TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Weinkath, Miriam
A1  - Nett, Simon
A1  - Kim, Chong Dae
ED  - Anwar, Sohel
T1  - Feasibility Study of Wheel Torque Prediction with a Recurrent Neural Network Using Vehicle Data
JF  - Vehicles
N2  - 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.
KW  - Weissagung
KW  - Time Series Prediction
KW  - Virtual Sensor
KW  - LSTM
KW  - Automotive
KW  - Twinster
KW  - Prediction
KW  - Neural Network
KW  - All-Wheel Drive
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-23709
SN  - 2624-8921
SS  - 2624-8921
U6  - https://doi.org/10.3390/vehicles5020033
DO  - https://doi.org/10.3390/vehicles5020033
VL  - 5
IS  - 2
SP  - 605
EP  - 614
S1  - 10
PB  - MDPI
ER  -