Milla Vehviläinen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Milla Vehviläinen has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Anouar Belahcen
Aalto University
2 shared publications - Janne Keränen
VTT Technical Research Centre of Finland Ltd., Espoo, Finland
2 shared publications - Pekka Rahkola
VTT Technical Research Centre of Finland, FI-02150 Espoo, Finland
2 shared publications - Antti Holopainen1 shared publication
- Jari Halme
VTT Technical Research Centre of Finland Ltd., Visiokatu 4, 33720 Tampere, Finland
1 shared publication - Jenni Pippuri-Mäkeläinen
VTT Technical Research Centre of Finland Ltd
1 shared publication
Latest Publications
- Open Access
- 2 December 2023
- Published by Elsevier in Mechanism and Machine Theory
- Vol. 192
Adapting geometry-based polygonal contacts for simulating faulty rolling bearing dynamics
- 2 December 2023
- Published by Elsevier in Mechanism and Machine Theory
- Vol. 192
Open AccessAdapting geometry-based polygonal contacts for simulating faulty rolling bearing dynamics
- Open Access
Simulation-Based Comparative Assessment of a Multi-Speed Transmission for an E-Retrofitted Heavy-Duty Truck
Electric retrofitting (e-retrofitting) is a viable option for accelerating the renewal of heavy-duty vehicle fleets to reduce the related emissions. We introduce a simulation-based assessment of e-retrofitting strategies for heavy-duty vehicles. Our simulation tool, an electric vehicle fleet simulation toolbox, comprises three modules, namely driving cycles, vehicle dynamics, and vehicle profiles. The first allows for the creation of realistic driving cycles based on GPS data from real routes. The vehicle dynamics and vehicle profiles incorporate, e.g., the modelling of the powertrain and driving conditions. Ten realistic driving cycles were created and used for investigating and comparing three different powertrain alternatives, including the original diesel powertrain, electric with a single-speed transmission and electric with a multi-speed transmission. The vehicles were simulated in two different heavy-load scenarios. First, driving with a cargo load represented by the maximum vehicle weight and second, driving with snow ploughing. We found that the multi-speed transmission in an electric heavy-duty truck significantly improved its traction performance and gradeability. On the other hand, the effect on the electric powertrain efficiency, and thereby on the energy consumption, remained rather minor. Considering the given workload scenarios, our results advocate employing rather than omitting the gearbox in the e-retrofit truck process.
Open AccessSimulation-Based Comparative Assessment of a Multi-Speed Transmission for an E-Retrofitted Heavy-Duty Truck
Electric retrofitting (e-retrofitting) is a viable option for accelerating the renewal of heavy-duty vehicle fleets to reduce the related emissions. We introduce a simulation-based assessment of e-retrofitting strategies for heavy-duty vehicles. Our simulation tool, an electric vehicle fleet simulation toolbox, comprises three modules, namely driving cycles, vehicle dynamics, and vehicle profiles. The first allows for the creation of realistic driving cycles based on GPS data from real routes. The vehicle dynamics and vehicle profiles incorporate, e.g., the modelling of the powertrain and driving conditions. Ten realistic driving cycles were created and used for investigating and comparing three different powertrain alternatives, including the original diesel powertrain, electric with a single-speed transmission and electric with a multi-speed transmission. The vehicles were simulated in two different heavy-load scenarios. First, driving with a cargo load represented by the maximum vehicle weight and second, driving with snow ploughing. We found that the multi-speed transmission in an electric heavy-duty truck significantly improved its traction performance and gradeability. On the other hand, the effect on the electric powertrain efficiency, and thereby on the energy consumption, remained rather minor. Considering the given workload scenarios, our results advocate employing rather than omitting the gearbox in the e-retrofit truck process.