This paper presents the design of a control framework to co-optimize the velocity and power-split operation of a plug-in hybrid vehicle (PHEV) online in the presence of traffic constraints. The striking challenge in its online implementation lies in the conflict between the long control horizon required for global optimality and the limit in computational power. To resolve the conflict between the horizon length and the computation complexity, we adopt a receding horizon strategy where the co-states are used to approximate the future cost and thus shorten the prediction horizon. In particular, we update the co-state using a nominal trajectory and the temporal-difference (TD) error based on the co-state dynamics. Simulation results demonstrate 12% fuel economy benefits of the proposed co-optimization strategy upon the sequential/layered control strategy for the considered driving scenario. Moreover, its real-time implementability is evidenced by the computation time per model predictive controller (MPC) step of around 80-millisec on average with 10-sec prediction horizon.


A Receding-Horizon Framework to Co-Optimizing the Velocity and Power-Split of Automated Plug-in Hybrid Electric Vehicles

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

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