A model predictive controller for non-cooperative eco-platooning

Turri, Valerio; Kim, Yeojun; Guanetti, Jacopo; Johansson, Karl Henrik; Borrelli, Francesco

doi:10.23919/acc.2017.7963297

Cited by 49 publications

(32 citation statements)

References 10 publications

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“…[8] can be replaced by a quadratic programming solver that can be implemented online; or the nonlinear function in the formulation used in Ref. [9] can be replaced by a linear function to reduce the computational burden. This method would significantly simplify the optimal control problem in Ref.…”

Section: Discussionmentioning

confidence: 99%

“…Another approach is tractive energy minimization; here, the penalized variable is tractive or propulsion energy demand at the wheels, and hence is more closely related to fuel consumption. Such an approach should in theory reduce fuel consumption to beyond acceleration optimization [7][8][9]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…It is common practice in the literature to assume that a reduction in propulsion power or engine torque would yield a reduction in fuel consumption [9,11,12]. Translating vehicle power demand to an engine power demand is difficult for both offline and online optimization as complex powertrain dynamics cannot be accurately modeled without using a high-fidelity software, which, in turn, increases computational time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

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With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

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show abstract

“…An important role is also played by the inter-vehicular spacing policy: most systems adopt a constant distance policy or a constant heading time policy [121,124,125], as we discuss further in the next paragraph. While guaranteeing safety, various performance objectives can be pursued, such as road throughput [121], fuel economy [124,126], and driver comfort by mimicking her driving style [123].…”

Section: Literature Reviewmentioning

confidence: 99%

Control of connected and automated vehicles: State of the art and future challenges

Guanetti

Kim

Borrelli

2018

Annual Reviews in Control

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498

260

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Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.

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“…Both methods above rely on V2V and can thus not be utilized in this work. An MPC controller for longitudinal control of the follower truck for noncooperative platooning on flat road segments is presented in [13]. In the proposed framework, the future velocity profile of the preceding vehicle is assumed to be known or well approximated as constant within the prediction horizon.…”

Section: A Related Workmentioning

confidence: 99%

Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks

Ling

Lindsten

Ljungqvist

et al. 2018

2018 Annual American Control Conference (ACC)

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The demand for fuel-efficient transport solutions are steadily increasing with the goal of reducing environmental impact and increasing efficiency. Heavy-Duty Vehicle (HDV) platooning is a promising concept where multiple HDVs drive together in a convoy with small intervehicular spacing. By doing this, the aerodynamic drag is reduced which in turn lowers fuel consumption. We propose a novel Model Predictive Control (MPC) framework for longitudinal control of the follower vehicle in a platoon consisting of two HDVs when no vehicleto-vehicle communication is available. In the framework, the preceding vehicle's velocity profile is predicted using artificial neural networks which uses a topographic map of the road as input and is trained offline using synthetic data. The gear shifting and mass of consumed fuel for the controlled follower vehicle is modeled and used within the MPC controller. The efficiency of the proposed framework is verified in simulation examples and is benchmarked with a currently available control solution.

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A model predictive controller for non-cooperative eco-platooning

Cited by 49 publications

References 10 publications

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Control of connected and automated vehicles: State of the art and future challenges

Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks

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