Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV
A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver conve...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/10/3/57 |
| _version_ | 1811263136647348224 |
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| author | Kyunghan Min Gyubin Sim Seongju Ahn Inseok Park Seungjae Yoo Jeamyoung Youn |
| author_facet | Kyunghan Min Gyubin Sim Seongju Ahn Inseok Park Seungjae Yoo Jeamyoung Youn |
| author_sort | Kyunghan Min |
| collection | DOAJ |
| description | A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking. |
| first_indexed | 2024-04-12T19:40:17Z |
| format | Article |
| id | doaj.art-fb2223aae89140468817725cb2e8447f |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-04-12T19:40:17Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-fb2223aae89140468817725cb2e8447f2022-12-22T03:19:07ZengMDPI AGWorld Electric Vehicle Journal2032-66532019-09-011035710.3390/wevj10030057wevj10030057Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EVKyunghan Min0Gyubin Sim1Seongju Ahn2Inseok Park3Seungjae Yoo4Jeamyoung Youn5Department of Automotive Electronics and Controls, Hanyang University, Seoul 04763, KoreaDepartment of Automotive Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Automotive Electronics and Controls, Hanyang University, Seoul 04763, KoreaResearch & Development Division, Hyundai Motor Company, Hwaseong 445-706, KoreaResearch & Development Division, Hyundai Motor Company, Hwaseong 445-706, KoreaResearch & Development Division, Hyundai Motor Company, Hwaseong 445-706, KoreaA smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking.https://www.mdpi.com/2032-6653/10/3/57autonomous deceleration controlelectric vehicleadvanced driver assistance systemdeceleration planningreinforcement learningdriver characteristics |
| spellingShingle | Kyunghan Min Gyubin Sim Seongju Ahn Inseok Park Seungjae Yoo Jeamyoung Youn Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV World Electric Vehicle Journal autonomous deceleration control electric vehicle advanced driver assistance system deceleration planning reinforcement learning driver characteristics |
| title | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV |
| title_full | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV |
| title_fullStr | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV |
| title_full_unstemmed | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV |
| title_short | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV |
| title_sort | multi level deceleration planning based on reinforcement learning algorithm for autonomous regenerative braking of ev |
| topic | autonomous deceleration control electric vehicle advanced driver assistance system deceleration planning reinforcement learning driver characteristics |
| url | https://www.mdpi.com/2032-6653/10/3/57 |
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