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,
, 114 4.23 Comparison results between baseline EMS and health-conscious EMS : (a) Fuel cell power comparison ; (b) Battery SOC comparison ; (c) Zooming part of the comparison of fuel cell power when prognostics happens
, 119 4.25 Degradation comparison between baseline EMS and the proposed healthconscious EMS : (a) Comparison of fuel cell degradation (fuel cell replaced) ; (b) Comparison of battery degradation
, Evolution of the measured fuel cell stack voltage, p.120
, Evolution of the measured battery capacity
, PHM cycle realised in an early stage
, Prognostics framework based on a deep learning architecture, p.130
, Health state classification based on RUL uncertainty, p.131
, Expected field test scenarios
, 5 Illustration of short/long-term decision policies [151], p.132
, Fuzzy logic controller results after GA optimization (degradation case 2) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison ; (e) Battery degradation comparison
, Fuzzy logic controller results after GA optimization (degradation case 3) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison ; (e) Battery degradation comparison
, Fuzzy logic controller results after GA optimization (degradation case 4) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison ; (e) Battery degradation comparison
, Fuzzy logic controller results after GA optimization (degradation case 5) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison ; (e) Battery degradation comparison
, Simulation results with ?D f c = 0.5 : (a) Power distribution ; (b) Battery SOC evolution ; (c) Fuel cell degradation evolution ; (d) Battery degradation revolution
, Simulation results with ?D f c = 0.4 : (a) Power distribution ; (b) Battery SOC evolution ; (c) Fuel cell degradation evolution ; (d) Battery degradation revolution
, Simulation results with ?D f c = 0.3 : (a) Power distribution ; (b) Battery SOC evolution ; (c) Fuel cell degradation evolution ; (d) Battery degradation revolution
, Simulation results with ?D f c = 0.2 : (a) Power distribution ; (b) Battery SOC evolution ; (c) Fuel cell degradation evolution ; (d) Battery degradation revolution
, Power distribution ; (b) Battery SOC evolution ; (c) Fuel cell degradation evolution ; (d) Battery degradation revolution, Simulation results with ?D f c = 0.1 : (a)
, Summary of various battery degradation modelling methods, p.42
, Major failure modes of different components in the PEM fuel cell (Source [33, 152])
, Summary of various fuel cell degradation modelling methods, p.50
, Physical parameters controlled by the test bench
, Evaluation results of fuel cell prognostics (10-hours time scale), p.80
, ? ? accuracy of fuel cell prognostics results (10-hours time scale), p.80
, Evaluation results of battery prognostics (10-hours time scale), vol.83
, ? ? accuracy of battery prognostics (10-hours time scale), vol.84
, Evaluation results of fuel cell prognostics (1-hour time scale), p.85
, ? ? accuracy of fuel cell prognostics (1-hour time scale), p.85
, Evaluation results of fuel cell prognostics (20-hours time scale), p.85
, ? ? accuracy of fuel cell prognostics (20-hours time scale), p.86
,
, Electric motor specifications
, Parameters of the developed battery model
, Smoothing inductor and boost chopper specifications, p.97
, Fuel cell specifications
,
, Controller area of output Ifc
, Five degradation cases of the hybrid system
, 112 4.10 Refined confidence factors of "I f c low " MFs
, Refined confidence factors of "I f c med " MFs
, Refined confidence factors of "I f c high, vol.116, p.153
, 122 4.15 Comparison between the existing real-time optimization-based healthconscious EMSs and the proposed prognostics-based health-conscious EMSs
, GA OPTIMIZATION RESULTS UNDER DIFFERENT DEGRADATION STATES FIGURE A.1 -Fuzzy logic controller results after GA optimization (degradation case 2) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison
, 2 -Fuzzy logic controller results after GA optimization (degradation case 3) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison
, 3 -Fuzzy logic controller results after GA optimization (degradation case 4) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison
, 4 -Fuzzy logic controller results after GA optimization (degradation case 5) : (a) Power distribution ; (b) Fuel cell power comparison ; (c) Battery SOC comparison ; (d) Fuel cell degradation comparison
, Energy management strategy based on prognostics-enabled decision-making for fuel cell hybrid electric vehicles, 2019.
, Systèmes et Pilesà Combustible, 2019.
, Developing a Health-Conscious Energy Management Strategy based on Prognostics for a Fuel cell Hybrid Electric Vehicle, 8th International Conference on Fundamentals & Development of Fuel Cells (FDFC), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02130789
, Developing a Health-Conscious Energy Management Strategy Based on Prognostics for a Battery/Fuel Cell Hybrid Electric Vehicle, 2018 IEEE Vehicle Power and Propulsion Conference (VPPC), pp.1-6, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02130789
, Developing a Prognostics-based Energy Management Strategy for Battery/Fuel cell Hybrid Electric Vehicles, 2018.
, Systèmes et Pilesà Combustible, 2018.
, Towards the Energy Management of a Fuel Cell/Battery Vehicle Considering Degradation, 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), pp.1-6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02392529
, le style L AT E X pour Thèse de Doctorat créé par S. Galland