Data-Driven Robust Optimization for Energy-Aware Safe Motion Planning of Electric Vehicles

In this paper, we simultaneously address the problems of energy optimal and safe motion planning of electric vehicles (EVs) in a data-driven robust optimization framework. Safe maneuvers, especially in urban traffic, are characterized by frequent lateral motions, such as lane changes, overtakes and turning along curved roads. Motivated by our previous work which shows a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\!-\!10 \%$</tex-math></inline-formula> increase in energy consumption due to lateral motion when an electric vehicle changes its lane once every kilometer while following standard drive cycles, we incorporate vehicle lateral dynamics in the modeling and control synthesis, which is in contrast with most prior works. In the context of safety, we leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we formulate a finite-horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption, and solve it in a receding horizon fashion. Finally, we show the effectiveness of the proposed approach in reducing energy consumption and collision avoidance via numerical simulations involving curved roads and multiple obstacles. A detailed analysis of energy consumption along different components of EV motion highlights appreciable improvement under the proposed approach.