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multi-objective optimization and Analysis of Asymmetric Hybrid-Magnet Offset Motor for Electric Vehicles

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第1期19卷 109-118页

作者： Shi, Liwei Lv, Bingchang Li, Lintao Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255049 Shandong Peoples R China

Aiming at the high cost of permanent magnet synchronous motor and the reduction of motor output performance caused by high cogging torque and torque ripple, an asymmetric hybrid-magnet offset motor is proposed. Based on the winding function theory and the equivalent magnetic circuit method, the expressions of stator and rotor magnetic potential are derived respectively, and the analytical model of torque ripple is established by combining with the Lorentz force law. By using the magnetic pole offset method, the phase offset model of the cogging torque is established, and the analytical expressions of the cogging torque and the magnetic pole offset angle are derived. The results of the simulation and experiments show that the asymmetric hybrid-magnet offset motor has lower no-load back-EMF harmonic content and rare-earth material ratio than the traditional permanent magnet motor, and its performance in terms of the cogging torque and the torque ripple is better than that of the traditional permanent magnet motor. (c) 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： permanent magnet motor asymmetric hybrid-magnet offset multi-objective optimization electromagnetic performance analysis

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multi-objective optimization of loading path for sheet hydroforming of tank bottom

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年第5期238卷 625-637页

作者： Zhang, Zaifang Zhou, Liang Xu, Feng Sun, Xiwu Zhang, Zhichao Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Shanghai Aerosp Equipments Manufacture Co LTD Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat 333 Nanchen Rd Shanghai 200444 Peoples R China

As a critical component of the propellant tank, the tank bottom is subjected to complex loads such as internal pressure and vibration and has high requirements for structural load-bearing capacity. Hydroforming deep drawing is one of the techniques for the integral forming of the tank bottom. As the tank bottom is a large-size thin-walled structure, defects such as cracks and wrinkles are prone to occur during the hydroforming deep drawing process. Aiming at reducing these defects, the hydraulic pressure loading path and blank holder force loading path of the hydroforming deep drawing process are studied, and a multi-objective optimization method is proposed to improve the surface accuracy and thickness distribution uniformity of the tank bottom. The complex loading path curve optimization problem is transformed into a functional relationship between hydraulic pressure and blank holder force with time. The hydraulic pressure and blank holder force at each time node are used as design variables, and the maximum wall thickness reduction rate, rupture trend factor, wrinkle height, and wrinkle trend factor are used as optimization targets. The radial basis function (RBF) neural network is used to establish the approximate model between the loading path and the optimization target, and the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the solution. Taking the hemispherical tank bottom as an example, the optimal hydraulic pressure loading path and blank holder force loading path are obtained, and the quality of the formed part is improved.

关键词： multi-objective optimization storage tank hydroforming Kriging

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multi-objective optimization and thermodynamic assessment of a solar unit with a novel tube shape equipped with a helical tape

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APPLIED THERMAL ENGINEERING 2024年 254卷

作者： Mohammadzadeh, M. Anisi, A. Sheikholeslami, M. Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA Babol Noshirvani Univ Technol Dept Mech Engn Babol Iran Babol Noshirvani Univ Technol Renewable Energy Syst & Nanofluid Applicat Heat Tr Babol Iran

