A Bilevel Energy Management Strategy for HEVs Under Probabilistic Traffic Conditions

• Published in: IEEE transactions on control systems technology Vol. 30; no. 2; pp. 728 - 739
• Main Authors: Le Rhun, Arthur, Bonnans, Frederic, De Nunzio, Giovanni, Leroy, Thomas, Martinon, Pierre
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Acceleration; Algorithms; Batteries; Bilevel optimization; Computing costs; Driving conditions; Electric motors; Electric power; Energy management; Engineering Sciences; Engines; Hybrid electric vehicles; hybrid electric vehicles (EVs); Mechanics; Optimization; State of charge; Statistical analysis; stochastic dynamic programming (SDP); Stochastic processes; Torque; Traffic; traffic data clustering; Traffic information; Traffic management; Traffic speed; Torque; Stochastic processes; bi-level optimization; traffic data clustering; Batteries; Hybrid electric vehicles; Energy management; Engines; Optimization; stochastic dynamic programming; Electric motors
• ISSN: 1063-6536; 1558-0865; 2374-0159; 1558-0865
• DOI: 10.1109/TCST.2021.3073607

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.