Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

• Published in: IEEE transactions on control systems technology Vol. 23; no. 3; pp. 1075 - 1086
• Main Authors: Chao Sun, Moura, Scott Jason, Xiaosong Hu, Hedrick, J. Karl, Fengchun Sun
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Batteries; Energy management; Engines; Fuel economy; plug-in hybrid electric vehicle (PHEV); power balance model; Real-time systems; supervised energy management; System-on-chip; traffic velocity; Trajectory; Vehicles; plug-in hybrid electric vehicle (PHEV); Fuel economy; traffic velocity; supervised energy management; power balance model
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2014.2361294

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.