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    邓忠伟

    • 副教授 硕士生导师
    • 性别:男
    • 毕业院校:上海交通大学
    • 学历:博士研究生毕业
    • 学位:工学博士学位
    • 在职信息:在岗
    • 所在单位:机械与电气工程学院
    • 入职时间: 2022-10-10
    • 电子邮箱:

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    个人简介

    1. 基本情况:

    邓忠伟,男,四川成都人,中共党员,博士,副教授,硕士生导师。

    围绕新能源汽车动力电池及储能电池系统,开展了大量数据驱动及电化学机理建模、参数辨识与状态估计、健康管理与故障诊断、安全预警等研究。

    先后主持国家自然科学基金青年项目、国家重点研发计划子课题项目、中国博士后科学基金特别资助及面上项目等多项国家省部级项目以及多项企业攻关项目。相关成果获2022年重庆市科技进步一等奖、2023年自动化学会自然科学二等奖。发表高水平论文70余篇,其中以第一作者/通信作者发表SCI论文30余篇(含ESI高被引论文6篇),授权/申请国家发明专利40余项。担任SustainabilityEnergies等期刊客座编辑,SAE 新能源汽车技术委员会委员,Renewable and Sustainable Energy ReviewsCell iScienceIEEE TIIIEEE TITSIEEE TVTIEEE TTEApplied EnergyEnergy等国际期刊审稿专家,IEEESAE China会员。

    2. 个人主页:

    学院主页:https://www.smee.uestc.edu.cn/info/1177/17454.htm

    谷歌学术:https://scholar.google.com/citations?user=aL1sCI4AAAAJ&hl=zh_CN

    ResearchGate https://www.researchgate.net/profile/Zhongwei_Deng2

    3. 科研项目:

        主持项目:

    1)      国家自然科学基金委员会,青年基金项目,52102420,固态电池多场耦合建模及抑制老化的充放电优化控制研究,2022-012024-12,主持。

    2)      中国科技部,国家重点研发计划—中德政府间合作项目,2022YFE0102700,大数据驱动的纯电动汽车运行安全性和经济性研究及测评技术开发,任务二,2022-062024-5,主持。

    3)      中国博士后科学基金,特别资助项目,2023T160085,计及机理的动力电池健康及安全性能快速评估与预测研究,2023-092025-08,主持

    4)      中国博士后科学基金,面上项目,2021M693725,感知关键机理特征的固态电池充放电优化控制研究,2021-092023-08,主持。

    5)      重庆市科技局,自然科学基金博士后面上项目,cstc2020jcyj-bshX0079,车用固态电池电化学建模及充放电优化控制研究,2020-092022-08,主持。

    6)      中国汽车研究院股份有限公司,企业攻关项目,H20211445,动力电池全生命周期健康状态评估,2021-062022-06,主持。

        参研项目:

    1)      广东省科技厅,重点研发计划新能源汽车专项,2020B0909030001,无模组动力电池系统关键技术研究,2021-012023-12,主研。

    2)      国家自然科学基金委员会,区域创新发展联合基金(吉林地区),U20A20310,寒区全气候电动汽车动力电池系统热电耦合机理与高效管理,2021-012024-12,主研。

    3)      国家自然科学基金委员会,国际(地区)合作交流项目,52111530194,新能源汽车锂离子电池关键物理特征估计与优化研究,2021-012023-12,主研。

    4)      四川省科技厅,区域创新合作,2020YFQ0037,燃料电池客车双动力源系统智能管理与优化控制,2020-012022-12,主研。

    5)      重庆市科技局,技术预见与制度创新项目,cstc2020jsyj-ydxwtA X0006,新能源汽车退役电池梯次利用技术创新路线研究,2020-052020-12,主研。

    6)      华为技术有限公司,企业攻关项目,02090026050406,电池热电模型技术合作,2021-012021-12,主研。

    7)      新能源科技有限公司(ATL),企业攻关项目,02090026050370CE电芯电化学参数辨识技术开发,2021-022021-11,主研。

    8)      国家自然科学基金委员会,面上项目,51875339,兼合路径规划的插电式混合动力电动汽车预测性能量管理方法研究,2019-012022-12,主研。

    4. 代表性论文:  

    [1]     Z. Deng, L. Xu*, H. Liu, X. Hu, B. Wang, and J. Zhou, "Rapid health estimation of in-service battery packs based on limited labels and domain adaptation," Journal of Energy Chemistry, vol. 89, pp. 345-354, 2024.

    [2]     W. Guo, L. Yang*, Z. Deng*, B. Xiao, and X. Bian, "Early diagnosis of battery faults through an unsupervised health scoring method for real-world applications," IEEE Transactions on Transportation Electrification, pp. 1-1, 2023.

    [3]     W. Guo, L. Yang*, Z. Deng*, J. Li, and X. Bian, "Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment," Energy, vol. 281, p. 128320, 2023.

    [4]     K. Peng, Z. Deng*, Z. Bao, and X. Hu*, "Data-driven battery capacity estimation based on partial discharging capacity curve for lithium-ion batteries," Journal of Energy Storage, vol. 67, p. 107549, 2023.

    [5]     H. Liu, Z. Deng*, Y. Yang* et al., "Capacity evaluation and degradation analysis of lithium-ion battery packs for on-road electric vehicles," Journal of Energy Storage, vol. 65, p. 107270, 2023.

    [6]     Z. Deng, L. Xu*, H. Liu, X. Hu, Z. Duan, and Y. Xu, "Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles," Applied Energy, vol. 339, p. 120954, 2023.

    [7]     L. Wang, X. Zhao, Z. Deng*, and L. Yang, "Application of electrochemical impedance spectroscopy in battery management system: State of charge estimation for aging batteries," Journal of Energy Storage, vol. 57, p. 106275, 2023.

