MPC based vehicular following control considering both fuel economy and tracking capability

Li, Shengbo; Li, Keqiang; Wang, Jianqiang; Zhang, Lei; Lian, Xiaojuan; Ukawa, Hiroshi; Bai, Dongsheng

doi:10.1109/vppc.2008.4677689

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The authors in [7] compared a linear quadratic and a Model Predictive Control (MPC) following algorithm, showing a fuel consumption decrease of 8.8% if a 5s look-ahead capability is guaranteed. However, the authors employed time-invariant constraints on position.…”

Section: Introductionmentioning

confidence: 99%

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

Prakash

Cimini

Stefanopoulou

et al. 2016

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Com

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Constrained optimization control techniques with preview are designed in this paper to derive optimal velocity trajectories in longitudinal vehicle following mode, while ensuring that the gap from the lead vehicle is both safe and short enough to prevent cut-ins from other lanes. The lead vehicle associated with the Federal Test Procedures (FTP) [1] is used as an example of the achieved benefits with such controlled velocity trajectories of the following vehicle. Fuel Consumption (FC) is indirectly minimized by minimizing the accelerations and decelerations as the autonomous vehicle follows the hypothetical lead. Implementing the cost function in offline Dynamic Programming (DP) with full drive cycle preview showed up to a 17% increase in Fuel Economy (FE). Real time implementation with Model Predictive Control (MPC) showed improvements in FE, proportional to the prediction horizon. Specifically, 20s preview MPC was able to match the DP results. A minimum of 1.5s preview of the lead vehicle velocity with velocity tracking of the lead was required to obtain an increase in FE. The optimal velocity trajectory found from these algorithms exceeded the presently allowable error from standard drive cycles for FC testing. However, the trajectory was still safe and acceptable from the perspective of traffic flow. Based on our results, regulators need to consider relaxing the constant velocity error margins around the standard velocity trajectories dictated by the FTP to encourage FE increase in autonomous driving.

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Section: Introductionmentioning

confidence: 99%

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

Prakash

Cimini

Stefanopoulou

et al. 2016

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Com

View full text Add to dashboard Cite

show abstract

“…One approach to optimizing a drive cycle is velocity smoothing (reducing acceleration and deceleration) which improves passenger comfort by reducing jerk and, in turn, reduces fuel consumption, but this method does not guarantee an absolute minimum of fuel consumption [3][4][5]. This solution is attractive as it is independent of vehicle characteristics such as vehicle mass, road load and aerodynamic resistances.…”

Section: Introductionmentioning

confidence: 99%

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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Research on control strategy of electric vehicle battery thermal management system based on MPC

Wei,

Shuhai,

Jijie

2024

Ferroelectrics

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MPC based vehicular following control considering both fuel economy and tracking capability

Cited by 7 publications

References 11 publications

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

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

Research on control strategy of electric vehicle battery thermal management system based on MPC

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