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A bi-level energy management strategy for HEVs under probabilistic traffic conditions

Abstract : This work proposes a new approach to optimize the consumption of a hybrid electric vehicle taking into account the traffic conditions. The method is based on a bi-level decomposition in order to make the implementation suitable for online use. The offline lower level computes cost maps thanks to a stochastic optimization that considers the influence of traffic, in terms of speed/acceleration probability distributions. At the online upper level, a deterministic optimization computes the ideal state of charge at the end of each road segment, using the computed cost maps. Since the high computational cost due to the uncertainty of traffic conditions has been managed at the lower level, the upper level is fast enough to be used online in the vehicle. Errors due to discretization and computation in the proposed algorithm have been studied. Finally, we present numerical simulations using actual traffic data, and compare the proposed bi-level method to a deterministic optimization with perfect information about traffic conditions. The solutions show a reasonable over-consumption compared with deterministic optimization, and manageable computational times for both the offline and online parts.
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Contributor : Pierre Martinon <>
Submitted on : Thursday, September 10, 2020 - 5:05:31 PM
Last modification on : Wednesday, June 2, 2021 - 4:27:03 PM


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  • HAL Id : hal-02278359, version 2


Arthur Le Rhun, Frédéric Bonnans, Giovanni de Nunzio, Thomas Leroy, Pierre Martinon. A bi-level energy management strategy for HEVs under probabilistic traffic conditions. 2020. ⟨hal-02278359v2⟩



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