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hierarchical MPC-based Motion Planning for Automated Vehicles in Parallel Autonomy

UNMANNED SYSTEMS 2024年第5期12卷 927-938页

作者： Cheng, Zijun Zeng, Xianlin Fang, Hao Wang, Gang Dou, Lihua Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China

Automated vehicles with parallel autonomy show advantages over fully automated vehicles and manual driving. This paper proposes a hierarchical motion planning method that mixes inputs of human drivers and the automated driving systems for automated vehicles in scenarios such as multi-lane roads and multi-intersections with dynamic obstacles. The proposed method comprises a reference path generator in the upper level and a nonlinear model predictive controller with mixed human-vehicle control in the lower level. The path planner considers dynamic obstacles, static obstacles, and human comfort to generate a reference path composed of splines with continuous curvatures in the upper level. In the lower level, the MPC generates a trajectory by tracking the reference path and optimizing the cost function containing inputs of drivers while avoiding both dynamic and static obstacles. The simulation verifies the efficacy and the computational tractability of the proposed method.

关键词： hierarchical model predictive control motion planning automated vehicle shared control

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Computationally efficient model predictive control for electric vehicle battery thermal management

Computationally efficient model predictive control for elect...

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2024 Vehicle Power and Propulsion Conference

作者： Li, Kaibo Dinh, Truong Yao, Kairui

ISBN: (纸本)9798331541613;9798331541606

model predictive control (MPC) stands out as a promising approach for Battery Thermal Management (BTM). However, due to large thermal inertia of batteries, achieving optimal control of BTM necessitates longer prediction horizons, leading to increased computational loads and uncertainties. This study aims to address these challenges by developing computationally efficient MPC solutions for BTM. Two approaches, Adaptive MPC (AMPC) and hierarchical MPC (H-MPC), are proposed alongside the baseline Nonlinear MPC (NLMPC). NLMPC employs linearization at the nominal operating point and updates state parameters online, thereby simplifying model complexity. Through AMPC, both accumulation constraint violation and execution time are notably reduced, by up to 15% and 76%, respectively. On the other hand, H-MPC utilizes one NLMPC in the planning layer and another one in the operating layer. The optimal battery temperature from the MPC in the planning layer serves as a reference for the MPC in the operating layer. Notably, the two NLMPC in the two layers adopt different prediction horizons and sampling times. Simulation results demonstrate that H-MPC achieves significant reductions in BTM consumption and execution time, by up to 8% and 89%.

关键词： battery thermal management model predictive control adaptive model predictive control hierarchical model predictive control electric vehicle

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Enhancing Autonomous Vehicles System Security: Advanced Attack Detection for Robust Safeguarding 10

Enhancing Autonomous Vehicles System Security: Advanced Atta...

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10th International Conference on control, Decision and Information Technologies (CoDIT)

作者： Akbarian, Fatemeh Papageorgiou, Dimitrios Chamideh, Seyedezahra Mikkelsen, Jeppe Heini Karstensen, Peter Iwer Hoedt Kihl, Maria Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden Tech Univ Denmark Dept Elect Engn Elektrovej 326 DK-2800 Lyngby Denmark

ISBN: (纸本)9798350373981;9798350373974

The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical model predictive control scheme, as well as, vehicle-to-vehicle communication in order to generate redundancy of collision risk information. The efficacy of the method is demonstrated in Eclipse Simulation of Urban Mobility (SUMO) considering the scenario of junction traffic management.

关键词： fault diagnosis hierarchical model predictive control autonomous vehicles attack detection

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Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles

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ENERGY 2023年第1期266卷

作者： Hou, Shengyan Yin, Hai Xu, Fuguo Benjamin, Pla Gao, Jinwu Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Jilin Univ Dept Control Sci & Engn Changchun 130025 Peoples R China Tokyo City Univ Adv Res Labs Tokyo 1588557 Japan Univ Politecn Valencia CMT Motores Term Valencia 46022 Spain Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China

