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- W4384162613 abstract "An effective energy management strategy (EMS) is essential to ensure the safe and efficient operation of the fuel cell hybrid vehicles. In this paper, an online adaptive EMS is proposed for the fuel cell hybrid vehicles to minimize hydrogen consumption and adjust the strategy according to the driving conditions. Driving pattern recognition is realized by the improved k-means cluster approach which combines multiple k-means clusters with specific distances to serve as the classifier. In each driving pattern, separate machine learning (ML) models are trained to obtain the energy management regression learner. Comparison experiments are performed to determine the optimal ML model and input parameters. The effectiveness of the proposed EMS is evaluated using two compound test driving cycles. Results show that the proposed method achieves the lowest fuel consumption compared to the other five algorithms considered. Remarkably, it reduces hydrogen consumption by up to 5.66% when compared to commonly used methods." @default.
- W4384162613 created "2023-07-14" @default.
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- W4384162613 date "2023-09-01" @default.
- W4384162613 modified "2023-09-24" @default.
- W4384162613 title "Online adaptive energy management strategy for fuel cell hybrid vehicles based on improved cluster and regression learner" @default.
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- W4384162613 doi "https://doi.org/10.1016/j.enconman.2023.117388" @default.
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