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multi-level optimization of thermal management systems with compact large-bend configurations in hybrid electric vehicles

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 164卷

作者： Yang, Yu Xu, Qianghui Zheng, Siyu Yang, Tao Wang, Yongzhen Zhang, Pengcheng Shen, Jun Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

The thermal management system design for specialized hybrid vehicles presents unique challenges, particularly due to the presence of a 90 degrees bend in the airflow path. This study proposes a multi-level modelling and optimization methodology aimed at meeting cooling requirements while minimizing airflow pressure drop across all system components, despite spatial constraints. The integrated modelling framework incorporates a zerodimensional heat transfer model, a one-dimensional model for non-uniform airflow, and three-dimensional computational fluid dynamics simulations. Coupled with the NSGA-II algorithm, the optimization addresses multiscale structures, including fin design, radiator arrangement, radiator dimensions, and air duct size. The 90 degrees bend induces substantial non-uniform flux across the radiator's airflow face, reducing heat transfer performance by up to 10 % compared to uniform airflow. However, adjusting the radiator thickness and expanding the air duct can mitigate this non-uniformity, reducing it by up to 40 %. Results indicate that the total pressure drop initially decreases and then increases as the air duct expands and the radiator thins, due to the interplay of pressure drop across components, allowing for the identification of an optimal radiator height. This optimization framework and the insights gained provide valuable guidance for developing integrated thermal management systems for large-bend configurations in hybrid electric vehicles.

关键词： Electric vehicles Thermal management system multi-level optimization MRF simulation 90 degrees bend flow

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A multi-level optimization design and intelligent control framework for fuel cell-based combined heat and power systems

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ENERGY CONVERSION AND MANAGEMENT 2025年 325卷

作者： Cheng, Jiabao Yang, Fubin Zhang, Hongguang Wang, Nanqiao Yan, Yinlian Xu, Yonghong Beijing Univ Technol Coll Mech & Energy Engn Beijing 100124 Peoples R China Beijing Informat Sci & Technol Univ Coll Mech & Elect Engn Beijing 100192 Peoples R China

Fuel cell systems have attracted significant attention in the field of residential energy due to their high efficiency and environmentally friendly characteristics. However, the inherent coupling of its thermoelectric output limits the flexibility of the system to meet diverse residential energy needs. This study proposes a combined heat and power system based on a proton exchange membrane fuel cell integrated with an organic Rankine cycle and heat pump, and builds a multi-level optimization design and intelligent control framework. Through this framework, current density and split ratio were identified as two key operational parameters affecting heat and power output. To enhance the precision and adaptability of system control, a neural network evaluation metric based on sensitivity weighting was introduced to optimize the hyperparameters of the Back Propagation neural network controller. This approach significantly improved the accuracy of the control model and system performance. Based on the optimized neural network controller, an intelligent control strategy oriented towards heat demand was realized, effectively meeting users' dynamic needs. Results show that under typical demand conditions, the system achieved significant performance improvement: maximum thermal efficiency of 47.48 %, maximum electrical efficiency of 36.73 %, maximum hydrogen consumption rate of 1.3 g/s, and minimum levelized cost of energy of 0.4183 $/kW & sdot;h- 1. This research provides valuable theoretical guidance for the optimization design and operations management of fuel cell-based combined heat and power systems.

关键词： Combined heat and power multi-level optimization Intelligent control PEMFC

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multi-level optimization of biomass chemical looping gasification process based on composite oxygen carrier

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CHEMICAL ENGINEERING SCIENCE 2024年 287卷

作者： Cui, Zhe Sun, Yang Zhang, Jiawei Liu, Bin Tian, Wende Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Peoples R China

Biomass chemical looping gasification (BCLG) is one of the essential technology to utilize renewable resources, but the current lack of in-depth research on BCLG makes it difficult for practical industrial applications. In this paper, the combination of quantum chemical calculation, ReaxFF molecular dynamic (ReaxFF MD) simulation, and process simulation is used to conduct an in-depth study of BCLG process. Firstly, three composite oxygen carriers (CuFe2O4, CoFe2O4, and Fe3O4) are selected by density functional theory (DFT), which finally indicates that CuFe2O4 is the best composite oxygen carrier for BCLG. Then, the reaction mechanism between syngas and oxygen carrier surface is studied at the microscopic level, which validates the high performance of CuFe2O4. After determining the oxygen carrier, the reaction mechanism of BCLG and the effect of temperature on the product distribution are studied by ReaxFF MD simulation. Finally, the process simulation of BCLG is constructed based on the results discussed above.

