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convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles

ENERGY 2022年 257卷

作者： Li, Yapeng Wang, Feng Tang, Xiaolin Hu, Xiaosong Lin, Xianke Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing Peoples R China Ontario Tech Univ Dept Automot Mech & Mfg Engn Oshawa ON L1G 0C5 Canada

Energy management is one of the key technologies to improve the energy efficiency of electrified vehicles. In the existing real-time powertrain control strategies, most studies focus on improving fuel economy based on high-fidelity powertrain models without adequately exploring the impact of model accuracy on computational efficiency and energy saving. To address this research gap, this paper proposes a hierarchical control framework to minimize the fuel consumption of a plug-in hybrid electric vehicle. Specifically, three main contributions are presented to distinguish our efforts from existing research. First, two types of powertrain models are used in the optimization framework. In the upper control layer, an approximated model is employed to generate the optimal reference state of charge trajectory using convex optimization. Then in the lower control layer, by integrating the equivalent consumption minimize strategy into the model predictive control framework, the fuel consumption is minimized in real-time by using a high-fidelity powertrain model. Second, optimization results from the other three real-time control strategies and two predictive energy management strategies are presented and analyzed to verify the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are discussed. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Energy management Plug-in hybrid electric vehicles convex programming Real-time control Sustainable transport

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Heterogeneous RAN slicing resource allocation using mathematical program with equilibrium constraints

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IET COMMUNICATIONS 2022年第15期16卷 1772-1786页

作者： Ma, Tengteng Zhang, Yong Han, Zhu Li, Chen China Telecom Corp Ltd Strategy Dev Inst Beijing Res Inst Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China Univ Houston Elecct & Comp Engn Dept Houston TX USA Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two-layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi-variational problem, indicating that the MPEC is solvable. Combining the two-layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.

关键词： telecommunication computing total network system mathematical program quasivariational problem infrastructure provider two-layer resource allocation architecture fair algorithm convex programming resource allocation individual slice utility virtualisation network slicing equilibrium constraints heterogeneous radio access network heterogeneous RAN two-layer architecture cloud computing radio access networks fair resource allocation strategies 5G mobile communication mathematical programming mobile virtual network operators multiple tenants unfair resource allocation virtual resources existing static slicing successive convex approximation method MPEC

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Optimal downlink and uplink design in a wireless powered two-user indoor communication system

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IET COMMUNICATIONS 2022年第7期16卷 745-759页

作者： Syam, M. S. Che, Yue Ling Luo, Sheng Wu, Kaishun Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Shenzhen Univ IoT Res Ctr Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Canghai Campus Shenzhen 518060 Guangdong Peoples R China

This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H-AP), due to the short device-to-device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H-AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non-convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device-to-device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum-throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non-convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless-powered communication system.

关键词： nonconvex problem unfair downlink uplink design information signals indoor scenario sufficient downlink rate dedicatedly deployed hybrid access point energy harvesting uplink rate allocation phenomenon convex programming telecommunication power management indoor applications total throughput increases outdoor communications uplink communication quality radio access networks wireless powered indoor communication system uplink rate allocation fairness convex optimization techniques uplink throughput two-user indoor communication system optimal downlink wireless-powered communication system indoor communication uplink sum-throughput device-to-device distance mobile radio wireless powered communication network multi-access systems

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Accelerated convergence of time-splitting algorithm by relaxation method

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IET CONTROL THEORY AND APPLICATIONS 2022年第8期16卷 776-788页

作者： Gao, Jiaxin Li, Shengbo Eben Ma, Fei Li, Wenyu Sun, Hao Maihemuti, Maierdanjiang Jin, Chun Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China

The alternating direction method of multipliers (ADMM) is a widely used model predictive control (MPC) acceleration method. It adopts the time-splitting technique, splitting the original problem into independent subproblems. Relaxed ADMM (R-ADMM) is a generalization of ADMM that often achieves faster convergence. However, its parameters must be chosen by an expert user. Besides, the existing convergence proof of R-ADMM adopts a first-order Taylor approximation, which makes the range of relaxation factors conservative. We tackle these weaknesses by giving rigorous evidence and finding the optimal relaxation factor. Firstly, we deduce the convergence of the R-ADMM algorithm, yielding an accurate range of relaxation factors. Then, we analyze the relationship between convergence rate and relaxation factor and conclude that the optimal relaxation factor depends on a recurrence condition. Since splitting introduces the equality constraint, the decoupled states are getting close in the iteration, meeting the recursive requirements and helping find the optimal relaxation factor. Finally, various trajectory tracking tasks are conducted to verify the efficiency of the R-ADMM algorithm. And the simulation results show that the R-ADMM algorithm reduces the number of iterations by 63.7% compared with the ADMM algorithm in the double lane change task.

