PROJECT TITLE :
Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles
Recent advances in traffic monitoring systems have made real-time traffic velocity information ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electrical vehicle (PHEV). Compared with conventional model predictive management (MPC), an additional supervisory state of charge (SoC) planning level is built based mostly on real-time traffic knowledge. A power balance-primarily based PHEV model is developed for this higher level to rapidly generate battery SoC trajectories that are used as final-state constraints within the MPC level. This PHEV energy management framework is evaluated under three completely different situations: one) while not traffic flow information; a pair of) with static traffic flow info; and three) with dynamic traffic flow info. Numerical results using real-world traffic information illustrate that the proposed strategy successfully incorporates dynamic traffic flow knowledge into the PHEV energy management algorithm to achieve enhanced fuel economy.
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