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modeling and flexible power control of electric vehicle wireless charging applied to building side AC/DC power distribution and consumption on building side

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JOURNAL OF POWER ELECTRONICS 2025年 1-10页

作者： Zhang, Hui Li, Zhifan Wang, Xuli Deng, Haozhe Econ & Tech Res Inst State Grid Anhui Elect Power Hefei Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China Wuhan Britain China Sch Wuhan Peoples R China

For AC/DC power distribution and consumption on the building side, EV charging provides a flexible and fast power regulation potential. With wireless charging, household users can actively execute load power adjustment according to the direction of the distribution network dispatch center to balance the supply between the power generation side and the demand side. For independent residential scenarios, the power of wireless charging electric vehicle (EV) systems can be regulated conveniently. To investigate the dynamic characteristic of EV wireless charging power regulation, a data-driven modeling method is proposed in this paper, which identifies the dynamic model based on the instrument variable method. Based on the estimated model, an internal model controller is further designed to regulate the wireless charging power of EVs to realize rapid response adjustments of the demand side loads. Experimental results show that the designed controller can autonomously adjust the charging power to suppress household power fluctuations and quickly respond to load regulation instructions from a distribution scheduling center to achieve a rapid response for the demand side.

关键词： Demand side response data-driven modeling Electrical vehicle modeling and control Wireless charging

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SINDy and PD-Based UAV Dynamics Identification for MPC

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DRONES 2025年第1期9卷 71-71页

作者： Guevara, Bryan S. Varela-Aldas, Jose Gandolfo, Daniel C. Toibero, Juan M. Univ Nacl San Juan Inst Automat CONICET Ave San Martin Oeste 1109 RA-J5400ARL San Juan Argentina Univ Tecnol Indoamer Ctr Invest Mecatron & Sistemas Interact MIST Carrera Ingn Ind Ambato 180103 Ecuador

This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approximation and Sparse Identification of Nonlinear Dynamics (SINDy). Addressing the inherent platform constraints-where control inputs are restricted to specific attitude angles and z-axis velocities-thrust and torque are approximated via a PD controller, which serves as a practical intermediary for facilitating nonlinear system identification. Both methodologies leverage data-driven strategies to construct compact and interpretable models from experimental data, capturing significant nonlinearities with high fidelity. The resulting models are rigorously evaluated within a Model Predictive Control (MPC) framework, demonstrating their efficacy in precise trajectory tracking. Furthermore, the integration of data-driven insights enhances the accuracy of the identified models and improves control performance. This framework offers a robust and adaptable solution for analyzing UAV dynamics under realistic operational conditions, emphasizing the comparative strengths and applicability of each modeling approach.

关键词： SINDy nonlinear identification MPC UAV data-driven modeling

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Effect of dataset representation bias on generalizability of machine learning models in predicting flexural properties of ultra-high-performance concrete (UHPC) beams

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ENGINEERING STRUCTURES 2025年 326卷

作者： Chen, Jinxin Bao, Yi Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA

Machine learning (ML) offers transformative potential in structural design through the high efficiency in exploring optimal solutions within vast design spaces. However, concerns persist among structural engineers regarding the generalizability and reliability of ML models trained on datasets with biases. This study investigates the influence of dataset representation bias on the generalizability of ML models in predicting the flexural properties of ultra-high-performance concrete beams. The research addresses three primary objectives: (1) developing a novel metric to quantify representation bias of continuous datasets, (2) devising an algorithm to create datasets with controlled bias levels, and (3) evaluating the effect of dataset representation bias on the generalizability of ML models. Key contributions include the first comprehensive analysis of representation bias in structural prediction models, the introduction of a Monte Carlo Bias Estimation method for evaluating dataset bias, the development of an Adaptive Bias Sampling Algorithm for dataset generation, and the modification of Latin Hypercube Sampling to ensure uniform dataset distribution. Findings reveal that dataset bias significantly undermines the generalizability of ML models, and the proposed methods offer effective strategies for assessing and mitigating dataset bias, thereby enhancing the generalizability of ML models.

关键词： data-driven modeling Flexural properties Generalizability Machine learning (ML) Representation bias Structural design

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data-driven identification of crystallization kinetics

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AICHE JOURNAL 2024年第5期70卷 e18333-e18333页

作者： Nyande, Baggie W. Nagy, Zoltan K. Lakerveld, Richard Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China Purdue Univ Davidson Sch Chem Engn W Lafayette IN USA Purdue Univ Davidson Sch Chem Engn 480 W Stadium Ave W Lafayette IN 47907 USA Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Clear Water Bay Hong Kong Peoples R China

A novel data-driven methodology is presented for developing mathematical models for crystallization processes. The data-driven approach is based on the sparse identification of nonlinear dynamics (SINDy) method, which iterates between a partial least-squares fit and a sparsity-promoting step leading to the discovery of sparse interpretable models. The performance of the SINDy methodology is characterized for the identification of crystallization kinetics in a mixed tank operated in a continuous mode, the isothermal crystallization of lysozyme in a batch stirred tank and cooling crystallization of paracetamol. The SINDy method is robust against noise. Good agreement is obtained between the data-driven model and the data obtained from crystallization experiments. The presented data-driven approach can be attractive for modeling industrial crystallization processes where process analytical technology tools are available for the measurement of process variables but functional forms of kinetic expressions are unknown.

