Health-Conscious Energy Management for Fuel Cell Hybrid Electric Vehicles Based on Prognostics-Enabled Decision-Making

• Published in: IEEE transactions on vehicular technology Vol. 68; no. 12; pp. 11483 - 11491
• Main Authors: Yue, Meiling, Jemei, Samir, Zerhouni, Noureddine
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Automatic; Batteries; Computer Science; Computer simulation; Data fusion; Data integration; Data Structures and Algorithms; Decision making; Degradation; Diagnostic systems; Economic impact; Electric power; Electric vehicles; Energy management; Engineering Sciences; Fluid mechanics; Fuel cells; Fuzzy control; Fuzzy logic; fuzzy logic controller; Genetic algorithms; Health; health-conscious energy management; hybrid electric vehicle; Hybrid electric vehicles; Mathematical analysis; Mechanics; Multisensor fusion; Physics; Prognostics and health management; prognostics-enabled decision making; State of charge; Thermics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2937130

In this paper, a health-conscious energy management strategy based on prognostics-enable decision-making in the framework of prognostics and health management has been proposed for the studied fuel cell hybrid electric vehicle. Fuzzy logic controllers have been optimized by off-line genetic algorithm under different degradation states and corresponding confidence factors have been allocated to them according to the on-line health state classification results. Then, the parameters of the off-line tuned fuzzy logic controllers are fused together based on Dempster-Shafer data fusion theory in order to calculate the on-line used fuzzy rules. Simulation results have showed that the proposed approach can mitigate the power source degradation and save the economic cost.