Seamless shifting and suppressing clutch judder of 2-speed DCT for EV by deep reinforcement learning
| Autor: | Kazuki Ogawa, Tatsuhito Aihara, Gaku Minorikawa |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Advances in Mechanical Engineering, Vol 16 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1687-8140 16878132 |
| DOI: | 10.1177/16878132241293630 |
| Popis: | One of the challenges facing widespread adoption of electric vehicles (EVs) is their short driving range. To address this challenge, the development of various EV transmissions is underway. In transmissions, clutches are used for disconnection from the drive source, and a phenomenon called judder, which is a violent vibration, may occur when the clutch slides on a frictional surface. To resolve this problem, the use of deep reinforcement learning, which is being used and advanced in areas such as machine control in simulations, was focused upon. The purpose of this study is to suppress clutch judder using deep reinforcement learning. The deep reinforcement learning model was developed for seamless gear shift control, and the gearshift results without control were compared with the gearshift results after training. As a result, a control rule that achieves seamless gear shifting while suppressing the judder is designed by applying deep reinforcement learning to gear shifting control. In addition, seamless gear shifting can be achieved for various patterns of friction coefficients, enabling the development of a robust controller for changing the friction coefficients. |
| Databáze: | Directory of Open Access Journals |
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