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Research on variable domain three-point energy management strategy and parameter adaptive optimization for range-extended electric vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Liu, Hanwu Lei, Yulong Sun, Wencai Chang, Cheng Jiang, Wei Liu, Yuwei Jilin Univ Transportat Coll 5988 Renmin St Changchun 130022 Jilin Peoples R China Jilin Univ Automot Engn Coll Changchun Jilin Peoples R China Henan Inst Technol Sch Vehicle & Traff Engn Xinxiang Henan Peoples R China

This study proposes a variable domain three-point energy management strategy (VDT-PEMS) for range-extended electric vehicles (R-EEV), which focuses on achieving energy conservation, emission reduction, and battery life extension through the refinement of the auxiliary power unit (APU) variable operation domain and targeted parameter adaptive adjustment. The strategy divides the power interval into variable domain three-point and APU system operates at constant speed and variable torque to achieve effective tracking of the required power. And further designed an adaptive parameter optimization module, which adaptively adjusts the variable domain power coverage of the working area at various speeds based on fuzzy controller, enabling the APU system to more flexibly cope with complex and changing working conditions. And improving the adaptability and stability of the system by refining the variable operating range of APU and implementing targeted parameter adaptive adjustment, dynamic optimization of the working mode strategy has been achieved. Subsequently, the study aimed to solved the multi-objective optimization problem, VDT-PEMS was designed to combines offline optimization based on particle swarm optimization algorithm and online adaptive parameter optimization module. A series of simulated pareto solution sets were used for comparative analysis and initial parameter calibration. The results showed that the optimization results under a single objective cannot achieve the optimal comprehensive performance, and the proposed VDT-PEMS significantly improves fuel economy and reduces emissions. The effectiveness and feasibility of the proposed strategy were verified through bench testing. The discovery provides important theoretical basis for the management and maintenance of power batteries, which helps to better protect batteries and extend their service life in practical applications.

关键词： Range-extended electric vehicle variable domain energy management parameter adaptive optimization fuzzy control pso algorithm

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Novel Approach for Reducing Train Wheel and Brake Shoe Wear Using Speed Profile Optimization

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TRANSPORTATION RESEARCH RECORD 2024年第9期2678卷 241-250页

作者： Adeli, Hasan Mirabadi, Ahmad Yousefi, Shahin Fazel, Saeed Iran Univ Sci & Technol Sch Railway Engn Tehran Iran

In this article, we propose a new approach for generating a train's speed profile that takes into account the wear caused by the wheel-rail interaction and brake shoe wear. This method involves modeling the train and its path and simulating the train's movement to calculate the wear caused by the wheel-rail interaction and brake shoe wear resulting from mechanical braking. Finally, using the particle swarm optimization algorithm, an optimal speed profile is produced, which minimizes the total wear. The results of implementing this method on the Shahriar-Roodshour route demonstrate that using an optimal speed profile results in a significant wear reduction of over 40%.

关键词： speed profile optimization wear reduction railway pso algorithm wheel and brake wear

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NSNpso-INC: A Simplified Particle Swarm Optimization algorithm for Photovoltaic MPPT Combining Natural Selection and Conductivity Incremental Approach

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IEEE ACCESS 2024年 12卷 137760-137774页

作者： Xu, Shi-Zhou Zhong, Yi-Ming Henan Normal Univ Coll Elect & Elect Engn Xinxiang 453002 Peoples R China

To enhance photovoltaic (PV) system performance under various environmental conditions, this paper proposes a hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates the Incremental Conductance (INC) method with an improved Particle Swarm Optimization (pso) algorithm. The proposed approach simplifies the pso velocity update process and uses natural selection to filter less adaptive particles. Leveraging the rapid convergence of the INC method, this hybrid algorithm effectively tracks the maximum power point (MPP). Simulation results show that under static partial shading conditions, the NSNpso-INC algorithm achieves a stable MPPT efficiency of 99.9%, with a 68.18% faster convergence than conventional pso and more stable performance than traditional INC. Under dynamic irradiance conditions, NSNpso-INC adapts quickly, improving convergence speed by 93.33% over conventional pso. Experimental results further demonstrate that NSNpso-INC reaches steady state fastest, with an 85.52% improvement in convergence speed over traditional pso. The algorithm maintains a stable power output around 550W even under cloudy conditions, significantly enhancing PV energy conversion efficiency and providing valuable insights for future MPPT algorithm development.