关键词： BCLG Composite oxygen carrier multi-level optimization ReaxFF MD Process optimization

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multi-level optimization Based on Approximate Models for Double-Sided Linear Flux Switching Permanent Magnet Motors 21

Multi-level Optimization Based on Approximate Models for Dou...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Zhao, Qiankai Wen, Cheng Huang, Lei Li, Yuan Southeast Univ Sch Elect Engn Nanjing Peoples R China Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350348958;9798350348965

In this paper, a novel multi-level optimization method for DLFSPM is proposed, to improve the efficiency of multi-objective optimization. To split the high dimension parameters space, a combination of comprehensive sensitivity analysis, correlation analysis, and cross-factor variance analysis is employed to evaluate the effect of the machine's structure parameters towards the optimization objective. Moreover, an approximate model for this machine based on BOHB-RF and a least squares method of approximation is applied to reduce the computational complexity for motor modeling. Then, Nondominated sorting genetic algorithm-II (NSGA-.) is applied to optimize the DLFSPM with a level-by-level multi-objective optimization design, that to obtain the global optimal performance of this motor with high thrust density, low thrust ripple and low mover loss. Finally, the results of FEA prove the effectiveness for the multi-level optimization method.

关键词： linear motor multi-level optimization multi-objective optimization approximation model NSGA-II

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Research on topology optimization of liquid-cooled plates based on multi-level optimization

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 155卷

作者： Xie, Liyao He, Zhaowei Zhao, Yulong Vulin, Domagoj Bhayo, Barkat Ali Hebei Univ Technol Sch Energy & Environm Engn Tianjin 300401 Peoples R China Univ Zagreb Fac Min Geol & Petr Engn Pierottijeva 6 Zagreb 10000 Croatia Lappeenranta Lahti Univ Technol LUT Sch Energy Syst Lappeenranta Finland

This paper employs topology optimization techniques to enhance the performance of liquid-cooled plates while investigating the optimization process and its outcomes. A multilevel optimization strategy is implemented to refine the accuracy and smoothness of the optimization process. The study demonstrates that expanding the range of penalty factors and refining the granularity of penalty factors can effectively enhance the capability of multilevel optimization in suppressing the formation of intermediate densities. Furthermore, a multi-objective function is utilized to strike a balance between the heat transfer efficiency and the hydraulic performance of the liquid-cooled plate. Ultimately, after considering aspects related to heat transfer and hydraulic performance, the research finds that compared to traditional straight-channel liquid-cooled plates, liquid-cooled plates featuring streamlined multi-branched flow channels not only increase the heat transfer area but also reduce pressure drop. As a result, this design improves the temperature uniformity of the liquid-cooled plate and enhances the velocity uniformity of the cooling fluid. At different inlet velocities, the topology-optimized liquidcooled plate exhibits a reduction in both effective thermal resistance and pumping power compared to the traditional straight-channel liquid-cooled plate.

关键词： Topology optimization Liquid-cooled plate multi-level optimization multi-objective function Thermal and hydraulic performance

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A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

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ENERGY 2011年第6期36卷 3933-3943页

作者： Kim, Kihyung von Spakovsky, Michael R. Wang, M. Nelson, Douglas J. Virginia Polytech Inst & State Univ Dept Mech Engn Energy Syst Res Ctr Blacksburg VA 24061 USA

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer's objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies. To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell (PEMFC)system multi-level optimization Physical decomposition Uncertainty quantification Response sensitivity analysis Dynamic optimization

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A state-space modeling approach and multi-level optimization algorithm for predictive control of multi-zone buildings with mixed-mode cooling

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BUILDING AND ENVIRONMENT 2014年 80卷 259-273页

作者： Hu, Jianjun Karava, Panagiota Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Div Construct Engn & Management W Lafayette IN 47907 USA