The performance of several heat transfer mechanisms, including solar energy sources, is being improved through optimization, which also effectively supports the most efficient feasible design. A numerical study has been done on a turbulator-enhanced flat plate solar collector (FPSC) while also introducing the utilization of a quad-lobed tube as a pioneering innovation in the system's design. The incorporation of nanoparticles into the working fluid was implemented to enhance performance, with aluminum oxide particles selected for this purpose. Furthermore, assessing the second law and analyzing exergy generation was employed to discern irreversibility within the system. optimization of both geometric and non-geometric elements has been accomplished through the integration of a multi-objective optimization (MOO) method with machine learning (ML). Random Forest (RF), LASSO Regression (LaR), and Support Vector Regression (SVR) are the machine learning models that were utilized in order to ascertain the correlations that exist between the system's inputs and outputs. According to the findings, RF was found to be the most appropriate algorithm for capturing the impacts of parameter variations. This was demonstrated by the high coefficient of determination (R2) 2 ) quantities that RF possessed, which were 0.95, 0.99, and 0.99 for friction factor (f), f ), exergy loss (Xd), X d ), and second law effectiveness (eta II), eta II ), respectively. However, the SVR and LaR algorithms showed significant deviations from the FPSC simulation results. The SVR algorithm achieved R2 2 values of 0.54 for f , 0.78 for X d , and 0.84 for eta II, II , while the LaR algorithm achieved R2 2 values of 0.65 for f , 0.99 for X d , and 0.98 for eta II. II . This research aims to find the optimal input parameters that minimize f and Xd d while maximizing eta II. II . Therefore, to determine the Pareto optimal solutions the non-dominated sorting genetic algorithm (NSGA-II) has been utilized. The r

关键词： Flat plate solar collector multi-objective optimization Nanofluid Heat transfer enhancement Random Forest NSGA-II

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multi-objective optimization analysis of air-cooled heat dissipation coupled with thermoelectric cooling of battery pack based on orthogonal design

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APPLIED THERMAL ENGINEERING 2024年 249卷

作者： Liu, Hongmin Xie, Jianchao Ma, Xukun Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China

As the energy supply core of electric vehicles (EVs), the battery's performance is closely related to its temperature. Therefore, the battery thermal management system (BTMS) plays a crucial role in ensuring the vehicle's driving safety and power performance. This paper proposes the air cooling as the primary heat dissipation method, combined with a semiconductor refrigeration sheet (SRS) to improve heat transfer and reduce local high temperature. Firstly, determine the optimum air volume with the U-type air-cooled structure. Additionally, orthogonal analysis is used to investigate the inlet and outlet locations, the front deflector position and length of the shunt chamber, and the rear deflector position on the air-cooling effect. Then, through multi-objective optimization, the optimal air-cooled structure is selected based on the air supply pressure drop. The chosen structure is the same-side dual-exit ventilation with a front deflector located 150 mm from the near-wind end wall and a length of 30 mm, and a rear deflector located 100 mm from the far-wind end wall. Finally, SRS coupled air cooling is used to dissipate the heat. Four SRSs are used to analyze the effect of mounting position and different currents on the battery pack. The findings suggest that the optimal placement for SRSs is parallel to the lower end of the battery module at the far-wind end. When SRSs pass a current of up to 0.4A at 37 degrees C, the entire battery module can complete 7200 s discharge with 0.5C. This leads to a decrease in the maximum temperature (Tmax) to 46.09 degrees C and a drop in the temperature uniformity index (delta T) to 1.36.

关键词： Battery module Air cooling Orthogonal analysis multi-objective optimization

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multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

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ENGINEERING COMPUTATIONS 2024年第6期41卷 1549-1571页

作者： Deng, Shuai Cheng, Xin Wu, Huachun Hu, Yefa Wuhan Univ Technol Wuhan Peoples R China Res Ctr Magnet Suspens Hubei Prov Engn Technol Wuhan Peoples R China

PurposeThe multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting ***/methodology/approachA multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current ***(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure ***/valuei) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. T

关键词： Active magnetic bearing (AMB) Electromagnetic actuators (EMAs) multi-objective optimization Non-dominated sorting genetic algorithm

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multi-objective optimization study applied to an ethanol fermentation of cashew apple juice

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BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第1期41卷 71-85页

作者： Correa, Isabela Barreto da Silva, Matheus de Almeida Rodrigues Santos, Lizandro de Sousa Univ Fed Fluminense Dept Chem & Petr Engn Rua Passo Patria 156 BR-24210240 Niteroi RJ Brazil

