Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles

The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradien...

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Vydáno v:	Applied energy Ročník 376; s. 124198
Hlavní autoři:	Lin, Xinyou, Zhou, Qiang, Tu, Jiayi, Xu, Xinhao, Xie, Liping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.12.2024
Témata:	algorithms Battery life degradation energy Energy management strategy Fuel cell hybrid electric vehicle fuel cells Gradient drop power strategy hydrogen hydrogen fuels Markov prediction prediction Energy management strategy Fuel cell hybrid electric vehicle Battery life degradation Gradient drop power strategy Markov prediction
ISSN:	0306-2619
On-line přístup:	Získat plný text
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Shrnutí:	The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy. •The fuel cell gradient drop power strategy is formulated to suppress power transients.•A self-learning Markov predictor for real-time prediction is proposed.•Balancing the multi-objective cost function to minimize the global cost.•The numerical validations and HIL are conducted to validate the proposed strategy.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0306-2619
DOI:	10.1016/j.apenergy.2024.124198