Engine power smoothing energy management strategy for a series hybrid electric vehicle

Cairano, Stefano Di; Liang, Wei; Kolmanovsky, Ilya; Kuang, Ming; Phillips, Anastasia

doi:10.1109/acc.2011.5990627

Cited by 30 publications

(20 citation statements)

References 8 publications

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“…In applications such as automotive [7], [8], [52], the memory requirements are actually several orders of magnitude more stringent than the chronometrics requirements. For instance, automotive control units running several tens of control loops and significant amount of control logics may have only in the order of a megabyte of memory for code and data, for all the controllers and logics.…”

Section: E Memory Occupancy and Code Complexitymentioning

confidence: 99%

Projection-free parallel quadratic programming for linear model predictive control

Cairano

Brand

Bortoff

2013

International Journal of Control

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Brand, M.; Bortoff, S. TR2013-059 July 2013Abstract A key component in enabling the application of model predictive control (MPC) in fields such as automotive, aerospace and factory automation is the availability of low-complexity fast optimization algorithms to solve the MPC finite horizon optimal control problem in architectures with reduced computational capabilities. In this paper we introduce a projection-free iterative optimization algorithm and discuss its application to linear MPC. The algorithm, originally developed by Brand for non-negative quadratic programs, is based on a multiplicative update rule and it is shown to converge to a fixed point which is the optimum. An acceleration technique based on a projection-free line search is also introduced, to speed-up the convergence to the optimum. The algorithm is applied to MPC through the dual of the quadratic program (QP) formulated from the MPC finite time optimal control problem. We discuss how termination conditions with guaranteed degree of suboptimality can be enforced, and how the algorithm performance can be optimized by pre-computing the matrices in a parametric form.We show computational results of the algorithm in three common case studies and we compare such results with the results obtained by other available free and commercial QP solvers. International Journal of ControlThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractA key component in enabling the application of model predictive control (MPC) in fields such as automotive, aerospace and factory automation is the availability of low-complexity fast optimization algorithms to solve the MPC finite horizon optimal control problem in architectures with reduced computational capabilities. In this paper we introduce a projection-free iterative optimization algorithm and discuss its application to linear MPC. The algorithm, originally developed by Brand for non-negative quadratic programs, is based on a multiplicative update rule and it is shown to converge to a fixed point which is the optimum. An acceleration technique based on a projectionfree line search is also introduced, to speed-up the convergence to the optimum. The algorithm is applied to MPC through the dual of the quadratic program (QP) formulated from the MPC finite time optimal control problem. We discuss how termination conditions with guaranteed degree of suboptimality can be enforced, and how the algo...

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Section: E Memory Occupancy and Code Complexitymentioning

confidence: 99%

Projection-free parallel quadratic programming for linear model predictive control

Cairano

Brand

Bortoff

2013

International Journal of Control

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“…Instead of minimizing only energy consumption, several authors have addressed also different optimization goals, such as pointwise powertrain efficiency [106], drivability [107], pollutant emissions [63,108], battery aging [109,110,111], driving cost [112,113,114,115]. The MPC approach in [116], instead, combines longitudinal control and energy management, exploiting forecasts of traffic signals and road slope.…”

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

526

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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“…There exists a conclusion that ‫ݔ‬ ଷ is linear correlation with ‫ݔ‬ ଵ ǡ ‫ݔ‬ ଶ from (2) and (3).The poles of the closed control system that are expressed in (1) supposed as ‫‬ ଵ ǡ ‫‬ ଶ ǡ ‫‬ ଷ . Then, the feedback gains of controllers can be obtained by configuration of poles [4][5]: From the above equations, ݂ ସ can be reprented by ݂ ଵ and ݂ ହ can be reprented by ݂ ଶ , while ݂ ൌ Ͳ. Hence ݂ ଵ ǡ ݂ ଶ ǡ ݂ ଷ are the gains that should be optimized.…”

Section: Overview Of Control Systems For Phevmentioning

confidence: 99%

Battery Component Control System Design for PHEV Based on ECE Cycle

Lin

Zhou

Cao

et al. 2012

2012 Third Global Congress on Intelligent Systems

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As the key technology of electric vehicles EV , control systems is always the research hotspot of EV field. When the battery component of HEV operates at charge sustaining (CS) mode, the most important problem is how to split the whole vehicle's required power between engine and battery component reasonably and effectively so as to make the good economy and driving performance. Taking battery component of Plug-in Hybrid Electric Bus as the research object, this paper established the model of feedback control system for battery component. Then the feedback control systems gains are optimized with the objective function of equivalent fuel consumption and accumulated power requirement error, which is based on BUSRTE driving cycle. The obtained gains are testified and verified by NEWYORK and CBDBUS driving cycle respectively. The results indicate that the optimized control system for batteries has good fuel economy and driving performance.

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Engine power smoothing energy management strategy for a series hybrid electric vehicle

Cited by 30 publications

References 8 publications

Projection-free parallel quadratic programming for linear model predictive control

Projection-free parallel quadratic programming for linear model predictive control

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

Battery Component Control System Design for PHEV Based on ECE Cycle

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