2 research outputs found
Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control
The automation of passenger vehicles is becoming more and more widespread,
leading to full autonomy of cars within the next years. Furthermore,
sustainable electric mobility is gaining in importance. As racecars have been a
development platform for technology that has later also been transferred to
passenger vehicles, a race format for autonomous electric racecars called
Roborace has been created. As electric racecars only store a limited amount of
energy, an Energy Management Strategy (EMS) is needed to work out the time as
well as the minimum energy trajectories for the track. At the same time, the
technical limitations and component behavior in the electric powertrain must be
taken into account when calculating the race trajectories. In this paper, we
present a concept for a special type of EMS. This is based on the Optimal
Control Problem (OCP) of generating a time-minimal global trajectory which is
solved by the transcription via direct orthogonal collocation to a Nonlinear
Programming Problem (NLPP). We extend this minimum lap time problem by adding
our ideas for a holistic EMS. This approach proves the fundamental feasibility
of the stated ideas, e.g. varying racepaths and velocities due to energy
limitations, covered by the EMS. Also, the presented concept forms the basis
for future work on meta-models of the powertrain's components that can be fed
into the OCP to increase the validity of the control output of the EMS.Comment: Accepted at the IEEE Intelligent Transportation Systems Conference -
ITSC 2019, Auckland, New Zealand 27 - 30 Octobe
Equivalent Lap Time Minimization Strategies for a Hybrid Electric Race Car
The powertrain of the Formula 1 car is composed of an electrically turbocharged internal combustion engine and an electric motor used for boosting and regenerative braking. The energy management system that controls this hybrid electric power unit strongly influences the achievable lap time, as well as the fuel and battery consumption. Therefore, it is important to design robust feedback control algorithms that can run on the ECU in compliance with the sporting regulations, and are able to follow lap time optimal strategies while properly reacting to external disturbances.
In this paper, we design feedback control algorithms inspired by equivalent consumption minimization strategies (ECMS) that adapt the optimal control policy implemented on the car in real-time. This way, we are able to track energy management strategies computed offline in a lap time optimal way using three PID controllers. We validate the presented control structure with numerical simulations and compare it to a previously designed model predictive control scheme