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Eco-Coasting controller Using Road Grade Preview: Evaluation and Online Implementation Based on mixed integer model predictive control

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第10期72卷 12508-12523页

作者： Yan, Yongjun Li, Nan Hong, Jinlong Gao, Bingzhao Zhang, Jia Chen, Hong Sun, Jing Song, Ziyou Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Auburn Univ Dept Aerosp Engn Auburn AL 36849 USA Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

Coasting is a common method used in eco-driving to reduce fuel consumption by utilizing kinetic energy. However, in order to avoid excessive computation induced by integer coasting maneuvers, the powertrain model used in eco-driving controllers that rely on look-ahead road information has been oversimplified. This oversimplification assumes that the engine goes to idle when coasting, which significantly limits the fuel-saving potential. To address this issue, we propose an eco-coasting strategy that calculates the optimal timing and duration of coasting maneuvers using road information preview. Different from the engine-idling method, two control-oriented coasting methods, fuel cut-off method and engine start/stop method are formulated for the model-based optimal control. To evaluate and choose the best coasting mechanism for eco-coasting strategy, dynamic programming (DP) is performed to provide the globally optimal performance (i.e., benchmark results) for evaluating the engine-idling method, fuel cut-off method, and engine start/stop method. Based on the offline simulation results, the engine start/stop method consistently outperforms the fuel cut-off method in terms of both fuel consumption and travel time. This is attributed to the engine start/stop method eliminating the engine drag torque during deceleration, despite the additional energy cost required for engine restart being taken into account in the modeling, thus providing a fair evaluation. Then, the online performance of the eco-coasting strategy with engine start/stop mechanism is evaluated using mixed integer model predictive control (MIMPC). We propose a tailored mixed-integer programming algorithm to facilitate online implementation. Simulation results show that the proposed eco-coasting strategy achieves near-optimal performance compared to DP and outperforms the rule-based method.

关键词： Eco-coasting strategy Engine start/stop mechanism mixed-integer model predictive control

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Management of hybrid energy supply systems in buildings using mixed-integer model predictive control

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ENERGY CONVERSION AND MANAGEMENT 2015年 98卷 470-483页

作者： Mayer, Barbara Killian, Michaela Kozek, Martin FH Joanneum Inst Ind Management A-8605 Kapfenberg Austria Vienna Univ Technol Inst Mech & Mechatron A-1060 Vienna Austria

In this paper a mixed-integer model predictive controller for hybrid energy supply systems in buildings is presented. This approach is based on a hierarchical building control concept where the energy supply level is coupled to the energy consumption level only by the heat load. The supply level is characterized by non-linear dynamics due to a stratified water storage tank and a switched heat pump with minimum on/off times. The mixed-integer model predictive controller optimizes the unit commitment problem at minimum costs while satisfying the consumption level's predicted heat load. The hybrid system is formulated as a piecewise affine model comprising continuous and discrete system inputs. moreover, the proposed controller is able to manage the stratified storage tank including switching sequences of the heat pump with respect to energy price forecasts. The effectiveness of this approach is shown by a comparison to a model predictive controller with an a priori fixed operation mode profile, where the heat pump is only operating at night, and discussing the effect of the variation of the stratified storage tank size. The proposed concept is able to flexibly manage all sizes of stratified storage tanks with better performance than the reference control strategy, which is only effective for larger tanks. Additionally, a robustness analyses demonstrates that the mixed-integer model predictive controller can handle errors in the heat load prediction from the consumption level. Both analyses show promising results for the practical use of the proposed controller within the hierarchical control concept or as a control module in a similar but more general application. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： mixed-integer model predictive control (MI-MPC) Hybrid energy supply system Building control Unit commitment Stratified storage tank management

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Optimal Semi-Active control for a Hybrid Wind-Wave Energy System on Motion Reduction

