Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

International audienceThe Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) isthe key part to enhance optimal power distribution. Indeed, the most recent works are focusing onoptimizing hydrogen consumption, without taking into consideration the degradation of embeddedenergy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize thebattery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMSis normalized in order to address its multi-objective optimization. Then, weighting factors areassigned in the objective function to minimize the selected criteria. Compared to most EMSs basedon optimization techniques, this proposed approach does not require any information about thespeed profile, which allows it to be used for real-time control of FCHEV. The achieved simulationresults show that the proposed approach reduces the economic cost up to 50% for some speed profile,keeping the battery pack in a safe range and significantly reducing energy sources degradation. Theproposed health-conscious EMS has been validated experimentally and its online operation abilityclearly highlighted on a PEMFC delivery postal vehicl