A Sequential Convex Programming Approach to Free-trajectory Minimum-lap-time Optimization of Racing Cars

TL;DR

This paper introduces a sequential convex programming framework for efficiently computing minimum-lap-time trajectories and powertrain strategies for racing cars, enabling near real-time optimization with improved lap times.

Contribution

It develops a convex optimization-based method for joint trajectory and powertrain optimization, significantly reducing computation time and improving lap-time performance.

Findings

Computation time reduced from minutes to seconds.

Minimum-time racing line yields 4% faster lap-times.

Energy constraints have negligible impact on optimal racing line.

Abstract

This paper presents a modeling and optimization framework to compute the minimum-lap-time spatial trajectory and powertrain operation of racing cars in a computationally efficient fashion. Specifically, we first derive a quasi-steady-state model of a racing car, whereby the racing line trajectory is jointly optimized. Next, we frame the minimum-lap-time problem and leverage its mostly convex structure by devising a sequential convex programming solution algorithm. We benchmark our method against off-the-shelf nonlinear programming solvers, showing how it can bring computation time down from a few minutes to a few seconds, paving the way for real-time implementations. Moreover, we compare our results to similarly efficient minimum-curvature racing line optimization methods, showing how a minimum-time-based racing line might lead to 4% faster lap-times. Finally, we showcase our framework for optimal powertrain energy management and we validate the common modeling assumption that the racing line is unaffected by energy limitations, showing that this assumption results in marginal lap-time losses of under 0.1%.