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Gestion énergétique de véhicules hybrides par commande optimale stochastique

Abstract : This thesis presents a comparative study between four recent real-time energy management strategies (EMS) applied to a hybrid electric vehicle and to a fuel cell vehicle applications: rule-based strategy (RBS), adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL) and stochastic dynamic programming (SDP) associated to driving cycle modeling by Markov chains. Pontryagin’s minimum principle and dynamic programming are applied to off-line optimization to provide reference results. Implementation and parameters setting issues are discussed for each strategy and a genetic algorithm is employed for A-ECMS calibration.The EMS robustness is evaluated using different types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation and experimental results lead to the following conclusions. The easiest methods to implement (RBS and OCL) give rather high fuel consumption. SDP has the best overall performance in real-world driving conditions. It achieves the minimum average fuel consumption while perfectly respecting the state-sustaining constraint. A-ECMS results are comparable to SDP’s when using parameters well-adjusted to the upcoming driving cycle, but lacks robustness. Using parameter sets adjusted to the type of driving conditions (urban, road and highway) did help to improve A-ECMS performances.
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Submitted on : Tuesday, January 2, 2018 - 1:47:48 AM
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Qi Jiang. Gestion énergétique de véhicules hybrides par commande optimale stochastique. Autre. Université Paris Saclay (COmUE), 2017. Français. ⟨NNT : 2017SACLS011⟩. ⟨tel-01674207⟩

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