A reliable energy optimization strategy incorporating vehicle connectivity is of great importance for the performance enhancement of fuel cell electric vehicles. In this paper, a multihorizon hierarchical model predictive control framework is proposed, which reduces energy consumption while incorporating fuel cell lifetime management though real-time speed preview. Specifically, the trajectories of battery state of charge are explored via convex optimization in the upper layer to provide a suboptimal reference for real-time optimization, and the concept of multihorizon is introduced into convex optimization for the first time. At the lower level, an equivalent consumption minimum strategy-based model predictive control is designed, which improves energy utilization efficiency and prolongs the lifetime of fuel cells. The main contribution of this paper is to use multihorizon optimization to solve the energy optimization and lifetime management of fuel cell electric vehicles over different timescales. Experimental results show that the proposed strategy has great potential in cost saving, which can reduce 10.10% to 16.95% of the total cost in real driving conditions compared with the rule-based strategy.

关键词： Connected fuel cell electric vehicles Energy optimization hierarchical model predictive control Lifetime management Multihorizon optimization

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Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump✩

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APPLIED ENERGY 2023年第1期347卷

作者： Thorsteinsson, Simon Kalaee, Alex Arash Sand Vogler-Finck, Pierre Staermose, Henrik Lund Katic, Ivan Bendtsen, Jan Dimon Aalborg Univ Sect Automat & Control Aalborg Denmark Neogrid Technol ApS Aalborg Denmark Danish Technol Inst Taastrup Denmark

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical model predictive control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-17% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.

关键词： hierarchical model predictive control Heat pump Retrofit building control Real experiment Load shifting Demand side management

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model predictive control of Diesel Fuel Consumption and On-Road Emission Constraints

Model Predictive Control of Diesel Fuel Consumption and On-R...

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作者： Duraiarasan, Saravanan University of Michigan

学位级别：Ph.D., Doctor of Philosophy

Diesel engines continue to be an important powerplant in heavy-duty trucks due to their inherent advantages such as range, robustness to operating conditions, and wide range of torque deliverability. With the introduction of on-road-focused emission regulations, one would explore an adaptive calibration scheme, where the fuel efficiency is maximized subject to tailpipe NOx emission constraint. Nowadays, the engine calibration setpoints are a function of speed and load calibrated offline, considering various driving profiles and certified on a specific drive cycle. This thesis explores adjusting engine setpoints online to enable future integration with the trip ahead and a preview of the load affecting the thermal and, thus, the aftertreatment conditions. Recognizing that the engine-out NOx increases by more than 50 % for a corresponding decrease of 1 % in brake-specific fuel consumption (BSFC). It is essential to control the engine at the low BSFC conditions only when the selective catalytic reduction (SCR) is predicted to operate efficiently. Similarly, the engine operation should support the warm-up of the SCR and avoid the generation of NOx when the SCR cannot convert it. In this thesis, a hierarchical predictive engine and aftertreatment control architecture is designed to alter the engine setpoints to achieve the best fuel economy while the SCR effectively reduces the corresponding increase in engine-out NOx. The advantage of the model predictive controller in handling time-delayed systems addresses the slow thermo-chemical dynamics of SCR. To implement this controller in real-time, physics-based engine airpath, engine-out NOx emission, and aftertreatment thermodynamic models are developed. The hierarchical controller architecture consists of a supervisory thermal management controller with a long prediction horizon and an air path controller with short prediction horizon. The supervisory controller aims to improve and maintain the aftertreatment temperature ab

关键词： Diesel Engine Powertrain control Emissions control model predictive control hierarchical model predictive control Fuel Economy

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hierarchical and decentralised model predictive control of drinking water networks: application to Barcelona case study

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IET control THEORY AND APPLICATIONS 2012年第1期6卷 62-71页

作者： Ocampo-Martinez, C. Barcelli, D. Puig, V. Bemporad, A. CSIC UPC Inst Robot & Informat Ind Barcelona 08028 Spain Univ Siena Dept Informat Engn I-53100 Siena Italy Inst Adv Studies Lucca IMT Lucca Italy

A hierarchical and decentralised model predictive control (DMPC) strategy for drinking water networks (DWN) is proposed. The DWN is partitioned into a set of subnetworks using a partitioning algorithm that makes use of the topology of the network, historic information about the actuator usage and heuristics. A suboptimal DMPC strategy was derived, which consists in a set of MPC controllers, whose prediction model is a plant partition, where each element solves its control problem in a hierarchical order. A comparative simulation study between centralised MPC (CMPC) and DMPC approaches is developed using a case study, which consists in an aggregate version of the Barcelona DWN. Results have shown the effectiveness of the proposed DMPC approach in terms of the scalability of computations with an acceptable admissible loss of performance in all the considered scenarios.

