Performance Enhanced Predictive Control for Adaptive Cruise Control System Considering Road Elevation Information

Li, Shengbo Eben; Guo, Qiangqiang; Xu, Shaobing; Duan, Jingliang; Shen, Li; Liu, Chengjun; Su, Kuifeng

doi:10.1109/tiv.2017.2736246

Cited by 75 publications

(38 citation statements)

References 33 publications

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“…An exemplary work of the NMPC, where an energy-efficient NMPC was introduced to drive a vehicle on roads with varying traffic and signals at intersections was introduced in [3]. An NMPC for a fuel-saving ACC system to improve the performance on tracking accuracy and fuel consumption by simultaneously considering the road elevation information and nonlinear powertrain dynamics was presented in [17]. An Eco-ACC system for plug-in hybrid electric vehicles to improve the total energy consumption and vehicle safety was introduced in [18].…”

Section: B Related Work In the Literaturementioning

confidence: 99%

“…The conditional probability Pr xt indicates the probability of g j (x i ) ≤ 0 holds based on initial statesx 0 = x t ; please note that the predicted statesx i depend on disturbances {ω i } i−1 i=0 [21]. Using the cost function (17) and the individual chanceconstraint (18), the OCP for (15) is formulated as follows:…”

Section: A Stochastic Nonlinear Model Predictive Control (Snmpc)mentioning

confidence: 99%

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Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

Sajadi-Alamdari

Voos

Darouach

2020

IEEE Intell. Transport. Syst. Mag.

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Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and dynamic traffic conditions such as speed limits and preceding vehicles have their share of energy consumption in the host electric vehicle. Optimal energy management based on a semi-autonomous ecological advanced driver assistance system can improve the longitudinal velocity regulation in a safe and energy-efficient driving strategy. The main contribution of this paper is the design of a real-time risksensitive nonlinear model predictive controller to plan the online cost-effective cruising velocity in a stochastic traffic environment. The basic idea is to measure the relevant states of the electric vehicle at runtime, and account for the road slopes, the upcoming curves, and the speed limit zones, as well as uncertainty in the preceding vehicle behaviour to determine the energy-efficient velocity profile. Closed-loop Entropic Value-at-Risk as a coherent risk measure is introduced to quantify the risk involved in the system constraints violation. The obtained simulation and field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of safe and energy-efficient states regulation and constraints satisfaction.

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Section: B Related Work In the Literaturementioning

confidence: 99%

Section: A Stochastic Nonlinear Model Predictive Control (Snmpc)mentioning

confidence: 99%

Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

Sajadi-Alamdari

Voos

Darouach

2020

IEEE Intell. Transport. Syst. Mag.

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“…where T is the total time of simulation or experiment, v(t) is the ego vehicle velocity at time t obtained as the closed-loop state of system (5) controlled with u MPC described in (7). The second term of the cost (6b) penalizes the control effort and presents energy consumption criteria.…”

Section: Problem Definition and Formulationmentioning

confidence: 99%

“…Road grade can considerably affect the ACC controller performance. The work in [7] considers an ACC which takes into account road elevation data to improve energy efficiency. In [13] real-time estimation of road grade and vehicle mass are utilized to improve comfort.…”

Section: Introductionmentioning

confidence: 99%

Safe Adaptive Cruise Control with Road Grade Preview and $\mathbf{V}2\mathbf{V}$ Communication

Firoozi

Nazari

Guanetti

et al. 2019

2019 American Control Conference (ACC)

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We present the design of a safe Adaptive Cruise Control (ACC) which uses road grade and lead vehicle motion preview. The ACC controller is designed by using a Model Predictive Control (MPC) framework to optimize comfort, safety, energy-efficiency and speed tracking accuracy. Safety is achieved by computing a robust invariant terminal set. The paper presents a novel approach to compute such set which is less conservative than existing methods. The proposed controller ensures safe inter-vehicle spacing at all times despite changes in the road grade and uncertainty in the predicted motion of the lead vehicle. Simulation results compare the proposed controller with a controller that does not incorporate prior grade knowledge on two scenarios including car-following and autonomous intersection crossing. The results demonstrate the effectiveness of the proposed control algorithm.

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“…Many ACC researches have been done, with different control theories being applied, such as MPC [3] and Fuzzy [4]. Some papers also do a comparison between different control technics, like [5] and [6].…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of an Adaptive Cruise Control System with a Customized Engine Control Unit

Kapelius¹,

Gatti²,

M³

et al. 2019

Blucher Engineering Proceedings

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This paper describes the development of an adaptive cruise control system (ACC), a driving assistant system, whose purpose is to control the vehicle's speed, determined by a setpoint inputted by the driver. Simultaneously, the system must also monitor the environment and adjust the car's velocity in order to maintain a safety distance from the other vehicles driving on the lane. This system's main purpose is to increase the driver's comfort, thus making it so he will no longer be required to control the throttle pedal and therefore be able to focus on the other tasks involved in driving. For the speed control to be possible, the system communicates with an open engine control unit (ECU), developed by the Automotive Engineering Group (group formed by researchers members of Escola Politécnica da USP and of Faculdade de Tecnologia de Santo André) as well as with an automotive radar, installed on the vehicle's front bumper, responsible for measuring relative distance and relative speed to the other vehicles driving on the road. As the vehicle used does not have brake-bywire technologies implemented, the speed reducing is done via engine breaking. After its development, the system was implemented on a Volkswagen Polo 2004 and its performance was validated with tests on an inertial dynamometer and with road tests.

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Performance Enhanced Predictive Control for Adaptive Cruise Control System Considering Road Elevation Information

Cited by 75 publications

References 33 publications

Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

Safe Adaptive Cruise Control with Road Grade Preview and $\mathbf{V}2\mathbf{V}$ Communication

Development and Implementation of an Adaptive Cruise Control System with a Customized Engine Control Unit

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