- title
- Optimization of operation strategy for primary torque based hydrostatics drivetrain using artificial intelligence
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-710730
- conference
- 12th International Fluid Power Conference (12. IFK). Dresden, October 12 – 14, 2020
- Source
- Volume 1 – Symposium - 1
Erscheinungsort: Dresden
Verlag: Technische Universität Dresden
Erscheinungsjahr: 2020
Bandnummer Schriftenreihe: 1
Seiten: 55-65
DOI: 10.25368/2020.6 - doi
- https://doi.org/10.25368/2020.13
- Abstract (EN)
- A new primary torque control concept for hydrostatics mobile machines was introduced in 2018 [1]. The mentioned concept controls the pressure in a closed circuit by changing the angle of the hydraulic pump to achieve the desired pressure based on a feedback system. Thanks to this concept, a series of advantages are expected [2]. However, while working in a Y cycle, the primary torque controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover due to lack of recuperation ability. Alternatively, we use deep learning algorithms to improve machines’ regeneration performance. In this paper, we firstly make a potential analysis to show the benefit by utilizing the regeneration process, followed by proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles. Compared to existing algorithms, the CRDNN with bidirectional LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable performance but much fewer training parameters. Based on our dataset including 119 truck loading cycles, our best neural network shows a 98.2 % test accuracy. Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.
- Keywords (DE)
- 12. IFK, Mobile Maschinen, Y-Zyklus-Erkennung, Deep Learning, Energieverwaltung, Hydrostatik, Primäre Drehmomentregelung
- Keywords (EN)
- 12th International Fluid Power Conference, Mobile machines, Y cycle detection, Deep learning, Power management, Hydrostatics, Primary torque control
- Classification (DDC)
- 620
- Classification (RVK)
- ZQ 5460
- university_publisher
- Technische Universität Dresden, Dresden
- corporation_other
- Dresdner Verein zur Förderung der Fluidtechnik e. V. Dresden, Dresden
- version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-710730
- Qucosa date of publication
- 23.06.2020
- Document type
- in_proceeding
- Document language
- English
- licence