关键词： relaxation method first-order Taylor approximation time-splitting technique convergence rate optimal relaxation factor relaxation factors convex programming alternating direction method optimisation relaxed ADMM convergence approximation theory R-ADMM algorithm widely used model predictive control acceleration method time-splitting algorithm existing convergence proof predictive control iterative methods accelerated convergence convergence of numerical methods

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Optimal frequency regulation in an uncertain islanded microgrid: A modified direct search algorithm

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IET RENEWABLE POWER GENERATION 2022年第4期16卷 726-739页

作者： Abolpour, Roozbeh Javanmardi, Hamidreza Dehghani, Maryam Hesamzadeh, Mohammad Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden

This paper presents a methodology for frequency regulation in a microgrid involving renewable energy sources (RES) using a dynamic controller, which is an output feedback controller (OFC). The parameters of OFC are tuned by searching the design space of the controller. Since the RES model is not exactly known, the uncertain model is derived and the OFC is considered for it. The goal of controller tuning is to find appropriate parameters of the controller such that the norm of frequency deviations, even in presence of uncertainties in the RES parameters is minimized. An algorithm based on searching the controller design space is suggested to find the suitable controller gains. The algorithm assumes the controller parameters lie in a convex space and searches the space systematically such that an appropriate solution is found. The method is proved mathematically and two theorems are mentioned, accordingly. Finally, a simulated model of a RES is utilized for algorithm evaluation and the results demonstrate the algorithm capability in optimal frequency regulation.

关键词： simulated model optimal frequency regulation power generation control distributed power generation algorithm capability modified direct search algorithm OFC RES parameters convex space convex programming control system synthesis optimal control output feedback controller frequency deviations algorithm evaluation uncertain islanded microgrid frequency control controller design space controller parameters suitable controller gains dynamic controller uncertain model search problems renewable energy sources RES model controller tuning uncertain systems

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New H∞ observer-based control for delayed LPV stochastic system

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IET CONTROL THEORY AND APPLICATIONS 2022年第3期16卷 353-365页

作者： Chang, Wen-Jer Ku, Cheung-Chieh Chen, Guan-Wen Natl Taiwan Ocean Univ Dept Marine Engn Keelung Taiwan Ship & Ocean Ind R&D Ctr New Taipei Taiwan

An H-infinity observer-based control problem of delayed Linear Parameter Varying (LPV) stochastic system is addressed in this paper. In the LPV stochastic system, stochastic behaviors and external disturbance in real environment are respectively described by stochastic difference function. Furthermore, the delay effects on state and control input are considered as time-varying case for wide application. For the control problem, a lemma dealing with the delay terms is proposed to increase some slack variables. Based on the proposed lemma and the chosen Lyapunov-Krasovskii function, some sufficient conditions are derived to relax the conservatism of stability criterion. Besides, disturbance effect on state and estimation error is constrained via H-infinity performance for improving transient response. Through using the extended projection lemma, the derived conditions are formulated as strict Linear Matrix Inequality (LMI) problem. To avoid the potential oscillation, another lemma is proposed to separately assign the performance level during solving the conditions. All feasible solutions can be directly obtained by convex optimization algorithm without using extra calculation. Therefore, an observer-based controller is established to guarantee the asymptotical stability and H-infinity performance in the mean square. Finally, some numerical simulations are provided to demonstrate the effectiveness and applicability of the proposed method.

关键词： stochastic systems linear matrix inequalities H Lyapunov-Krasovskii function stochastic difference function observers convex optimization algorithm asymptotical stability performance convex programming delayed LPV stochastic system delayed linear parameter varying stochastic system stability criterion numerical simulations asymptotic stability external disturbance stability criteria state error Lyapunov methods observer-based control control linear parameter varying systems estimation error delays transient response extended projection lemma stochastic behaviors

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Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing

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IEEE ACCESS 2022年 10卷 72416-72428页

作者： Liu, Jianhua Wu, Zibo Liu, Jiajia Tu, Xiaoguang Civil Aviat Flight Univ China Inst Elect & Elect Engn Deyang 618307 Sichuan Peoples R China

Edge computing is a new computing paradigm which distributes tasks to edge networks for processing, it provides effective support for various mobile applications to meet their rapid response requirement. Unmanned Aerial Vehicle (UAV) has been widely used in emergency rescue, mapping, etc., with the advantages of flexible deployment and rapid movement. However, on one hand, mobile applications will be terminated when the battery energy is exhausted. On the other hand, mobile applications will be out of service when mobile devices are out of radio coverage of UAVs. How to achieve low-cost task unloading in the resource limited and location sensitive multi-UAVs edge computing environment is rather challenging. In this paper, we propose a distributed location-aware task offloading scheme, aiming at the above issues. Specifically, we create a nonlinear task allocation problem by combining the limited energy constraints of edge nodes with the random movement of users, where the cost function is divided into static and dynamic costs, respectively. Then, we formulate this problem to a convex optimization one with linear constrains, based on regularization technology. The mathematical proof shows that the scheme can support a parameterized competitive ratio without requiring any prior knowledge of the input task. The simulation results show that the proposed scheme can achieve lower cost edge computing services.

