Abstract
This article 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 optimization-based 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.
Highlights
The associate editor coordinating the review of this manuscript and approving it for publication was Ton Duc Do
The nonlinear and constrained optimal control problem of power-split HEV is solved by using nonlinear model predictive control (MPC) with short prediction length, which improves fuel economy and enables online calculation [27]
If the power required to be generated by the engine is determined when the demand power is given, the engine operates according to the engine optimal operating line based on the brake specific fuel consumption (BSFC) data
Summary
Optimization problem into a sequence of subproblems [12]. In recent years, as environmental pollution draws atten- In this process, the value function, called optimal cost-totion of people across the world and related policies are go, is calculated offline via backward induction; the curse strengthened, regulations on exhaust gases of automobiles of dimensionality is among its disadvantages, wherein the are being reinforced. The nonlinear and constrained optimal control problem of power-split HEV is solved by using nonlinear MPC with short prediction length, which improves fuel economy and enables online calculation [27]. In a hardware-in-the-loop experiment, they show that compared to a conservative non-adaptive strategy to meet the emission regulation, their ECMS-based method results in a 7% improvement in fuel consumption. Another approach to minimize the drivetrain cost, fuel consumption, and exhaust emissions simultaneously is proposed in [37] that uses a multi-objective particle swarm optimization technique.
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