The paper presents a control-oriented modeling approach for multi-zone buildings with mixed-mode cooling, based on the linear state-space representation with varying coefficient matrices. Key features are the time-variant thermal resistances, associated with the heat extraction due to airflow, calculated using an airflow network model. This approach was validated with experimental data collected in a two-zone test-building under four operation modes. A forward linear time-variant state-space (LTV-SS) model, developed based on first principles, was then used as a true representation of the building, to identify the parameters of a low-order LTV-SS gray-box model. The low-order model can predict the building thermal dynamics with sufficient accuracy with a root mean square error (RMSE) of 0.58 degrees C for the air and 1.08 degrees C for the area-weighted mean surface temperature in the south direct gain zone. Furthermore, the study develops a progressive refinement (ProRe) optimization method, following the multi-level optimization topology and branch and bound decision trimming strategy, to find sequences of binary (open/close) decisions for the motorized windows. Due to the significant improvement in computing time, the models and algorithms presented in this paper enable long-term simulation for MPC performance evaluation and implementation of predictive strategies in real controllers. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mixed-mode cooling Model-predictive control multi-level optimization State-space model System identification

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Robust multi-objective and multi-level optimization of complex mechanical structures

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2011年第7期25卷 2444-2461页

作者： Ghanmi, S. Guedri, M. Bouazizi, M. -L. Bouhaddi, N. Preparatory Engn Inst Nabeul IPEIN Mrezgua 8000 Nabeul Tunisia Univ Franche Comte FEMTO ST Inst UMR 6174 Appl Mech Lab R Chaleat F-25000 Besancon France

This paper presents a new approach to robust multi-objective and multi-level optimization of the design of complex mechanical structures. The optimization is at two levels: system and elements. At system-level, the robust multi-objective problem has four cost functions: on the one hand the minimization of the global mass and displacement at a fixed point of the mechanical structure, and on the other hand the maximization of both the robustness and the displacement of the mass. At element-level the robust multi-objective problem has two cost functions: minimization of the element mass and maximization of its robustness. A robust condensation technique, based on an enriched Karhunen-Loeve condensation, is used for complex structures which require a large finite element model. In contrast to existing formulations, this new approach takes into account uncertainties in the design parameters at system-level and element-level. It also allows for the task sharing which is commonly used in structural engineering. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization multi-level optimization Uncertainties Genetic algorithm Robustness Karhunen-Loeve transformation

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Control Simulation of Flapping-Wing Micro Aerial Vehicle Based on multi-level optimization Model Predictive Control

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IEEE ACCESS 2024年 12卷 40700-40709页

作者： Zheng, Hongyu Chen, Wengang Xie, Fangfang Changzhou Inst Technol Sch Aeronaut & Mech Engn Changzhou 213000 Jiangsu Peoples R China Zhejiang Univ Dept Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China

In this paper, a control algorithm using multi-level optimization and model predictive control is proposed to solve the conflict between the computational cost and control accuracy of the flapping-wing micro aerial vehicle. First, a quasi-steady model is established to evaluate the aerodynamic forces and moments of the flapping-wing vehicle, and the forces and moments are then optimized to meet the control requirements based on classical model predictive control. Then an optimization module based on the quasi-steady model is introduced to optimize the kinematic parameters to achieve the optimal forces and moments, thus it decomposes the complex optimization problem of the classical model predictive control into two sub-problems. Compared with classical proportional-integral-derivative control and model predictive control, the response speed of the multi-level optimization model predictive control is effectively improved while maintaining high accuracy. Finally, the effectiveness and stability of the control framework are validated by the control simulations of set-point and motion-tracking control.

关键词： Flapping wing vehicle model predictive control multi-level optimization quasi-steady model

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Metamodeling and the Critic-based approach to multi-level optimization

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NEURAL NETWORKS 2012年 32卷 179-185页

作者： Werbos, Ludmilla Kozma, Robert Silva-Lugo, Rodrigo Pazienza, Giovanni E. Werbos, Paul J. IntControl LLC Memphis TN 38152 USA Univ Memphis CLION Memphis TN 38152 USA Natl Sci Fdn Arlington VA 22230 USA

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Approximate dynamic programming multi-level optimization Metamodeling Adaptive critic

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