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Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles

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Document pages: 11 pages

Abstract: To improve the driving mobility and energy efficiency of connected autonomouselectrified vehicles, this paper presents an integrated longitudinal speeddecision-making and energy efficiency control strategy. The proposed approachis a hierarchical control architecture, which is assumed to consist ofhigher-level and lower-level controls. As the core of this study, modelpredictive control and reinforcement learning are combined to improve thepowertrain mobility and fuel economy for a group of automated vehicles. Thehigher-level exploits the signal phase and timing and state information ofconnected autonomous vehicles via vehicle to infrastructure and vehicle tovehicle communication to reduce stopping at red lights. The higher-leveloutputs the optimal vehicle velocity using model predictive control techniqueand receives the power split control from the lower-level con-troller. Thesetwo levels communicate with each other via a controller area network in thereal vehicle. The lower-level utilizes a model-free reinforcement learningmethod to improve the fuel economy for each connected autonomous vehicle.Numerical tests illustrate that vehicle mobility can be noticeably improved(traveling time reduced by 30 ) by reducing red-light idling. The effectivenessand performance of the proposed method are validated via comparison analysisamong different energy efficiency controls (fuel economy promoted by 13 ).

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