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第1期14卷 75-82页

作者： Zhu, Hongzhong Kyushu Univ Res Inst Appl Mech Fukuoka 8190395 Japan

Integration of wave energy converters and floating wind turbine can potentially reduce the levelized cost of energy. The coupling effects between the wave energy converters and the foundation, however, could amplify the foundation motion so that both the efficiency and the fatigue life of wind turbine would be degraded. In this study, wave energy converters are designed as a controlled suspension system for a novel hybrid wind-wave energy system, and an optimal semi-active control method having predictive ability is proposed for reducing the foundation motion. In the controller design, the physical constraints and the passivity of the wave energy converters are also taken into consideration. A full numerical model taking account of aero-hydro-servo-mooring coupled dynamics is proposed for a comprehensive analysis of the controller under various environmental conditions. Numerical results show that the foundation motion can be reduced by 16% even in very rough sea states with the help of the controller.

关键词： Semi-active control hybrid wind-wave energy system mixed-integer model predictive control motion reduction

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Robust economic-environmental dispatch problem for BESS and PV generators in MT-HVDC systems: A MI-MPC approach

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JOURNAL OF ENERGY STORAGE 2024年 96卷

作者： Useche-Arteaga, Mario Gil-Gonzalez, Walter Montoya, Oscar Danilo Univ Politecn Cataluna Ctr Innovacio Tecnol Convertidors Estat & Accionam Dept Engn Elect Barcelona Spain Univ Tecnol Pereira Fac Engn Dept Elect Engn Pereira 660003 Colombia Univ Distrital Francisco Jose Caldas Fac Ingn Grp Compatibil & Interferencia Electromagnet Bogota 110231 Colombia

The research addresses the energy management problem (EMP) in multi-terminal high-voltage direct current (MT-HVDC) systems using a mixed-integer model predictive control (MI-MPC). The MT-HVDC system consists of conventional thermal generation plants (fossil sources), renewable generation sources (solar photovoltaic plants), and battery energy storage systems (BESS). This combination creates a complex nonlinear EMP, as it introduces the power losses of the transmission networks through B-coefficients. The main contributions of this research can be summarized in three elements: (i) the formulation of a measurement- or simulation-based approach to determine B - coefficients in MT-HVDC systems for calculating power losses;(ii) the convexification of the EMP model through MI-MPC approaches, allowing to minimize economic and environmental objective functions;and (iii) the inclusion of uncertainties through the robust convex method. Numerical validations in the 11-bus grid, including the linear transportation model for the MT-HVDC system, confirm the multi- objective nature of the EMP when considering economic and environmental indices. In addition, the effect of uncertainties on generation and demand causes significant variations in the optimal Pareto front when compared to the deterministic scenario.

关键词： Energy management problem Multi-terminal high-voltage direct current Battery energy storage mixed-integer model predictive control

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Learning-based multi-agent MPC for irrigation scheduling

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control ENGINEERING PRACTICE 2024年 147卷

作者： Agyeman, Bernard T. Naouri, Mohamed Appels, Willemijn M. Liu, Jinfeng Shah, Sirish L. Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Lethbridge Coll Ctr Appl Res Innovat & Entrepreneurship Lethbridge AB T1K 1L6 Canada

Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning -based predictive irrigation scheduler that integrates machine learning and model predictive control (MPC), while also incorporating multi -agent principles. The proposed framework incorporates a three -stage management zone delineation process, utilizing k -means clustering and hydraulic parameters estimates for optimized agro-hydrological modeling. Long Short -Term Memory (LSTM) networks are employed for accurate and computationally efficient root zone soil moisture modeling. The scheduler, formulated as a mixed -integer MPC with zone control, utilizes the identified LSTM networks to maximize root water uptake while minimizing overall water consumption and fixed irrigation costs. Additionally, the learning -based scheduler adopts a multi -agent MPC paradigm, where decentralized hybrid actor-critic agents and the concept of a limiting irrigation management zone are employed to enhance computational efficiency. Evaluating the performance on a 26.4 -hectare field in Lethbridge for the 2015 and 2022 growing seasons demonstrates the superiority of the proposed scheduler over the widely -used triggered scheduling approach in terms of Irrigation Water Use Efficiency (IWUE) and total prescribed irrigation. Notably, the proposed approach achieves water savings between 7 to 23%, coupled with IWUE increases ranging from 10 to 35%.