关键词： crystallization data-driven modeling PAT population balance model sparse identification

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data-driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第5期12卷 989-1003页

作者： Tang, Yiyin Wang, Yalin Liu, Chenliang Sui, Qingkai Liu, Yishun Huang, Keke Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

Most enterprises rely on railway transportation to deliver their products to customers, particularly in the salt lake chemical industry. Notably, allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes. However, due to demand fluctuations from changing product orders and unforeseen railway scheduling delays, manually adjusted allocation and loading may lead to excessive loading and unloading distances and times, ultimately increasing transportation costs for enterprises. To address these issues, this paper proposes a data-driven two-stage robust optimization (TSRO) framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism (GSTAC-AM), which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading. Specifically, GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers. Then, a robust counterpart model is formulated to ensure computational tractability. In addition, a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes. Finally, the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.

关键词： Salt Uncertainty Costs Loading Transportation Rail transportation Robustness Resource management Optimization Chemicals data-driven modeling demand uncertainty product resource allocation and loading salt lake chemical enterprise two-stage robust optimization

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Understanding HVAC system runtime of ***:An energy signature analysis using smart thermostat data

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Building Simulation 2025年第2期18卷 235-258页

作者： You-Jeong Kim Alexander Waegel Max Hakkarainen Yun Kyu Yi William Braham University of Illinois at Urbana-Champaign ChampaignIL61820USA University of Pennsylvania PhiladelphiaPA19104USA

Heating,ventilation,and air conditioning system runtime is a crucial metric for establishing the connection between system operation and energy *** homes in the same location can have varying runtime due to different *** understand such heterogeneity,this study conducted an energy signature analysis of heating and cooling system runtime for 5,014 homes across the US>using data from ecobee smart *** approaches were compared for the energy signature analysis:(1)using daily mean outdoor temperature and(2)using the difference between the daily mean outdoor temperature and the indoor thermostat setpoint(delta T)as the independent *** best-fitting energy signature parameters(balance temperatures and slopes)for each house were estimated and statistically *** results revealed significant differences in balance temperatures and slopes across various climates and individual ***,we identified the impact of housing characteristics and weather conditions on the energy signature parameters using a long absolute shrinkage and selection operator(LASSO)*** delta T into the energy signature model significantly enhances its ability to detect hidden impacts of various features by minimizing the influence of setpoint ***,our cooling slope analysis highlights the significant impact of outdoor humidity levels,underscoring the need to include latent loads in building energy models.

关键词： data-driven modeling HVAC system runtime energy signature analysis smart thermostat dataset ecobee DYD

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A Distributed Neural Hybrid System Learning Framework in modeling Complex Dynamical Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第5期36卷 9463-9473页

作者： Yang, Yejiang Wang, Tao Xiang, Weiming Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

In this article, a distributed neural network modeling framework including a novel neural hybrid system model is proposed for enhancing the scalability of neural network models in modeling dynamical systems. First, high-dimensional training data samples will be mapped to a low-dimensional feature space through the principal component analysis (PCA) featuring process. Following that, the feature space is bisected into multiple partitions based on the variation of the Shannon entropy under the maximum entropy (ME) bisecting process. The behavior of subsystems in the prespecified state space partitions will then be approximated using a group of shallow neural networks (SNNs) known as extreme learning machines (ELMs), and then it can further simplify the model by merging the redundant lattices based on their training error performance. The proposed modeling framework can handle high-dimensional dynamical system modeling problems with the advantages of reducing model complexity and improving model performance in training and verification. To demonstrate the effectiveness of the proposed modeling framework, examples of modeling the LASA dataset and an industrial robot are presented.

