Model predictive control‐based eco‐driving strategy for CAV

Wang, Hongliang; Peng, P; Huang, Yanjun

doi:10.1049/iet-its.2018.5336

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…The concept of virtual vehicle ranking by Uno et al is used to map the virtual vehicle into the main platooning before the actual merges [9]. Researchers find that driving strategies can be used to improve fuel efficiency in CAVs in different scenarios [10], which further explains the importance of improving the efficiency of CAVs.…”

Section: Related Workmentioning

confidence: 99%

DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

Jin¹,

Zhu²

2022

Preprint

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Researches have been devoted to making connected and automated vehicles (CAVs) faster in different traffic scenarios. By using C-V2X or DSRC communication protocol, CAVs can work more effectively. In this paper, we compare these two communication protocols on CAVs in three different traffic scenarios including ramp merging, intersection, and platoon brake. It shows there is a trade-off between communication range and interval when leveraging C-V2X or DSRC for CAVs. The result can help support further application designs for CAV autonomously choosing communication protocols in different traffic scenarios.

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Section: Related Workmentioning

confidence: 99%

DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

Jin¹,

Zhu²

2022

Preprint

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“…An EACC design which can be applied into online control is offered. 87 MPC problem is established based on the polynomial fuel consumption model obtained by Willans linear approximation and the linear vehicle dynamics model gained by Taylor formula. The optimal torque and shift law are obtained by the SQP.…”

Section: Eaccmentioning

confidence: 99%

A review of the development trend of adaptive cruise control for ecological driving

Pan

Huang

Chen

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Adaptive cruise control (ACC) is an important part of the advanced driver assistance system (ADAS), which aims to improve the user’s driving experience by reducing their manual operation and optimizing the vehicles’ performance such as economy, comfort, and safety. With the gradual maturity of research on ACC, it presents various development trends. Among them, ecological adaptive cruise control (EACC) has great development potential in reducing emissions and improving driving range in internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and electric vehicles (EVs). This paper firstly reviews the components of classic ACC. Then the main design methods, research hotspots, and development trends of EACC for HEVs and EVs are introduced in detail. This paper not only provides a comprehensive analysis of EACC design and application in different type of vehicles but also forecasts the future research direction and trend, expecting to promote the further development of EACC efficiently.

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“…The algorithm allows the driver to choose an arbitrary steady state velocity for a flat road, but modifies the speed for optimal fuel consumption on various grades. Recently the idea was further developed into the eco-driving methods [26], which adapt the vehicle speed according to road grade angle and hence significantly reduce fuel consumption without decreasing average speed. An EV equipped with Eco-driving shows a reduction of 14% in energy consumption [27].…”

Section: Introductionmentioning

confidence: 99%

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

Liu

Feng

2021

IET Intelligent Trans Sys

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Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Model predictive control‐based eco‐driving strategy for CAV

Cited by 16 publications

References 27 publications

DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

A review of the development trend of adaptive cruise control for ecological driving

Optimal road grade design based on stochastic speed trajectories for minimising transportation energy consumption

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