  This paper presents model predictive control strategies for battery electric
endurance race cars accounting for interactions with the competitors. In
particular, we devise an optimization framework capturing the impact of the
actions of the ego vehicle when interacting with competitors in a probabilistic
fashion, jointly accounting for the optimal pit stop decision making, the
charge times and the driving style in the course of the race. We showcase our
method for a simulated 1h endurance race at the Zandvoort circuit, using
real-life data of internal combustion engine race cars from a previous event.
Our results show that optimizing both the race strategy as well as the decision
making during the race is very important, resulting in a significant 21s
advantage over an always overtake approach, whilst revealing the
competitiveness of e-race cars w.r.t. conventional ones.
