This paper presents an economic nonlinear hybrid model predictive control strategy for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles are controlled for operation in various driveline modes and the associated optimal control problem involves both continuous and discrete control variables. To solve the resultant mixed-integer nonlinear optimal control problem, we propose a hierarchical supervisory control architecture that consists of demand prediction, driveline mode determination, and real-time optimization. These three modules are designed independently and connected in series to perform computer-aided control. The demand prediction module uses a times series model to forecast the mechanical traction power requests of the driver over a prediction horizon based on vehicle speed, road grade, acceleration pedal scale, brake pedal scale, and past and current power demands. For a given forecasted power demand profile, the mode determination module decides a sequence of driveline modes that are presumed to be operated over the prediction horizon. The model-based real-time optimization corresponding to nonlinear model predictive control computes the optimal motor power over a prediction horizon, and the receding horizon scheme as feedback control is applied to repeat the processes of the three control modules. A dedicated case study with real driving data obtained from Hyundai IONIQ PHEV 2018 is presented to demonstrate the effectiveness in fuel economy and emission reduction offered by the proposed optimal energy management strategy. The proposed hierarchical real-time predictive optimizationbased strategy is competitive with any exiting power management strategies such as dynamic programming and equivalent consumption minimization strategy in fuel economy and emission reduction while showing better charge-sustaining capability. This trade-off between fuel economy and charge-sustainability can be further improved by tuning the hyper-parameters in the proposed optimal control problem.
This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The design parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The time-varying bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicleto-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.INDEX TERMS Eco-driving, Optimal speed planning, Optimal control, Dynamic programming, Energyefficient regenerative braking, Electrified vehicles, Connected and autonomous vehicles.
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