关键词： data-driven modeling extreme learning machine (ELM) hybrid systems neural networks

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Design and modeling of an Integral Molding Flexible Tail for Robotic Fish

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年第2期30卷 1322-1332页

作者： Tong, Ru Wu, Zhengxing Li, Sijie Chen, Di Wang, Jian Tan, Min Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Tail flexibility optimization is crucial for improving the speed and efficiency of robotic fish. However, reliable flexible tail structures and accessible modeling methods are still in the exploratory stage. This article proposes a novel integral molding flexible fish tail (IMFFT) characterized by continuous flexibility, a hollow air cavity, and an embedded skeletal structure. These features empower the tail with continuous flexible propulsion capabilities, neutral buoyancy for stable fish posture, and limited compressibility for withstanding water pressure. In addition, a predictive-network-based model for the IMFFT is developed through thrust data acquisition, dynamic analysis, and predictive network training. Specifically, the predictive network enables the prediction of pattern parameters of passive angles on the flexible tail. Deploying the IMFFT on our self-developed robotic tuna, performance tests demonstrate significant improvements, including a high swimming speed of 3.28 body lengths per second, an average speed improvement of 25.30%, an average cost of transport (COT) reduction of 24.45%, and a 57.12% reduction in roll angle fluctuation range due to the neutral buoyancy of the fish tail, which competes favorably with rigid fish tails and other flexible tail structures. This study provides novel guidance for optimizing the flexibility of underwater bioinspired robots.

关键词： data-driven modeling flexibility optimization integral molding flexible tail predictive network robotic fish underwater robot

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Dynamic performance prediction of PEM electrolyzers combining an electrochemical neural network model and a lumped thermal capacitance model

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年

作者： Luo, Kui Li, Peng Yan, Yingshuang Ding, Chenxi Liu, Zilong Huang, Siyuan Duan, Zhengpeng Cai, Lanlan Wuhan Univ Technol Sch Mech & Elect Engn East CampusMafangshan Campus122 Luoshi Rd Wuhan 430070 Hubei Peoples R China Guangdong Lab Res & Dev Ctr Green Hydrogen & Green Ammonia Prod Foshan Xianhu Lab Adv Energy Sci & Technol Foshan Peoples R China

The proton exchange membrane (PEM) electrolyzer electrochemical models based on conventional analytical and empirical approaches require numerous parameters, which are difficult to obtain accurately, leading to lower dynamic performance prediction accuracy while the parameters of the lumped thermal capacitance model are relatively simple and easily identifiable. This study proposes an integrated modeling framework, which combines a backpropagation (BP) neural network model for electrochemical performance and a lumped thermal capacitance model for the assessment of thermal performance. The neural network model is trained on polarization data collected from 44 degrees C to 76 degrees C. The BP neural network, optimized through iterative hyperparameter tuning, achieves high predictive accuracy with three hidden layer nodes and tansig and purelin activation functions, exhibiting less than 1% error in polarization curve predictions at four additional temperatures and current densities from 0.04 to 2.6 A/cm2. The thermal model, calibrated with experimental data, predicts thermal-balance temperatures with root mean square error (RMSE) of 0.08 degrees C to 0.52 degrees C under four different operating conditions. The dynamic performance of the integrated model is validated under three different operating conditions. This comprehensive model enables detailed analysis of critical operating variables, providing actionable insights for selecting and optimizing operating conditions in practical applications.

关键词： BP neural network data-driven modeling PEM electrolyzer operating conditions performance prediction

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A novel hybrid LSTM and masked multi-head attention based network for energy consumption prediction of industrial robots

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APPLIED ENERGY 2025年 383卷

作者： Wang, Zuoxue Jiang, Pei Li, Xiaobin He, Yan Wang, Xi Vincent Yang, Xue Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China KTH Royal Inst Technol Dept Prod Engn SE-10044 Stockholm Sweden Chongqing Polytech Univ Elect Technol Coll Finance & Management Chongqing 401331 Peoples R China

Due to the wide application of industrial robots (IRs) in the manufacturing industry and their significant energy consumption (EC), predicting EC under different trajectories and working conditions has attracted increasing attention. data-driven modeling methods have proven to be a viable approach for revealing the quantitative relationship between IR operating parameters and EC. However, in manufacturing systems, the coexistence of numerous heterogeneous IRs necessitates a substantial amount of data with power labels and sufficient hardware computing resources to model the operational EC of each robot type. Motivated by these requirements, this paper proposes a transfer learning based method for modeling the operational EC of IRs. Based on an analysis of the temporal causal relationship between model input variables and operational EC, a time series information feature extraction method and an industrial robot operational energy consumption prediction network (ROEPN) are proposed, which combines layer normalization (LN), long short-term memory neural network (LSTM) and masked multi-head attention mechanism (MHA). Moreover, a rigorous pre-training- fine-tuning transfer learning scheme is designed and implemented on the target domain data, effectively achieving the transfer of ROEPN from the source domain to the target domain. Experiments were conducted on the HSR-JR612 and HSR-JR603, and the results demonstrate that the proposed EC model transfer method can predict EC for different IRs, trajectories and loads, with the mean absolute percentage error (MAPE) being less than 2.69% in the case of small samples.

关键词： Industrial robots Energy consumption prediction Temporal causal relationship data-driven modeling Transfer learning

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