A Real-Time Nonlinear CRPE Predictive PHEV Energy Management System Design and HIL Evaluation

The explicit nonlinear model predictive control (eNMPC) approach is a challenging yet encouraging prospective to design model-based and real-time controllers for complicated automotive applications. The precision of the nonlinear control-oriented model may contribute to the superior performance of the controller but adversely affect the real-time capability and even the convergence of optimization solution involved in such an approach. To address this challenge, control-relevant parameter estimation (CRPE) is used to ameliorate the capability of the nonlinear control-oriented model to exploit the cardinal dynamics of a complex plug-in hybrid powertrain. As such, an eNMPC energy management system is developed to be implemented in to an experimental hardware with fettered memory and computational capabilities. Hardware-in-the-Loop (HIL) simulation, which is conducted on a dSPACE MicroAutoBox unit along with sophisticated high-fidelity Autonomie model shows a superior performance of the proposed control scheme for various driving scenarios while maintaining real-time capability.

Automated summary

The explicit nonlinear model predictive control (eNMPC) approach is proposed as a challenging yet promising method for designing real-time controllers for complex automotive applications. By using control-relevant parameter estimation (CRPE), the model's capability is enhanced to improve system performance with limited memory and computational resources.