A Convex Optimization Framework for Minimum Lap Time Design and Control of Electric Race Cars

Lap time is the most relevant performance indicator in electric racing. In order to minimize it, it important carefully design powertrain of race car terms sizing and technology, meticulously administer energy available on-board. To facilitate efficient design, this paper presents a convex optimization framework rapidly compute minimum-lap-time control strategies for battery car. Specifically, we first identify model powertrain, including battery, motor, two transmission technologies: fixed-gear (FGT) continuously variable (CVT). Second, assuming an expert driver, formulate problem fashion given driving path, its globally optimal solution with second-order conic programming algorithms. Third, showcase our on Le Mans track by comparing achievable FGT CVT, validate nonlinear simulations. Our results show that setup CVT can balance lower efficiency higher weight higher-efficiency more aggressive motor operation, significantly outperforming lap FGT. Finally, leverage computational carry out parameter studies components, revealing optimizing size specific scenario considerably improve time, best strongly depends decisions.