Stochastic optimal control for the energy management of hybrid electric vehicles under traffic constraints.

Summary This thesis deals with the design of an Energy Management System (EMS), taking into account the traffic constraints, for a hybrid electric vehicle. Currently, EMSs are usually classified into two categories: those proposing a real-time architecture seeking a local optimum, and those seeking a global optimum, which is more costly in terms of computation time and therefore more appropriate for offline use. This thesis is based on the fact that energy consumption can be accurately modeled using probability distributions on speed and acceleration. In order to reduce the size of the data, a classification is proposed, based on the Wasserstein distance, where the barycenters of the classes can be computed using Sinkhorn iterations or the Alternate Stochastic Gradient method. This traffic modeling allowed an offline optimization to determine the optimal control (the torque of the electric motor) that minimizes the fuel consumption of the hybrid vehicle on a road segment. Continuing on, a two-level algorithm took advantage of this information to optimize fuel consumption over the entire route. The upper level of optimization, being deterministic, is fast enough for a real time implementation. The relevance of the traffic model and the bi-level method is illustrated using traffic data generated by a simulator, but also using real data collected near Lyon (France). Finally, an extension of the bi-level method to the eco-routing problem is considered, using an augmented graph to determine the load state during the optimal path.