Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication

Ma, Fangwu; Yang, Yu; Wang, Jiawei; Liu, Zhenze; Li, Jinhang; Nie, Jiahong; Shen, Ya‐Ching; Wu, Liang

doi:10.1016/j.energy.2019.116120

Cited by 81 publications

(27 citation statements)

References 38 publications

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“…The use of autonomous vehicles can offer economic, social, and environmental benefits (Lim and Taeihagh 2019). In particular, autonomous vehicles connected with Vehicle-to-Vehicle communication as basic technology exhibit an energy-saving potential higher than other systems (Ma et al 2019).…”

Section: Results From Camentioning

confidence: 99%

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

Francini

Chieffallo

Palermo

et al. 2021

Journal of Planning Literature

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This work aims to reorganize theoretical and empirical research on smart mobility through the systematic literature review approach. The research goal is to reach an extended and shared definition of smart mobility using the cluster analysis. The article provides a summary of the state of the art that can have broader impacts in determining new angles for approaching research. In particular, the results will be a reference for future quantitative developments for the authors who are working on the construction of a territorial measurement model of the smartness degree, helping them in identifying performance indicators consistent with the definition proposed.

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Section: Results From Camentioning

confidence: 99%

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

Francini

Chieffallo

Palermo

et al. 2021

Journal of Planning Literature

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“…Prior studies including modeling powertrain for control of CAVs [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Figurementioning

confidence: 99%

“…In [11], the authors used a simplified EV model to explore the effect of different control strategies on the energy consumption. In [18], the authors simulate rear wheel CEVs driven by two permanent magnet synchronous in-wheel motors (PMSM), and reported energy savings up to 6% for Urban Dynamometer Driving Schedule (UDDS) obtained through proposed control algorithms. In [19], a look-ahead model predictive controller (LA-MPC) is designed that calculates the required motor torque demand to meet the dual objectives of increased traction, and anti-jerk control of a CEV.…”

Section: Figurementioning

confidence: 99%

Essential Dynamics for Developing Models for Control of Connected and Automated Electrified Vehicles: Part A - Powertrain

Hemmati

Yadav

Surresh

et al. 2021

Preprint

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Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Even though the CAV technology is applicable to both conventional and electrified powertrains, the energy saving opportunities are more apparent when the CAVs are Hybrid Electric Vehicles (HEVs). This is because of the flexibility in the vehicle powertrain and possibility of choosing optimum powertrain modes based on the predicted traction power needs. In this paper, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption.

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“…There is no doubt that it indirectly reduces the fuel consumption and emissions during vehicles frequently stop and go [137]. Literature [138] proposes an ecological collaborative ACC prediction optimisation strategy based on non‐linear MPC. The simulation result shows that the strategy has good string stability, excellent tracking performance, and significant energy saving potential.…”

Section: Emss For Hev/phev Under Itsmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

187

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Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication

Cited by 81 publications

References 38 publications

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

Essential Dynamics for Developing Models for Control of Connected and Automated Electrified Vehicles: Part A - Powertrain

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

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