关键词： PV array PV array MPPT MPPT pso algorithm pso algorithm boost converter boost converter

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Non-linear parametric vibration of the laminated composite shallow shells including primary and 1:2 internal resonances

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第15期31卷 3444-3475页

作者： Foroutan, Kamran Dai, Liming Zhao, Haixing Qinghai Normal Univ Sino Canada Res Ctr Computat & Math Xining Qinghai Peoples R China Univ Regina Ind Syst Engn Regina SK Canada

This research aims to study the non-linear parametric vibration of laminated composite shallow (LCS) shells with the optimal fiber angles exposed to external and parametric excitations, including primary and 1:2 internal resonances. In this regard, optimal fiber angles are found with implementations of the P-T method for the objective functions and utilization of the particle swarm optimization (pso). Also, the non-linear model of the shallow shells is established based on the stress function and the first-order shear deformation theory (FSDT). According to FSDT, Hooke's law, von-Karman equation, Hamilton's principle, and Galerkin method, two-degree-of-freedom non-linear ordinary differential governing equations are discretized.

关键词： Laminated composite shallow shells non-linear parametric vibration chaos primary and internal resonances pso algorithm P-T method multiple scales method

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A novel framework for calibrating DEM parameters: A case study of sand and soil-rock mixture

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COMPUTERS AND GEOTECHNICS 2024年 174卷

作者： Hu, Yangyu Lu, Ye Shanghai Univ Dept Civil Engn 99 Shangda Rd Shanghai 200444 Peoples R China

The discrete element method (DEM) has been widely used to simulate geotechnical works, e.g. slopes, embankments, tunnels, pipelines, etc. since the last decade. However, how to choose the appropriate input parameters still remains problematic and greatly affects the accuracy of DEM modelling results. One of the common practice is to calibrate DEM parameters using lab or in-situ tests. Such operation is largely dependent on personal judgement and thus induces errors to the numerical modeling results. Therefore, a novel framework based on particle swarm optimization (pso) algorithm was proposed in this study, which could calibrate parameters using 3 or more macro-indicators. The framework was written in Python, which performed automatic calibration process and thus minimized involvement of personal judgement. To validate its effectiveness, the framework was applied to the simulation of biaxial tests on sand first. The calibration results showed that similar parameters were yielded when different initial values were fed to the framework. Then, the framework was applied to simulation of soil-rock mixture (S-RM), and the calibration process was effective as well. These two cases proved that the proposed framework was an efficient tool for DEM parameter calibration and largely reduced computation errors resulting from personal judgement.

关键词： Discrete element method Parameter calibration pso algorithm Soil-rock mixture

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Hybrid Energy Storage Strategy Based on MPC and pso-VMD to Stabilize Wind Power Fluctuation 7

Hybrid Energy Storage Strategy Based on MPC and PSO-VMD to S...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Huo, Junda Wang, Yi Meng, Jianhui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding Peoples R China

ISBN: (纸本)9798350375794;9798350375800

In order to stabilize the original power fluctuation of wind power and avoid threatening the stable operation of power grid, a strategy of applying hybrid energy storage to stabilize wind power fluctuation is proposed. Firstly, the model predictive control is used to stabilize the wind power fluctuation in real time, and the wind power grid-connected power and the total output of the hybrid energy storage system that meet the requirements are obtained. Then, pso algorithm is used to optimize the parameters of VMD algorithm, and VMD algorithm is used to realize the initial allocation of HESS power. Finally, aiming at the problem of SOC over-limit of energy storage, the initial distribution instruction is optimized by fuzzy control method. The results of the example analysis show that the strategy proposed in this paper can effectively solve the fluctuation problem of the original power of wind power and protect the energy storage.

关键词： model predictive control pso algorithm variational mode decomposition fuzzy control

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Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model

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PETROLEUM SCIENCE AND TECHNOLOGY 2025年

作者： Fang, Xinjie Hu, Zhongzhi Song, Wei Zhou, Yan Tan, Yuelong Sichuan Univ Sci & Engn Coll Mech Engn Yibin 644000 Peoples R China Jidong Oilfield Co Petrochina Co Ltd Tangshan Peoples R China CNPC Daqing Drilling Engn Co Daqing Peoples R China

Accurate prediction of the rate of penetration (ROP) is of great significance for efficient drilling. The traditional ROP prediction model mostly ignores the influence of formation, and takes the term estimated downhole weight on bit (TTEDWOB) as the real downhole weight on bit (WOB) input model, which has a large prediction error. In this study, we modified TTEDWOB, and then proposed a back propagation (BP) neural network ROP prediction model based on particle swarm optimization (pso) on the basis of formation classification. The results show that the error of ROP prediction model is reduced by 7.7% on average in different formation experiments after the correction of TTEDWOB;The prediction accuracy of the model after formation classification is increased by 9.5%;The model is applied to two wells in different blocks, and the average relative errors are 8.51% and 24.78% respectively. The difference between our work and the previous work is that we consider the correction of TTEDWOB and formation conditions. Our suggestion is that it is necessary for researchers to consider TTEDWOB correction and formation factors when establishing ROP prediction models, because doing so will help improve the prediction accuracy of the model.

