A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Malikopoulos, Andreas A.

doi:10.1109/tcst.2015.2454444

Cited by 39 publications

(20 citation statements)

References 38 publications

(47 reference statements)

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“…Therefore, through the proposed approach, we consider minimizing the control input (acceleration/deceleration though the gas/brake pedal position) that results in minimizing transient engine operation. If we minimize transient engine operation, we have direct benefits in fuel consumption [5] and emissions since internal combustion engines are optimized over steady state operating points (constant torque and speed) [27], [28]. Fuel consumption was quantified by using the polynomial metamodel proposed in [36] which yields vehicle fuel consumption as a function of speed, v(t), and control input, u(t), namelẏ…”

Section: Simulation Framework and Resultsmentioning

confidence: 99%

“…Even though the speed of each vehicle is reduced, the throughput at the speed reduction zone is maximized. Moreover, by minimizibg the acceleration/deceleration of each vehicle, we minimize transient engine operation, thus we can have direct benefits in fuel consumption [5] and emissions since internal combustion engines are optimized over steady state operating points (constant torque and speed) [27], [28].…”

Section: B Optimal Control Problem Formulationmentioning

confidence: 99%

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Optimal Control for Speed Harmonization of Automated Vehicles

Malikopoulos

Hong

Park

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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This article addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12-17% compared to the variable speed limit algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicularbased SPD-HARM algorithm. His research spans several fields, including analysis, optimization, and control of cyber-physical systems; decentralized systems; and stochastic scheduling and resource allocation problems. The emphasis is on applications related to sociotechnical systems, energy efficient mobility systems, and sustainable systems. He is currently an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems. He is a member of SIAM and a Fellow of the ASME. Seongah Hong received the B.S. degree in Urban Planning and Engineering from Yonsei University, Seoul, Korea in 2012 and the M.S. from the Civil Engineering in the University of Minnesota, Duluth, MN in 2014, and a Ph.D. degree in the Civil and Environmental Engineering Department at the University of Virginia, Charlottesville, VA, USA. She has participated in various research project about traffic operations, intelligent transportation systems, connected automated vehicles applications, cybersecurity and monitoring system, and sustainable traffic control devices. She has conducted extensive work of modeling and analyzing the effectiveness of the traffic operations applications with the customized control algorithms under diverse scenarios.

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Section: Simulation Framework and Resultsmentioning

confidence: 99%

Section: B Optimal Control Problem Formulationmentioning

confidence: 99%

Optimal Control for Speed Harmonization of Automated Vehicles

Malikopoulos

Hong

Park

et al. 2019

IEEE Trans. Intell. Transport. Syst.

Self Cite

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“…in [93] to minimize the discounted infinite-horizon cost, and in [94,95] in a shortest path formulation. A stochastic optimal control framework is developed in [96,97] to determine the policy minimizing the long-run expected average cost. All formulations yield a causal, time-invariant, state-feedback controller that can be fairly easily implemented.…”

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

523

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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“…Optimal control is a standard approach to adjustment of such inconsistent objectives with restrictions. However, it balances conflicting objectives and does not allow simultaneous achievement of both objectives [19], [20]. Another approach is the use of consistent trajectories for the velocity and force [21], where the position trajectory is calculated from the required force reference.…”

mentioning

confidence: 99%

“…,(11),(15),(16),(20),(21),(25),(26),(30),(31),(35), and(36), where R = 1 + H−1 v Z e . The transfer functions show the achievement of the control objectives in…”

mentioning

confidence: 99%

Periodic/Aperiodic Motion Control Using Periodic/Aperiodic Separation Filter

Muramatsu

Katsura

2020

IEEE Trans. Ind. Electron.

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Motion control is a fundamental technique used in automated mechanical systems. Classically, velocity, force, and impedance are controlled in motion control systems, but simultaneous control is difficult. This article proposes periodic/aperiodic (P/A) motion control based on periodicity and aperiodicity of motion. The P/A motion control separately applies different control methods to P/A motions using P/A velocity and P/A force, which are extracted using a periodic/aperiodic separation filter (PASF) from velocity and force. Accordingly, six types of P/A motion controls are constructed in this article, which correspond to different combinations of the P/A velocity, P/A force, and P/A impedance controls. To construct the P/A motion control systems, acceleration control based on a disturbance observer is used. The ACS, which rejects disturbances, enables the P/A motion control design to ignore disturbances. The experiments were conducted to validate the six P/A motion controls, which simultaneously realized two P/A motion control objectives.

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A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Cited by 39 publications

References 38 publications

Optimal Control for Speed Harmonization of Automated Vehicles

Optimal Control for Speed Harmonization of Automated Vehicles

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

Periodic/Aperiodic Motion Control Using Periodic/Aperiodic Separation Filter

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