BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING
Lukas BAUER
lukas.bauer@hs-aalen.deAalen University, Institute for Drive Technology (Germany)
Leon STÜTZ
Aalen University, Institute for Drive Technology (Germany)
Markus KLEY
Aalen University, Institute for Drive Technolog (Germany)
Abstract
The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.
Keywords:
electromobility, powertrain, electric drives, artificial neural network, efficiency modellingReferences
Albers, A., Behrendt, M., Klingler, S., & Matros, K. (2016). Verifikation und Validierung im Produktentstehungsprozess [E-Book]. In M. Behrendt, S. Klingler & K. Matros (Eds.), Handbuch Produktentwicklung (pp. 541–557). Carl Hanser Verlag. https://doi.org/10.3139/9783446445819.019
DOI: https://doi.org/10.3139/9783446445819.019
Google Scholar
Bauer, L., Bauer, M., & Kley, M. (2021). Modelbasierte Validierung der Prüfstandsdynamik zur Erprobung von Komponenten elektrifizierter Antriebsstränge mithilfe eines digitalen Zwillings. Stuttgarter Symposium für Produktentwicklung, SSP 2021, 105–116. https://doi.org/10.18419/opus-11478
Google Scholar
Bauer, L., Beck, P., Stütz, L., & Kley, M. (2021). Enhanced efficiency prediction of an electrified off-highway vehicle transmission utilizing machine learning methods. Procedia Computer Science, 192, 417–426. https://doi.org/10.1016/j.procs.2021.08.043
DOI: https://doi.org/10.1016/j.procs.2021.08.043
Google Scholar
Beine, M., & Rasche, R. (2018). Datenmanagement für das szenariobasierte Testen. ATZextra, 23(S4), 20–25. https://doi.org/10.1007/s35778-018-0024-9
DOI: https://doi.org/10.1007/s35778-018-0024-9
Google Scholar
ÇElik, E., Gör, H., ÖZtürk, N., & Kurt, E. (2017). Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator. International Journal of Hydrogen Energy, 42(28), 17692–17699. https://doi.org/10.1016/j.ijhydene.2017.01.168
DOI: https://doi.org/10.1016/j.ijhydene.2017.01.168
Google Scholar
Dismon, H. (2017). Wir sind gefordert, Entwicklungen schnell und treffsicher umzusetzen. MTZextra, 22(S1), 8–11. https://doi.org/10.1007/s41490-017-0009-4
DOI: https://doi.org/10.1007/s41490-017-0009-4
Google Scholar
Dohmen, H., Pfeiffer, K., & Schyr, C. (2009). Antriebsstrangprüftechnik: Vom stationären Komponententest zum fahrmanöverbasierten Testen (Die Bibliothek der Technik (BT)) (1. Aufl.). Süddeutscher Verlag onpact. German Environment Agency. (2020). Submission under the United Nations Framework Convention on Climate Change and the Kyoto Protocol 2020.
Google Scholar
Guggenmos, J., Rückert, J., Thalmair, S., & Wagner, M. (2018). Das Prüffeld der Antriebsentwicklung im Wandel. VPC – Simulation und Test 2015 (pp. 1–13). Springer. https://doi.org/10.1007/978-3-658-20736-6_1
DOI: https://doi.org/10.1007/978-3-658-20736-6_1
Google Scholar
Hoekstra, A. (2019). The Underestimated Potential of Battery Electric Vehicles to Reduce Emissions. Joule, 3(6), 1412–1414. https://doi.org/10.1016/j.joule.2019.06.002
DOI: https://doi.org/10.1016/j.joule.2019.06.002
Google Scholar
Isermann, R. (2007). Mechatronische Systeme. Springer.
