Journal
JOURNAL OF CLEANER PRODUCTION
Volume 260, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.121017
Keywords
Fuel cell hybrid electric vehicle; Energy management strategy; Hierarchical clustering; Rule learning
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Funding
- National Natural Science Foundation of China [51775063, 61763021]
- Fundamental Research Funds for the Central Universities [2018CDQYQC0035]
- National Key R&D Program of China [2018YFB0105402]
- EU [845102-HOEMEV-H2020-MSCA-IF-2018]
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In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived offline by the Pontryagin's minimum principle. Next, the K-means algorithm is employed to hierarchically cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can achieve more than 95% savings of energy consumption economy, solved by Pontryagin's minimum principle, with less calculation intensity and without dependence on prior driving conditions, thereby manifesting the feasibility of online application. (C) 2020 Elsevier Ltd. All rights reserved.
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