关键词： BP neural network machine learning pso algorithm ROP prediction WOB

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Investigating the effect of various types of components in optimal designing of a solar/wind/storage hybrid system

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JOURNAL OF ENERGY STORAGE 2025年 110卷

作者： Ghanbari, Kamyar Maleki, Akbar Ochbelagh, Dariush Rezaei Amirkabir Univ Technol Dept Energy Engn & Phys Tehran Iran Shahrood Univ Technol Fac Mech Engn Shahrood Iran

The growing global population and increasing scarcity of conventional fuels raise concerns about meeting power demand, particularly in remote areas with limited grid access. Hybrid renewable microgrids offer a reliable and cost-effective solution to this challenge. These systems typically integrate multiple components, each with varying models and technologies suited to specific conditions. Optimizing such systems and selecting appropriate technologies are critical for enhancing their performance and ensuring cost-effectiveness. This paper focuses on the optimal design, sizing, and technology selection for a hybrid renewable microgrid comprising batteries, wind turbines, and photovoltaic panels. To capture the impact of technology choices, three distinct models for each component were selected, resulting in 27 configurations optimized to identify the most suitable setup for the case study area. The Particle Swarm Optimization algorithm was employed to maximize system reliability, measured by LPSP, and economic performance, reflected in TAC. The optimization results were validated against the Genetic algorithm, a widely recognized method for optimizing similar systems, using statistical benchmark tests. The optimal configuration (PV3/WT3/B1) comprises polycrystalline photovoltaic panels, vertical-axis wind turbines, and lithium-ion batteries, achieving a TAC of $117,521 at 2 % unavailability. In contrast, the least efficient configuration (PV1/WT1/B3) includes thin-film photovoltaic panels, horizontal-axis wind turbines, and lead-acid batteries, with a TAC of $281,167 representing a 139.2 % increase, highlighting the importance of technology selection. A comprehensive sensitivity analysis was also conducted, examining key meteorological, economic, and social factors. This analysis identified the most influential parameters, validated the optimization results, and provided actionable insights for future system designs.

关键词： Hybrid solar/wind/storage system Various energy technologies pso algorithm GA algorithm Optimal sizing Optimal technology selection

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A novel, reduced-order optimization method for nonlinear model correction of turboshaft engines

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第4期38卷 2103-2122页

作者： Han, Xinhao Huang, Jinquan Zhou, Xin Zou, Zelong Lu, Feng Zhou, Wenxiang Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

Turboshaft engines are different from each other due to manufacturing and installation tolerances. Hence, it is difficult to draw out the physical model from the average component maps and design points to represent the performance of the individual engine. The available test-bed data is usually less than the number of correction coefficients to update the maps, and it is the underdetermined state optimization issue. In this paper, we propose a novel reduced-order optimization, namely pso-EKF algorithm, combined with prior state estimation for non-linear model correction of the turboshaft engine. This method combines the advantages of pso and extended Kalman filter (EKF). pso-EKF method converts partial parameters optimized by pso into parameters directly solved by EKF. The order of optimization space is reduced. Using the correction coefficient function, a stability improvement strategy is designed to ensure the stability of the optimization process. Compared with the GA and pso algorithms, experimental verification shows that the method has a faster convergence speed and smaller model error than the general pso. After the performance map is updated by this method, the error of outputs of the individual model is within 1.4 %.

关键词： Turboshaft engine Component-level model pso algorithm EKF Model correction

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Research on stability control strategy of adjustable pressure wall-climbing robot based on adsorption identification

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第5期238卷 1590-1602页

作者： Cheng, Hao Wang, Xuejun Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

In order to solve the problem that it is difficult to accurately model the complex fluid domain in the negative pressure chamber of the robot in the process of wall climbing, and thus cannot quickly and steadily adsorb, a double closed-loop control strategy based on parameter identification and particle swarm optimization is proposed in this paper to improve the speed and stability of the adsorption response of the robot in the process of wall climbing. Firstly, a vacuum negative pressure wall climbing robot with a spring pressure regulating structure is developed, and the critical conditions of the robot's instability are solved by mechanical analysis. Then, the rigid-flexible coupling model of the whole machine was established based on mass, spring and Maxwell viscoelastic elements. The model parameters of the adsorption system were identified by vacuum adsorption test. Compared with the experimental data, the accuracy of the model was more than 80%. On this basis, PID controller is used to adjust the adsorption response speed of the robot, a double closed-loop stable adsorption control strategy is designed, and the control parameters of the adsorption system are adjusted by using the dynamic weight standard particle swarm optimization algorithm. Finally, simulation and prototype tests show that, the proposed control strategy can shorten the static and dynamic response time of the adsorption system by about 3.5 s and 1.4 s, the flow response time by about 1.15s, the maneuvering performance of the whole system is improved by about 26%, and the parameter overshoot and steady-state error are lower, which provides a theoretical reference for the stability control and engineering application of the wall-climbing robot.

关键词： Adjustable pressure wall climbing robot parameter identification pso algorithm rigid-flexible coupling stability control strategy

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