关键词： Learning-based scheduler mixed-integer model predictive control Hybrid actor-critic paradigm Proximal policy optimization Nonlinear programming

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LSTM-based model predictive control with discrete actuators for irrigation scheduling 13th

LSTM-based model predictive control with discrete actuators ...

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13th IFAC Symposium on Dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Agyeman, Bernard T. Sahoo, Soumya R. Liu, Jinfeng Shah, Sirish L. Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. In this article, a model predictive control (MPC) with discrete actuators is developed for irrigation scheduling, where a long short-term memory (LSTM) model of the soil-water-atmosphere system is used to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. A heuristic method involving a sigmoid function is used in this framework to enhance the computational efficiency of the scheduler. The scheduling scheme is applied to a homogeneous field and the results indicate that the LSTM-based MPC with discrete actuators is able to prescribe optimal or near-optimal irrigation schedules that are typical of irrigation practice. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://***/licenses/by-nc-nd/4.0/)

关键词： Irrigation scheduling mixed-integer model predictive control heuristic method long short-term memory networks sigmoid function

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LSTM-based model predictive control with discrete actuators for irrigation scheduling

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IFAC-PapersOnLine 2022年第7期55卷 334-339页

作者： Bernard T. Agyeman Soumya R. Sahoo Jinfeng Liu Sirish L. Shah

关键词： Irrigation scheduling mixed-integer model predictive control heuristic method long short-term memory networks sigmoid function

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Reinforcement Learning for mixed-integer problems based on MPC 21st

Reinforcement Learning for mixed-integer problems based on M...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Gros, Sebastien Zanon, Mario Norwegian Univ Technol NTNU Trondheim Norway IMT Sch Adv Studies Lucca Lucca Italy

model predictive control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning. This approach has been investigated for Q-learning and actor-critic methods, both in the context of nominal Economic MPC and Robust (N)MPC, showing very promising results. In that context, actor-critic methods seem to be the most reliable approach. Many applications include a mixture of continuous and integer inputs, for which the classical actor-critic methods need to be adapted. In this paper, we present a policy approximation based on mixed-integer MPC schemes, and propose a computationally inexpensive technique to generate exploration in the mixed-integer input space that ensures a satisfaction of the constraints. We then propose a simple compatible advantage function approximation for the proposed policy, that allows one to build the gradient of the mixed-integer MPC-based policy. Copyright (C) 2020 The Authors.

关键词： Reinforcement Learning mixed-integer model predictive control actor-critic methods stochastic and deterministic policy gradient

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Reinforcement Learning for mixed-integer problems based on MPC

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IFAC-PapersOnLine 2020年第2期53卷 5219-5224页

作者： Sebastien Gros Mario Zanon Norwegian University of Technology NTNU IMT School for Advanced Studies Lucca

model predictive control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning. This approach has been investigated for Q-learning and actor-critic methods, both in the context of nominal economic MPC and Robust (N)MPC, showing very promising results. In that context, actor-critic methods seem to be the most reliable approach. Many applications include a mixture of continuous and integer inputs, for which the classical actor-critic methods need to be adapted. In this paper, we present a policy approximation based on mixed-integer MPC schemes, and propose a computationally inexpensive technique to generate exploration in the mixed-integer input space that ensures a satisfaction of the constraints. We then propose a simple compatible advantage function approximation for the proposed policy, that allows one to build the gradient of the mixed-integer MPC-based policy.

关键词： Reinforcement Learning mixed-integer model predictive control actor-critic methods stochastic deterministic policy gradient

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