Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 4, Pages 2388-2399Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3003554
Keywords
Decision making; driving pattern recognition; energy management; fuel cell hybrid electric vehicle (FCHEV); price evolution; prognostics
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The article introduces a cost minimization strategy for fuel cell hybrid electric vehicles, incorporating a decision-making framework and an online applicable approach. By combining driving pattern recognition, prognostics, and price prediction enabled decision making, the strategy aims to optimize overall costs and battery charge sustaining. The effects of driving cycles, degradation states, and pricing scenarios are also analyzed to improve cost efficiency.
The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. In this article, a road, health, and price-conscious optimal cost minimization strategy based on a decision-making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of the vehicles, including the hydrogen cost and degradation costs of the fuel cell and battery. Second, a decision-making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate the health states of power sources, and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach the optimal results on the overall cost and charge sustaining of a battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.
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