufacturers, these are usually not accessible by third parties. In this paper, the authors present an innovative methodology of estimating vehicle parameters for the generation of representative driving profiles for implementation into a consecutive powertrain design process. The approach combines the measurement of real driving data with state estimation. The authors show that the presented methodology enables the generation of driving profiles with less than 25% deviation from the original data set.https://www.mdpi.com/2032-6653/16/2/71electric powertrainheavy-dutydrive cycle generationpowertrain design |
| spellingShingle | Gordon Witham Daniel Swierc Anna Rozum Lutz Eckstein Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data World Electric Vehicle Journal electric powertrain heavy-duty drive cycle generation powertrain design |
| title | Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data |
| title_full | Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data |
| title_fullStr | Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data |
| title_full_unstemmed | Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data |
| title_short | Innovative Methodology for Generating Representative Driving Profiles for Heavy-Duty Trucks from Measured Vehicle Data |
| title_sort | innovative methodology for generating representative driving profiles for heavy duty trucks from measured vehicle data |
| topic | electric powertrain heavy-duty drive cycle generation powertrain design |
| url | https://www.mdpi.com/2032-6653/16/2/71 |
| work_keys_str_mv | AT gordonwitham innovativemethodologyforgeneratingrepresentativedrivingprofilesforheavydutytrucksfrommeasuredvehicledata AT danielswierc innovativemethodologyforgeneratingrepresentativedrivingprofilesforheavydutytrucksfrommeasuredvehicledata AT annarozum innovativemethodologyforgeneratingrepresentativedrivingprofilesforheavydutytrucksfrommeasuredvehicledata AT lutzeckstein innovativemethodologyforgeneratingrepresentativedrivingprofilesforheavydutytrucksfrommeasuredvehicledata |