The work presented in this paper proposes an innovative energy management approach for hybrid-electric powertrain for optimizing the power exchange among multiple energy sources such as battery pack, fuel cell and solar panels for competing in an endurance race in a maritime application. The algorithm's goal is the minimization of a racetrack time, achieved by ensuring the charge-depletion while maintaining the most efficient working point for all the energy sources. Starting from analyzing and modelling the powertrain architecture and vehicle dynamics, the energy economy problem formulation is done following the mathematical principles behind the optimal control theory. Then, to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, to support mathematical modelling and considerations, simulations and real-time hardware testing have been performed in a real-world race scenario, showing promising results.
Campanini A., Simonazzi M., Peirano F., Rossi C. (2023). Rule-Based Energy Supervisory in Racing Hybrid-Electric Powertrain for Minimizing the Racetrack Time. Institute of Electrical and Electronics Engineers Inc. [10.1109/EUROCON56442.2023.10199049].
Rule-Based Energy Supervisory in Racing Hybrid-Electric Powertrain for Minimizing the Racetrack Time
Campanini A.;Simonazzi M.;Rossi C.
2023
Abstract
The work presented in this paper proposes an innovative energy management approach for hybrid-electric powertrain for optimizing the power exchange among multiple energy sources such as battery pack, fuel cell and solar panels for competing in an endurance race in a maritime application. The algorithm's goal is the minimization of a racetrack time, achieved by ensuring the charge-depletion while maintaining the most efficient working point for all the energy sources. Starting from analyzing and modelling the powertrain architecture and vehicle dynamics, the energy economy problem formulation is done following the mathematical principles behind the optimal control theory. Then, to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, to support mathematical modelling and considerations, simulations and real-time hardware testing have been performed in a real-world race scenario, showing promising results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.