A Bilevel Energy Management Strategy for HEVs Under Probabilistic Traffic Conditions

This work proposes a new approach for the optimal energy management of a hybrid electric vehicle (EV) considering traffic conditions. The method is based on a bilevel decomposition. At the microscopic level, the offline part computes cost maps due to a stochastic optimization that considers the influence of traffic, in terms of speed/acceleration probability distributions. At the online macroscopic level, a deterministic optimization computes the ideal state of charge at the end of each road segment using the computed cost maps. The optimal torque split can then be recovered according to the cost maps and this SoC target sequence. Since the high computational cost due to the uncertainty of traffic conditions has been managed offline, the online part should be fast enough for real-time implementation on board the vehicle. Errors due to discretization and computation in the proposed algorithm have been studied. Finally, we present numerical simulations using actual traffic data and compare the proposed bilevel method to the best possible consumption, obtained by a deterministic optimization with full knowledge of future traffic conditions, as well as to an established solution for energy management of a hybrid EV. The solutions show a reasonable overconsumption compared with deterministic optimization and manageable computational times for both the offline and the online part.