@article{WeinkathNettKim2023,
  author    = {Miriam Weinkath and Simon Nett and Chong Dae Kim},
  title     = {Feasibility Study of Wheel Torque Prediction with a Recurrent Neural Network Using Vehicle Data},
  series   = {Vehicles},
  volume    = {5},
  number    = {2},
  editor    = {Sohel Anwar},
  publisher = {MDPI},
  issn      = {2624-8921},
  doi       = {10.3390/vehicles5020033},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-23709},
  pages     = {605 -- 614},
  year      = {2023},
  abstract  = {In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55\% and a root mean squared error (RMSE) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination (R2) score of −1.396 and an RMSE score of 69.4 °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology.},
  language  = {en}
}