Novel enhancement of energy management in fuel cell hybrid electric vehicle by an advanced dynamic model predictive control

In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with a multi-objective cost function driven by dynamic weights is proposed to improve the energy performance of fuel cell hybrid electric vehicles whilst prolonging their component lifetime. By the use of dynamic weights, the cost function is effectively formulated as the combination of fuel consumption, rate of change of fuel cell power, battery power, the fuel cell efficiency, state of charge of the battery, and their temperatures. In order to enhance the adaptability of the AMPC, a Fuzzy Cognitive Map (FCM) is then newly designed to regulate online the dynamic weights to adjust the importance of each cost component according to the conditions prevailing during driving. A comparative study between the proposed AMPC, a constant weight based NMPC and a conventional NMPC having cost function with fewer objectives has been carried out by means of simulation using a FCHEV model from the simulation tool ADVISOR to illustrate the efficacy of the proposed AMPC.

Automated summary

In this paper, an Advanced Dynamic Model Predictive Control (AMPC) based on a Nonlinear Model Predictive Control (NMPC) framework with dynamic weights is proposed to improve the energy performance and prolong the component lifetime of fuel cell hybrid electric vehicles. By utilizing dynamic weights and a Fuzzy Cognitive Map (FCM), the cost function of the AMPC is effectively formulated to adjust the importance of each cost component according to driving conditions. Simulation results using a FCHEV model demonstrate the efficacy of the proposed AMPC.