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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.06885 |
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| _version_ | 1866916155925463040 |
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| author | van Kampen, Jorn Moriggi, Mauro Braghin, Francesco Salazar, Mauro |
| author_facet | van Kampen, Jorn Moriggi, Mauro Braghin, Francesco Salazar, Mauro |
| contents | This paper presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1h endurance race at the Zandvoort circuit, using real-life data of internal combustion engine race cars from a previous event. Our results show that optimizing both the race strategy as well as the decision making during the race is very important, resulting in a significant 21s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06885 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions van Kampen, Jorn Moriggi, Mauro Braghin, Francesco Salazar, Mauro Systems and Control This paper presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1h endurance race at the Zandvoort circuit, using real-life data of internal combustion engine race cars from a previous event. Our results show that optimizing both the race strategy as well as the decision making during the race is very important, resulting in a significant 21s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones. |
| title | Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2403.06885 |