关键词： Task analysis Costs Edge computing Internet of Things Autonomous aerial vehicles Resource management Optimization convex programming edge computing task allocation UAV

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Contour information regularized tensor ring completion for realistic image restoration

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IET IMAGE PROCESSING 2022年第13期16卷 3499-3506页

作者： Yu, Zhi Luo, Yihao Liu, Zhifa Zhou, Guoxu Guangdong Univ Technol Sch Automat Guangzhou 51006 Peoples R China Minist Educ Key Lab Intelligent Detect & Internet Things Mfg Guangzhou Peoples R China

Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low-rank nature of images, implicitly requiring that the whole picture is of globally consistent features. As a result, existing tensor completion algorithms often give reasonably good performance if the target image has only random pixel-level missing. Unfortunately, pixel-level missing is very rare in practice and it is often wanted to restore an image with irregular hole-shaped missing, such as removing electricity poles from landscape photos or irrelevant people from tourist photos. This task is extremely difficult for traditional low-rank based tensor completion methods. To overcome this drawback, a Contour Information regularized Tensor RIng Completion (CITRIC) method is proposed for practical image restoration. Meanwhile, the contour information regularization is used to capture significant local features, whereas the low-rank tensor ring structure is utilized to capture as much global information as possible. The alternating direction method of multipliers (ADMM) is adopted to optimize the cost function. Extensive experimental results using real-world images show that CITRIC is more practical than existing methods and can restore real-world images with irregular hole-shaped missing.

关键词： tensors image restoration alternating direction method of multipliers real-world images target image realistic image restoration global information realistic images contour information regularized tensor ring completion irregular hole-shaped missing tensor completion algorithms low-rank tensor ring structure pixel-level missing convex programming Contour information regularized tensor ring completion random pixel-level

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Optimal power scheduling of seaport microgrids with flexible logistic loads

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2711-2720页

作者： Fang, Sidun Wang, Chenxu Liao, Ruijin Zhao, Changhong Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Chongqing Univ Sch Elect Engn Chongqing Peoples R China

Seaports are progressively electrified to harness various energy resources to provide green logistic services to ships. They essentially act as multi-energy microgrids to enhance the energy efficiency and environmental sustainability of the maritime industry. This trend necessitates the coordinated operation of multiple sectors in seaport microgrids, which is a challenging problem due to the complex couplings of power, thermal, and fluid flows and the heterogeneity of logistic loads. An optimal power scheduling framework for seaport microgrids is developed. Specifically, a non-linear, non-convex optimisation problem is formulated to integrate various logistic loads, including cold ironing, quay and yard cranes, and reefer areas, in an unbalanced multi-phase power distribution network connected with a thermal network. The optimisation problem is solved in a scalable decomposed manner based on iterative convex relaxation and approximation. Simulation results on a real-world port model verify the efficacy of the proposed framework, as well as the potential of the flexible logistic loads in improving port energy efficiency.

关键词： seaports ships cranes flexible logistic loads yard cranes distributed power generation nonconvex optimisation problem energy conservation seaport microgrids multienergy microgrids convex programming optimisation iterative methods logistics port energy efficiency optimal power scheduling framework unbalanced multiphase power distribution network green logistic services scheduling quay harness various energy resources sea ports freight handling

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A fast robust numerical continuation solver to a two-dimensional spectral estimation problem

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IET CONTROL THEORY AND APPLICATIONS 2022年第9期16卷 902-915页

作者： Zhu, Bin Liu, Jiahao Sun Yat Sen Univ Sch Intelligent Syst Engn Waihuan East Rd 132 Guangzhou 510006 Peoples R China

This paper presents a fast algorithm to solve a spectral estimation problem for two-dimensional random fields. The latter is formulated as a convex optimization problem with the Itakura-Saito pseudodistance as the objective function subject to the constraints of moment equations. The structure of the Hessian of the dual objective function is exploited in order to make possible a fast Newton solver. Then the Newton solver is incorporated to a predictor-corrector numerical continuation method which is able to produce a parametrized family of solutions to the moment equations. Two sets of numerical simulations are performed to test the algorithm and spectral estimator. The simulations on the frequency estimation problem show that our spectral estimator outperforms the classical windowed periodograms in the case of two hidden frequencies and has a higher resolution. The other set of simulations on system identification indicates that the numerical continuation method is more robust than Newton's method alone in ill-conditioned instances.

关键词： predictor-corrector methods fast Newton solver convex optimization problem spectral estimator moment equations convex programming 2D spectral estimation problem Newton's method numerical simulations 2D random fields Itakura-Saito pseudodistance Newton method spectral analysis dual objective function fast robust numerical continuation solver numerical analysis frequency estimation predictor-corrector numerical continuation method frequency estimation problem objective function subject

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