Google Scholar
Jazayeri, K., Jazayeri, M., & Uysal, S. (2016). Comparative Analysis of Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithms in Photovoltaic Power Estimation Using Artificial Neural Network. Advances in Data Mining. Applications and Theoretical Aspects (pp. 80–95). Springer. https://doi.org/10.1007/978-3-319-41561-1_7
DOI: https://doi.org/10.1007/978-3-319-41561-1_7
Google Scholar
Khan, A., Mohammadi, M. H., Ghorbanian, V., & Lowther, D. (2020). Efficiency Map Prediction of Motor Drives Using Deep Learning. IEEE Transactions on Magnetics, 56(3), 1–4. https://doi.org/10.1109/tmag.2019.2957162
DOI: https://doi.org/10.1109/TMAG.2019.2957162
Google Scholar
Li, Y. L., Kley, M., & Wang, S. J. (2014). Driveline Simulation of 2013 Formula Student Electric Racing Vehicle. Applied Mechanics and Materials, 541–542, 424–429. https://doi.org/10.4028/www.scientific.net/amm.541-542.424
DOI: https://doi.org/10.4028/www.scientific.net/AMM.541-542.424
Google Scholar
Machrowska, A., Karpiński, R., Jonak, J., & Krakowski, P. (2020). Numericalprediction of the component-ratiodependent compressive strength of bone cement. Applied Computer Science, 16(3), 88–101. https://doi.org/10.23743/acs-2020-24
Google Scholar
Martini, E., Voß, H., Töpfer, S., & Isermann, R. (2003). Effiziente Motorapplikation mit lokal linearen neuronalen Netzen. MTZ - Motortechnische Zeitschrift, 64(5), 406–413. https://doi.org/10.1007/bf03226705
DOI: https://doi.org/10.1007/BF03226705
Google Scholar
Paulweber, M., & Lebert, K. (2014). Mess- und Prüfstandstechnik: Antriebsstrangentwicklung Hybridisierung Elektrifizierung (Der Fahrzeugantrieb) (2014. Aufl.). Springer. https://doi.org/10.1007/978-3-658-04453-4
DOI: https://doi.org/10.1007/978-3-658-04453-4
Google Scholar
Payal, A., Rai, C. S., & Reddy, B. V. R. (2013). Comparative analysis of Bayesian regularization and LevenbergMarquardt training algorithm for localization in wireless sensor network. 15th International Conference on Advanced Communications Technology (ICACT) (pp. 191–194). IEEE. https://ieeexplore.ieee.org/document/6488169
Google Scholar
Ratov, D., & Lyfar, V. (2020). Modeling transmission mechanisms with determination of efficiency. Applied Computer Science, 16(1), 33–40. https://doi.org/10.23743/acs-2020-03
Google Scholar
Stütz, J., Bauer, L., & Kley, M. (2019). Intelligente Lastkollektivoptimierung für Erprobungen von elektrischen und hybriden Antriebssträngen. Stuttgarter Symposium für Produktentwicklung SSP 2019 (pp. 93–102). Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO. https://doi.org/10.18419/opus-10394
Google Scholar
Stütz, L., Beck, P., & Kley, M. (2021). Wirkungsgraduntersuchungen am Antriebsstrang von Multifunktionsfahrzeugen unter Berücksichtigung von empirisch ermittelten Lastkollektiven. Stuttgarter Symposium für Produktentwicklung SSP 2021 (pp. 445–454). Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO. https://doi.org/10.18419/opus-11478
Google Scholar
The MathWorks. (2020). Statistics and Machine Learning Toolbox User’s Guide. The MathWorks.
Google Scholar
Willmerding, G., & Häckh, J. (2017). Echtzeitsimulation hochdynamischer Fahrzeugantriebe. ASIM-Treffen STS/GMMS 2017 (pp. 192–198). Ulm.
Google Scholar
Yadav, R. N., & Yadava, V. (2017). Artificial neural network modelling of erosion-abrasion-based hybrid machining of aluminium-silicon carbide-boron carbide composite. International Journal of Engineering Systems Modelling and Simulation, 9(2), 63–77. https://doi.org/10.1504/ijesms.2017.083223
DOI: https://doi.org/10.1504/IJESMS.2017.10003531
Google Scholar
Authors
Lukas BAUERlukas.bauer@hs-aalen.de
Aalen University, Institute for Drive Technology Germany
Authors
Leon STÜTZAalen University, Institute for Drive Technology Germany
Authors
Markus KLEYAalen University, Institute for Drive Technolog Germany
Statistics
Abstract views: 177PDF downloads: 42
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
- Łukasz GRABOWSKI, Arkadiusz DROZD, Mateusz KARABELA, Wojciech KARPIUK, AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLE. SIMULATION OF ENERGY CONSUMPTION , Applied Computer Science: Vol. 20 No. 3 (2024)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 4 (2018)
- Boutkhil SIDAOUI, PREDICTING STATES OF EPILEPSY PATIENTS USING DEEP LEARNING MODELS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Sara SALEHI, FUZZY MULTIPLE CRITERIA GROUP DECISION-MAKING IN PERFORMANCE EVALUATION OF MANUFACTURING COMPANIES , Applied Computer Science: Vol. 19 No. 3 (2023)
- Piotr WITTBRODT, Iwona ŁAPUŃKA, Gulzhan BAYTIKENOVA, Arkadiusz GOLA, Alfiya ZAKIMOVA, IDENTIFICATION OF THE IMPACT OF THE AVAILABILITY FACTOR ON THE EFFICIENCY OF PRODUCTION PROCESSES USING THE AHP AND FUZZY AHP METHODS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Muaayed F. AL-RAWI, Izz K. ABBOUD, Nasir A. AL-AWAD, PERFORMANCE ANALYSIS AND EVALUATION OF MASSIVE MIMO SYSTEM , Applied Computer Science: Vol. 16 No. 2 (2020)
- Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA, INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS , Applied Computer Science: Vol. 13 No. 1 (2017)
- Rowell HERNANDEZ, Robert ATIENZA, CAREER TRACK PREDICTION USING DEEP LEARNING MODEL BASED ON DISCRETE SERIES OF QUANTITATIVE CLASSIFICATION , Applied Computer Science: Vol. 17 No. 4 (2021)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