Recently, several works have focused on modeling and simulating the cashew apple juice fermentation process. However, multi-objective optimization (MOO) studies for constructing the Pareto set of this process have not yet been conducted. In this work, the model-based optimization of the fermentation of cashew apple juice was carried out using a MOO strategy based on the weighted sum method (WSM). The Nelder-Mead (NM) algorithm was implemented using Scilab software. In the proposed strategy, three parameters were simultaneously optimized: (i) the ethanol-substrate fed ratio, (ii) the ethanol-substrate consumed ratio and (iii) the volumetric ethanol productivity. The temperature and initial substrate concentration of the cashew juice, as well as the operating time, were used as decision variables. The simulation results were compared to data obtained from recent literature, such that the residual standard deviation (RSD) could be computed for different temperatures and initial concentrations of the substrate, ethanol, and biomass. The maximum value of RSD was 5.26% for the substrate concentration at 30 & DEG;C. The Pareto curve results showed that the process productivity could be adjusted with substrate consumption and that it is a valuable tool to avoid selecting operational points away from the optimal region.

关键词： multi-objective optimization Simulation Nonlinear programming

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multi-objective optimization of IPMSM for electric vehicles based on the combinatorial surrogate model and the hierarchical design method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 162卷

作者： Yu, Yinquan Liang, Chenglong Zeng, Dequan Hu, Yiming Yang, Jinwen East China Jiaotong Univ Inst Precis Machining & Intelligent Equipment Mfg 808 Shuanggang East St Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Sch Mechatron & Vehicle Engn 808 Shuanggang East St Nanchang 330013 Jiangxi Peoples R China Jiangxi Tongling Automot Technol Co LTD 2111 Yingbin Zhong Ave Nanchang 330052 Jiangxi Peoples R China

This paper investigates the optimization design of interior permanent magnet synchronous motors (IPMSM) for electric vehicles (EVs). The optimization process of IPMSM involves numerous design parameters, and the optimization objectives often conflict with each other, resulting in a vast design space and difficulties in establishing an accurate mathematical model. The traditional finite element analysis (FEA) optimization methods are time-consuming and computationally intensive, posing a significant challenge to achieving high-performance IPMSM with high torque, high efficiency, low vibration, and low losses. To address this issue, this paper proposes a multi-objective optimization of IPMSM for electric vehicles based on the combinatorial surrogate model and the hierarchical design method. Firstly, a comprehensive sensitivity coefficient method is employed to categorize design variables into two layers: high-sensitivity design variables (HSDVs) and low-sensitivity design variables (LSDVs). Secondly, using the improved Latin hypercube sampling (LHS) method to extract sample data, a highprecision combined surrogate model (RSM + Kriging) is constructed and combined with the non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm to optimize HSDVs. Meanwhile, the fuzzy inference Taguchi method (FITM) is utilized to optimize the LSDVs. Finally, the performance of the IPMSM before and after optimization has been analyzed through the FEA method, and different optimization methods were introduced for comparison. The results show that compared to other optimization methods, the optimization approach proposed in this paper can effectively enhance the overall performance of the IPMSM. The average torque of the optimized IPMSM increased by 5.22%, the torque ripple decreased by 77.64%, and the total losses were reduced by 6.21 %. Furthermore, compared to the traditional FEA method, this method reduces optimization time and improves optimization efficiency with

关键词： IPMSM EV Sensitivity analysis Surrogate model Fuzzy inference Taguchi method multi-objective optimization

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multi-objective optimization enabling CFRP energy-efficient milling based on deep reinforcement learning

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APPLIED INTELLIGENCE 2024年第23期54卷 12531-12557页

作者： Zhang, Meihang Zhang, Hua Yan, Wei Zhang, Lin Jiang, Zhigang Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Res Inst Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Sch Automot & Traff Engn Wuhan 430065 Peoples R China

