Formula-E race strategy development using artificial neural networks and Monte Carlo Tree Search

Energy management has been one of the most important parts in electric race strategies since the Fédération Internationale de l'Automobile (FIA) Formula-E championships was launched in 2014. Since that time, a number of unfavorable race finishes have been witnessed due to poor energy management. Previous researches have been focused on managing the power flow between different energy sources or different energy consumers based on a fixed cycle. However, there is no published work in the literature about energy management of a full electric racing car on repeated course but with changeable settings and driving styles. Different from traditional energy management problems, the electric race strategy is more of a multi-stage decision making problem which has a very large scale. Meanwhile, this is a time-critical task in motorsport where fast prediction tools are needed and decisions have to be made in seconds to benefit the final outcome of the race. In this study, the use of Artificial Neural Networks (ANN) and tree search techniques are investigated as an approach to solve such a large-scale problem. ANN prediction models are developed to replace the traditional lap time simulation as a much faster performance prediction tool. Implementation of Monte Carlo Tree Search (MCTS) based on the proposed ANN fast prediction models has provided decent capability to generate decision-making solution for both pre-race planning and in-race reaction to unexpected scenarios.