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PWd3.19
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| Beschleunigung | 0-100km/h: 2,0s |
|---|---|
| Leistung | 4x28kW |
| Gewicht | 226kg |
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| Monocoque | CFK einteillig |
|---|---|
| Aerodynamik | CFK Front-, Seit- und Heckflügel, Skid Plate und Splitter |
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| Motoren | 4 Radnabenmotoren |
|---|---|
| Spitzenleistung | je Motor: 28kw |
| Getriebe | 1 1/2 stufiges Planetengetriebe |
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| Akkucontainer | Zentraler Akkucontainer mit 7,5kWh |
|---|---|
| Zellen | Samsung INR 18650 25 R |
| Maximalspannung | 580V |
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| Steuereinheit | ETAS |
|---|---|
| Fahrdynamik Regelsysteme | Torque Vectoring, Rekuperation, Traktionskontrolle, Leistungsbegrenzung |
| Telemetrie | Live Telemetrie via 2,4GHz WLAN |
| Dashboard | eigenentwickeltes Dashboard |
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| Radstand | 1540mm |
|---|---|
| Spurweite Vorne | 1150mm |
| Spurweite Hinten | 1128mm |
| Reifen | 7,5″x10″ Hoosier Slicks |
| Felgen | 10″ CFK Felgen mit Aluminiumstern |
| Querlenker | Doppelquerlenker |
| Dämpfer | Pushrod betätigte Dämpfer |
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| Kameras | 2x Intel D435, 1x Bosch SVC2 |
|---|---|
| Sensoren | Kistler Correvit SFII, Bosch MM5.10-R Beschleunigungssensor, VectorNav VN-300 INS |
| Verabrbeitungseinheiten | NVIDIA Jetson Xavier, Filtet II, ETAS ES910 |
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| Umfelderkennung | Objekterkennung über neuronales Netz |
|---|---|
| Mapping/Lokalisierung | Eigenentwickelte C++ Bibliotheken mit einem Fokus auf Speicherplatzoptimierung |
| Testumgebung | Hardware in the Loop Prüfstand |
| Toolchain | C++, Python, Matlab, IPG Carmaker |
| Trajektorienplanung | Modellbasiertes off-policy Reinforcement Learning zur Optimierung der Rundenzeiten |
| Trainingsautomatisierung | Eigenentwickelte Software zur Datensatzverwaltung, automatische Hyperparameteroptimierung und Verwaltung von Maschinen im Trainingscluster |
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