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860 results on '"Zhang, Yongzhi"'

1. Process Circuit Automation Configuration Scheme Applied to Digital Transformation of Substations

Author

Zhang, Yongzhi, Tang, Xiaohui, Wang, Fengguang, Lv, Hang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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3. Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging

Author

Zhao, Mingyuan and Zhang, Yongzhi

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

This study develops a methodology by capturing both the battery aging state and degradation rate for improved life prediction performance. The aging state is indicated by six physical features of an equivalent circuit model that are extracted from the voltage relaxation data. And the degradation rate is captured by two features extracted from the differences between the voltage relaxation curves within a moving window (for life prediction), or the differences between the capacity vs. voltage curves at different cycles (for life classification). Two machine learning models, which are constructed based on Gaussian Processes, are used to describe the relationships between these physical features and battery lifetimes for the life prediction and classification, respectively. The methodology is validated with the aging data of 74 battery cells of three different types. Experimental results show that based on only 3-12 minutes' sampling data, the method with novel features predicts accurate battery lifetimes, with the prediction accuracy improved by up to 67.09% compared with the benchmark method. And the batteries are classified into three groups (long, medium, and short) with an overall accuracy larger than 90% based on only two adjacent cycles' information, enabling the highly efficient regrouping of retired batteries.

Published
2023

4. Comprehensive performance comparison among different types of features in data-driven battery state of health estimation

Author

Feng, Xinhong, Zhang, Yongzhi, Xiong, Rui, and Wang, Chun

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Battery state of health (SOH), which informs the maximal available capacity of the battery, is a key indicator of battery aging failure. Accurately estimating battery SOH is a vital function of the battery management system that remains to be addressed. In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods. The method performance is validated based on 58826 cycling data units of 118 cells. Experimental results show that the physics-driven ML generally estimates more accurate SOH than other non-physical features under different scenarios. The physical features-based GPR predicts battery SOH with the errors being less than 1.1% based on 10 to 20 mins' relaxation data. And the high robustness and generalization capability of the methodology is also validated against different ratios of training and test data under unseen conditions. Results also highlight the more effective capability of knowledge transfer between different types of batteries with the physical features and GPR. This study demonstrates the excellence of physical features in indicating the state evolution of complex systems, and the improved indication performance of these features by combining a suitable ML method.

Published
2023

6. Research on Jinan Green Logistics Development Strategy Based on SWOT Analysis

Author

Qiao, Mingxu, Zhang, Yongzhi, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Vasilev, Valentin, editor, Popescu, Cătălin, editor, Guo, Yanhong, editor, and Li, Xiaolin, editor

Published
2024
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7. Study on the Relationship Between E-Commerce Logistics and High Quality Development of County Business System

Author

Zhang, Yongzhi, Lu, Kaiwen, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Vasilev, Valentin, editor, Popescu, Cătălin, editor, Guo, Yanhong, editor, and Li, Xiaolin, editor

Published
2024
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29. Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

Author

Zhang, Yongzhi, Qu, Xiaobo, and Tong, Lang

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper, we develop a model to plan energy-efficient speed trajectories of electric trucks in real-time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. We then formulate an energy minimization problem and solve it by an alternating direction method of multipliers (ADMM) method that exploits the structure of the problem. A model predictive control framework is then employed to deal with topographic and traffic uncertainties in real-time. An empirical study is conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The experimental results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by as high as 35.35% compared to benchmarking solutions.

Published
2020
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32. A consensus protocol for functional connectivity analysis in the rat brain

