Time-optimal Control of the Formula 1 Hybrid Electric Power Unit
Event details
| Date | 09.05.2017 |
| Hour | 10:15 › 11:15 |
| Speaker | Mauro Salazar, Institute for Dynamic Systems and Control, ETH Zürich |
| Location | |
| Category | Conferences - Seminars |
Abstract: In recent years, the Formula 1 propulsion system has evolved from being a conventional combustion engine towards a highly integrated hybrid electric powertrain. Since 2014 the vehicles have been equipped with an electric motor for extra boosting and regenerative braking, and an electrified turbocharger to improve the engine’s torque response and to recover waste heat from the exhaust gas. The powertrain is controlled by a dedicated energy management system, which significantly influences the vehicle’s acceleration performance as well as its fuel and electric energy consumption. Therefore, the strategy must be carefully optimized.
First, we propose a computationally efficient method to evaluate the theoretic, optimal energy management strategy leading to the best possible lap time. The proposed method allows parameter studies to be conducted within a reasonable time frame of a few minutes, while the optimization results serve as a benchmark for any real-time energy management strategy ultimately to be used during a real race.
Subsequently, we propose a real-time implementable energy management strategy minimizing the lap time, by deriving the optimal control policy analytically. Optimality of the proposed feedforward controller is verified by comparing the results obtained with a benchmark simulator against the global optimal solution, while implementability and compatibility with the regulations are demonstrated using a third-party high-fidelity nonlinear simulator.
Finally, in order to properly react to disturbances, we introduce feedback, using a two-level MPC scheme. The optimality of the presented controller is also verified with the benchmark simulator, and its performance is finally tested on the third-party simulator of the race car under the presence of realistic disturbances.
Bio: Mauro Salazar was born in Zürich, Switzerland, and grew up in Lugano. He received the B.Sc. degree in mechanical engineering from ETH Zürich in 2013, and the M.Sc. degree in mechanical engineering in 2015. He did his master thesis at EPFL with Prof. Colin Jones on the topic Low Energy Control. He is currently pursuing a Ph.D. degree with the Institute for Dynamic Systems and Control, ETH Zürich, under the supervision of Prof. Chris Onder.
His current research interests include optimal control theory, hybrid electric vehicles and model predictive control.
He received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award. His master thesis was awarded the ETH Medal.
First, we propose a computationally efficient method to evaluate the theoretic, optimal energy management strategy leading to the best possible lap time. The proposed method allows parameter studies to be conducted within a reasonable time frame of a few minutes, while the optimization results serve as a benchmark for any real-time energy management strategy ultimately to be used during a real race.
Subsequently, we propose a real-time implementable energy management strategy minimizing the lap time, by deriving the optimal control policy analytically. Optimality of the proposed feedforward controller is verified by comparing the results obtained with a benchmark simulator against the global optimal solution, while implementability and compatibility with the regulations are demonstrated using a third-party high-fidelity nonlinear simulator.
Finally, in order to properly react to disturbances, we introduce feedback, using a two-level MPC scheme. The optimality of the presented controller is also verified with the benchmark simulator, and its performance is finally tested on the third-party simulator of the race car under the presence of realistic disturbances.
Bio: Mauro Salazar was born in Zürich, Switzerland, and grew up in Lugano. He received the B.Sc. degree in mechanical engineering from ETH Zürich in 2013, and the M.Sc. degree in mechanical engineering in 2015. He did his master thesis at EPFL with Prof. Colin Jones on the topic Low Energy Control. He is currently pursuing a Ph.D. degree with the Institute for Dynamic Systems and Control, ETH Zürich, under the supervision of Prof. Chris Onder.
His current research interests include optimal control theory, hybrid electric vehicles and model predictive control.
He received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award. His master thesis was awarded the ETH Medal.
Practical information
- General public
- Free