The expanding application of Carbon Fiber Reinforced Polymer (CFRP) in industries is drawing increasing attention to energy efficiency improvement and cost reducing during the secondary processing, particularly in milling. Machining parameter optimization is a practical and economical way to achieve this goal. However, the unclear milling mechanism and dynamic machining conditions of CFRP make it challenging. To fill this gap, this paper proposes a DRL-based approach that integrates physics-guided Transformer networks with Twin Delayed Deep Deterministic Policy Gradient (PGTTD3) to optimize CFRP milling parameters with multi-objectives. Firstly, a PG-Transformer-based CFRP milling energy consumption model is proposed, which modifies the existing De-stationary Attention module by integrating external physical variables to enhance modeling accuracy and efficiency. Secondly, a multi-objective optimization model considering energy consumption, milling time and machining cost for CFRP milling is formulated and mapped to a Markov Decision Process, and a reward function is designed. Thirdly, a PGTTD3 approach is proposed for dynamic parameter decision-making, incorporating a time difference strategy to enhance agent training stability and online adjustment reliability. The experimental results show that the proposed method reduces energy consumption, milling time and machining cost by 10.98%, 3.012%, and 14.56% in CFRP milling respectively, compared to the actual averages. The proposed algorithm exhibits excellent performance metrics when compared to state-of-the-art optimization algorithms, with an average improvement in optimization efficiency of over 20% and a maximum enhancement of 88.66%.

关键词： PGTTD3 Deep Reinforcement Learning (DRL) CFRP Energy-efficient milling multi-objective optimization

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multi-objective optimization of L-shaped fins in rectangular phase change energy storage units based on genetic algorithm

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JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Kong, Jiachang Zhang, Li East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China

Phase change energy storage technology holds immense potential in the field of energy storage, and enhancing the efficiency of energy storage systems has long been a focal point of industry attention. This study aims to optimize the design of an L-shaped fin structure to improve the melting rate of the phase change material (PCM) within a rectangular phase change energy storage unit and enhance the overall system performance. Through the utilization of numerical simulations and the response surface method (RSM), the influence of fin design parameters - specifically, the length and thickness of the main segment and the length and thickness of the branch segment - on the energy storage per unit mass (Em) and energy storage efficiency (Pt) of the energy storage unit is evaluated. The results reveal that the length of both the main and branch segments of the L-shaped fin significantly impacts the melting rate of the PCM, whereas the thickness of these segments has a comparatively lesser effect. Additionally, when the length of the main segment reaches a sufficient value, increasing the length of the branch segment effectively addresses the challenge of PCM melting difficulties at the bottom of the rectangular phase change cavity caused by natural convection. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is employed for the multi-objective optimization of the L-shaped fin shape, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with entropy weighting is applied for decision-making to determine the optimal solution. Notably, when the weights of Em and Pt are set at 44.9 % and 55.1 % respectively, the overall performance of the energy storage unit is optimized. The Pareto-optimal decision solution exhibits a significant improvement of nearly threefold in Pt compared to the case without fins, while the reduction in Em is only 5.24 %. This design approach presents a novel perspective for determining fin parameters in phase change energy s

关键词： Phase change material Fin design multi-objective optimization CFD NSGA-II TOPSIS

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multi-objective optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition

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ENERGIES 2024年第6期17卷 1334页

作者： Lyu, Renzhi Wang, Zhenpo Zhang, Zhaosheng Beijing Inst Technol Sch Mech Engn Beijing 100811 Peoples R China

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9-5.6%, 1.7-2.6%, and 1.2-1.6% SOC, saving 5.7-6.45%, 5.9-6.67%, and 6.1-6.67% in lifespan loss, and reducing hydrogen consumption by 3.0-7.1%, 2.8-4.4%, and 1.0-3.0%, respectively.

关键词： fuel cell hybrid electric trucks energy management strategy reinforcement learning multi-objective optimization

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