关键词： hierarchical model predictive control hierarchical systems centralised control Multivariable control systems predictive control multivariable control systems network topology water supply Actuating and final control devices centralised MPC partitioning algorithm decentralised control suboptimal DMPC strategy DWN Optimal control Natural resources and environmental control drinking water networks actuator decentralised model predictive control actuators

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B-POP: Bi-level parametric optimization toolbox

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COMPUTERS & CHEMICAL ENGINEERING 2019年 122卷 193-202页

作者： Avraamidou, Styliani Pistikopoulos, Efstratios N. Imperial Coll London Dept Chem Engn London England Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA

In this manuscript we present B-POP, a MATLAB toolbox for bi-level optimization through multiparametric programming. It features i) bi-level programming solvers for linear and quadratic programming problems, and their mixed-integer counter-parts, ii) a versatile problem generator capable of creating random bi-level problems of arbitrary size, and iii) a library of bi-level programming test problems. The features of B-POP are demonstrated through detailed computational studies showing the capabilities and the scalability of the embedded algorithms. Moreover, two applications, i) a supply chain planning problem, and ii) a hierarchical model predictive control of a reactor system are chosen to show the applicability of bi-level programming and B-POP. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Bi-level optimization Multi-parametric programming hierarchical model predictive control Supply chain planning

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Modular energy cost optimization for buildings with integrated microgrid

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APPLIED ENERGY 2017年 197卷 14-28页

作者： Lesic, Vinko Martincevic, Anita Vasak, Mario Univ Zagreb Lab Renewable Energy Syst Fac Elect Engn & Comp Unska 3 HR-10000 Zagreb Croatia

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants. The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energy price profiles. This is a key element for technology separation, replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated micro grid. The approach stands out with significant performance improvements compared to various non coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations, which are suitable for deployment as short and long-term zero-energy building investments. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Buildings energy management Zone comfort control Energy efficiency Microgrid energy management Volatile pricing hierarchical model predictive control

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A Novel predictive Energy Management System 11

A Novel Predictive Energy Management System

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11th UKSim-AMSS European modelling Symposium on Computer modelling and Simulation (EMS)

作者： Rehlaender, Philipp Kemper, Philipp Schwung, Andreas Witkowski, Ulf South Westphalia Univ Appl Sci Dept Elect & Circuit Technol Dept Automat Technol Soest Germany

ISBN: (纸本)9781538614105

Global warming, the decline of oil resources, as well as the introduction of emission regulations, has led to a research focus in new drive technologies. Whereas hybrid electric vehicles (HEV) can only be considered as a transition technology, electric vehicles (EV) and fuel cell vehicles (FCV) are zero emission technologies;HEV are only considered a bridge technology. FCV enable longer ranges and faster refills at the cost of a moderate increase in fuel cost. Challenges for an overall acceptance of fuel cell vehicles are the degradation mechanisms of the fuel cell that lead to a very limited lifetime. Current research focuses on energy management strategies to reduce the overall energy demand by predicting the vehicles coarse power consumption on the overall trip. This prediction is used to optimize the control of the drivetrain components. This paper reviews energy management strategies and points out the lack of research in the prediction of short-term speed changes. The knowledge of short-term speed changes can drastically reduce degradation mechanisms by avoiding power fluctuations in the fuel cell as well as in the battery. The proposed hierarchical model predictive control strategy is able to incorporate the knowledge of long-term energy consumption to minimize the energy demand as well as the short-term speed predictions to avoid degradation mechanisms. The suggested system will lead to longer lasting vehicles and to a better acceptance of fuel cell vehicles. To incorporate a drive data pool, this paper describes the development of onboard micro trips by evaluating the driving information and splitting them into sub trips.

关键词： energy management systems fuel cell vehicles speed prediction model predictive control hierarchical model predictive control degradation mechanisms

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