Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.