Liu, XuzeFotouhi, AbbasAuger, Daniel J.2022-03-082022-03-082022-02-25Liu X, Fotouhi A, Auger D. (2022) Application of advanced tree search and proximal policy optimization on formula-E race strategy development, Expert Systems with Applications, Volume 197, July 2022, Article number 1167180957-4174https://doi.org/10.1016/j.eswa.2022.116718https://dspace.lib.cranfield.ac.uk/handle/1826/17632Energy and thermal management is a crucial element in Formula-E race strategy development. Most published literature focuses on the optimal management strategy for a single lap and results in sub-optimal solutions to the larger multi-lap problem. In this study, two Monte Carlo Tree Search (MCTS) enhancement techniques are proposed for multi-lap Formula-E racing strategy development. It is shown that using the bivariate Gaussian distribution enhancement, race finishing time improves by at least 0.25% and its variance reduces by more than 26%. Compared to the published conventional MCTS technique used in multi-lap problems, this proposed technique is proved to bring a remarkable enhancement with no additional computational time cost. By further enhancing the MCTS using proximal policy optimization, the final product is capable of generating more than 0.5% quicker race time solutions and improving the consistency by over 90% which makes it a very suitable method particularly when enough training time is guaranteedenAttribution-NonCommercial-NoDerivatives 4.0 InternationalEnergy managementFormula-E race strategyMonte Carlo Tree searchProximal policy optimizationArticle