Author

Grandjean, Joanes, Desrosiers-Gregoire, Gabriel, Anckaerts, Cynthia, Angeles-Valdez, Diego, Ayad, Fadi, Barrière, David A., Blockx, Ines, Bortel, Aleksandra, Broadwater, Margaret, Cardoso, Beatriz M., Célestine, Marina, Chavez-Negrete, Jorge E., Choi, Sangcheon, Christiaen, Emma, Clavijo, Perrin, Colon-Perez, Luis, Cramer, Samuel, Daniele, Tolomeo, Dempsey, Elaine, Diao, Yujian, Doelemeyer, Arno, Dopfel, David, Dvořáková, Lenka, Falfán-Melgoza, Claudia, Fernandes, Francisca F., Fowler, Caitlin F., Fuentes-Ibañez, Antonio, Garin, Clément M., Gelderman, Eveline, Golden, Carla E. M., Guo, Chao C. G., Henckens, Marloes J. A. G., Hennessy, Lauren A., Herman, Peter, Hofwijks, Nita, Horien, Corey, Ionescu, Tudor M., Jones, Jolyon, Kaesser, Johannes, Kim, Eugene, Lambers, Henriette, Lazari, Alberto, Lee, Sung-Ho, Lillywhite, Amanda, Liu, Yikang, Liu, Yanyan Y., López -Castro, Alejandra, López-Gil, Xavier, Ma, Zilu, MacNicol, Eilidh, Madularu, Dan, Mandino, Francesca, Marciano, Sabina, McAuslan, Matthew J., McCunn, Patrick, McIntosh, Alison, Meng, Xianzong, Meyer-Baese, Lisa, Missault, Stephan, Moro, Federico, Naessens, Daphne M. P., Nava-Gomez, Laura J., Nonaka, Hiroi, Ortiz, Juan J., Paasonen, Jaakko, Peeters, Lore M., Pereira, Mickaël, Perez, Pablo D., Pompilus, Marjory, Prior, Malcolm, Rakhmatullin, Rustam, Reimann, Henning M., Reinwald, Jonathan, Del Rio, Rodrigo Triana, Rivera-Olvera, Alejandro, Ruiz-Pérez, Daniel, Russo, Gabriele, Rutten, Tobias J., Ryoke, Rie, Sack, Markus, Salvan, Piergiorgio, Sanganahalli, Basavaraju G., Schroeter, Aileen, Seewoo, Bhedita J., Selingue, Erwan, Seuwen, Aline, Shi, Bowen, Sirmpilatze, Nikoloz, Smith, Joanna A. B., Smith, Corrie, Sobczak, Filip, Stenroos, Petteri J., Straathof, Milou, Strobelt, Sandra, Sumiyoshi, Akira, Takahashi, Kengo, Torres-García, Maria E., Tudela, Raul, van den Berg, Monica, van der Marel, Kajo, van Hout, Aran T. B., Vertullo, Roberta, Vidal, Benjamin, Vrooman, Roël M., Wang, Victora X., Wank, Isabel, Watson, David J. G., Yin, Ting, Zhang, Yongzhi, Zurbruegg, Stefan, Achard, Sophie, Alcauter, Sarael, Auer, Dorothee P., Barbier, Emmanuel L., Baudewig, Jürgen, Beckmann, Christian F., Beckmann, Nicolau, Becq, Guillaume J. P. C., Blezer, Erwin L. A., Bolbos, Radu, Boretius, Susann, Bouvard, Sandrine, Budinger, Eike, Buxbaum, Joseph D., Cash, Diana, Chapman, Victoria, Chuang, Kai-Hsiang, Ciobanu, Luisa, Coolen, Bram F., Dalley, Jeffrey W., Dhenain, Marc, Dijkhuizen, Rick M., Esteban, Oscar, Faber, Cornelius, Febo, Marcelo, Feindel, Kirk W., Forloni, Gianluigi, Fouquet, Jérémie, Garza-Villarreal, Eduardo A., Gass, Natalia, Glennon, Jeffrey C., Gozzi, Alessandro, Gröhn, Olli, Harkin, Andrew, Heerschap, Arend, Helluy, Xavier, Herfert, Kristina, Heuser, Arnd, Homberg, Judith R., Houwing, Danielle J., Hyder, Fahmeed, Ielacqua, Giovanna Diletta, Jelescu, Ileana O., Johansen-Berg, Heidi, Kaneko, Gen, Kawashima, Ryuta, Keilholz, Shella D., Keliris, Georgios A., Kelly, Clare, Kerskens, Christian, Khokhar, Jibran Y., Kind, Peter C., Langlois, Jean-Baptiste, Lerch, Jason P., López-Hidalgo, Monica A., Manahan-Vaughan, Denise, Marchand, Fabien, Mars, Rogier B., Marsella, Gerardo, Micotti, Edoardo, Muñoz-Moreno, Emma, Near, Jamie, Niendorf, Thoralf, Otte, Willem M., Pais-Roldán, Patricia, Pan, Wen-Ju, Prado-Alcalá, Roberto A., Quirarte, Gina L., Rodger, Jennifer, Rosenow, Tim, Sampaio-Baptista, Cassandra, Sartorius, Alexander, Sawiak, Stephen J., Scheenen, Tom W. J., Shemesh, Noam, Shih, Yen-Yu Ian, Shmuel, Amir, Soria, Guadalupe, Stoop, Ron, Thompson, Garth J., Till, Sally M., Todd, Nick, Van Der Linden, Annemie, van der Toorn, Annette, van Tilborg, Geralda A. F., Vanhove, Christian, Veltien, Andor, Verhoye, Marleen, Wachsmuth, Lydia, Weber-Fahr, Wolfgang, Wenk, Patricia, Yu, Xin, Zerbi, Valerio, Zhang, Nanyin, Zhang, Baogui B., Zimmer, Luc, Devenyi, Gabriel A., Chakravarty, M. Mallar, and Hess, Andreas

Published
2023
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36. Experimental investigation of large-scale flow structures in an aircraft cabin mock-up.

Author

Zhang, Yongzhi, Li, Jiayu, Liu, Mingxin, Liu, Junjie, and Wang, Congcong

Subjects
Aircraft cabin, Airflow, Large-scale circulation, Thermal plume, Turbulence kinetic energy spectra
Abstract

The purpose of this study was to investigate the influence of large-scale circulation on the flow field in a cabin mockup. The velocity was measured by ultrasonic anemometers (UA). Then, this study analyzed the turbulence kinetic energy spectra of the velocity fluctuation signal. The turbulence kinetic energy spectra of the measurement points reflect the flow characteristic of the large-scale circulation in the cabin mockup. The results contribute to the understanding of the role of the thermal plume on the large-scale circulation in the cabin. The large-scale circulations impact on air quality was also investigated, and the contaminant distribution was measured using tracer gas in the cabin. The two large-scale circulation interactions made the air flow mixing approximately uniform.

Published
2020

42. An Adaptive and Collaborative Method Based on GMRA for Intrusion Detection

Author

Teng, Shaohua, Zhang, Yongzhi, Zhang, Wei, Liang, Lu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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43. Research on a New Type of Pipeline Robot

Author

Zhang, Yongzhi, Wu, Binghui, Liu, Jianfeng, Long, Siqing, Lv, Zechang, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, and Tan, Jianrong, editor

Published
2022
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