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• W2895865051 startingPage "e0205212" @default.
• W2895865051 abstract "Additional degrees of freedom existed in dual-motor coupling system bring considerable challenge to the optimal control of electric vehicles. Moreover, the stochastic characteristic of vehicle mass can further increase this challenge. A receding horizon control (RHC) strategy in consideration of stochastic vehicle mass is proposed in this study to respond to this challenge. Aiming at an electric vehicle with dual-motor coupling, a Markov chain is firstly deployed to predict future driving conditions by a formulated state transition probability matrix, based on historical driving cycles in real-world. Then, future required power is predicted by the predicted driving conditions, stochastic vehicle mass and road gradient, where the stochastic vehicle mass is formulated as stochastic variables in different bus stops. Finally, dynamic programming is employed to calculate the optimal vector of the vehicle within the defined prediction horizon, and only the first control values extracted from the optimal control vector are used to execute real-time power distribution control. The simulation results show that the proposed strategy is reasonable and can at least reduce electric consumption by 4.64%, compared with rule-based strategy." @default.
• W2895865051 created "2018-10-26" @default.
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• W2895865051 date "2018-10-11" @default.
• W2895865051 modified "2023-09-24" @default.
• W2895865051 title "Receding horizon control strategy for an electric vehicle with dual-motor coupling system in consideration of stochastic vehicle mass" @default.
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• W2895865051 doi "https://doi.org/10.1371/journal.pone.0205212" @default.
• W2895865051 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6181328" @default.
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• W2895865051 hasPublicationYear "2018" @default.
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