    [8]     Z. Ning, Z. Deng*, J. Li, H. Liu, and W. Guo, "Co-estimation of state of charge and state of health for 48 V battery system based on cubature Kalman filter and H-infinity," Journal of Energy Storage, vol. 56, p. 106052, 2022.

    [9]     J. Li, Z. Deng*, H. Liu, Y. Xie*, C. Liu, and C. Lu, "Battery capacity trajectory prediction by capturing the correlation between different vehicles," Energy, vol. 260, p. 125123, 2022.

    [10]  J. Wang, Z. Deng*, T. Yu et al., "State of health estimation based on modified Gaussian process regression for lithium-ion batteries," Journal of Energy Storage, vol. 51, p. 104512, 2022.

    [11]  Z. Deng*, X. Hu*, Y. Xie et al., "Battery health evaluation using a short random segment of constant current charging," iScience, vol. 25, no. 5, p. 104260, 2022.

    [12]  Z Deng*, Lin X, Cai J, Hu X. Battery health estimation with degradation pattern recognition and transfer learning. Journal of Power Sources, vol. 525, p. 231027, 2022. (ESI 高被引, top 1% of papers )

    [13]  Z. Deng*, X. Hu*, P. Li*, X. Lin, and X. Bian, "Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data," IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 5021-5031, 2022. (ESI 高被引, top 1% of papers )

    [14]  P. Li*, Z. Zhang, R. Grosu, Z. Deng*, et al., "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable Sustainable Energy Rev., vol. 156, p. 111843, 2022.

    [15]  Q. Zhang, L. Yang*, W. Guo, J. Qiang, C. Peng, Q. Li and Z. Deng*, "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, p. 122716, 2021.

    [16]  邓忠伟, 肖伟, 李阳, 黄勇, 贾俊, 胡晓松, "宏观时间尺度下的电动汽车循环里程预测," 机械工程学报, vol. 57, no. 24, pp. 250-258, 2021.

    [17]  P. Li, J. Liu, Z. Deng*, Y. Yang*, X. Lin, J. Couture and X. Hu. "Increasing energy utilization of battery energy storage via active multivariable fusion-driven balancing," Energy, p. 122772, 2021.

    [18]  贾俊, 胡晓松, 邓忠伟*, 徐华池, 肖伟, 韩锋, "数据驱动的锂离子电池健康状态综合评分及异常电池筛选," 机械工程学报, vol. 57, no. 14, pp. 141-149+159, 2021.

    [19]  X. Deng, Z. Deng*, Z. Song, X. Lin, and X. Hu*, "Economic Control for a Residential Photovoltaic-Battery System by Combining Stochastic Model Predictive Control and Improved Correction Strategy," Journal of Energy Resources Technology, vol. 144, no. 5, 2021.

    [20]  L. Jiang, Z. Deng (共同一作), X. Tang*, L. Hu, X. Lin, and X. Hu*, "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, vol. 234, p. 121266, 2021.

    [21]  Y. Che, Z. Deng*, X. Lin, L. Hu, and X. Hu*, "Predictive Battery Health Management with Transfer Learning and Online Model Correction," IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1269-1277, 2021. (ESI 高被引, top 1% of papers )

    [22]  Z. Deng, X. Hu*, X. Lin, Y. Kim, and J. Li*, "Sensitivity Analysis and Joint Estimation of Parameters and States for All-Solid-State Batteries," IEEE Transactions on Transportation Electrification, vol. 7, no. 3, pp. 1314-1323, 2021.

    [23]  Z. Deng, X. Hu*, X. Lin*, L. Xu, J. Li, and W. Guo, "A Reduced-Order Electrochemical Model for All-Solid-State Batteries," IEEE Transactions on Transportation Electrification, vol. 7, no. 2, pp. 464-473, 2021. (ESI 高被引, top 1% of papers )

    [24]  Z. Deng, X. Hu*, X. Lin*, L. Xu, Y. Che, and L. Hu, "General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries," IEEE/ASME Transactions on Mechatronics, vol. 26, no. 3, pp. 1295-1306, 2021. (ESI 高被引, top 1% of papers, 2021年川渝科技学术大会优秀论文奖)

    [25]  Z. Deng, X. Hu*, X. Lin*, Y. Che, L. Xu, and W. Guo, "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, vol. 205, p. 118000, 2020. (ESI 高被引, top 1% of papers )

    [26]  Z. Deng, L. Yang*, H. Deng, Y. Cai, and D. Li, "Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system," Energy, vol. 142, pp. 838-850, 2018.

    [27]  Z. Deng, H. Deng, L. Yang*, Y. Cai, and X. Zhao, "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, vol. 138, pp. 509-519, 2017.

    [28]  Z. Deng, L. Yang*, Y. Cai, H. Deng, and L. Sun, "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, vol. 112, pp. 469-480, 2016.

    5. 荣誉和奖励:

    1)      2023年自动化学会自然科学二等奖(排2),“车用动力电池的可解释状态估计与寿命预测理论与方法”

    2)      2022年重庆市科技进步一等奖(排12),“新能源汽车动力电池运行安全监测管控技术及应用”

    3)      2021/2022年度机械工程学报优秀审稿人奖

    4)      2021/2022年川渝科技学术大会优秀论文奖

    5)      获第三十四届世界电动车大会优秀论文奖

    6)      2018年获国际SCI期刊Energy杰出审稿人奖

    6. 学术服务:

    1)      客座编辑:Sustainability, Energies

    2)      技术委员会(Technical Committee MembersTPM):国际汽车工程师学会SAE新能源汽车技术委员会、International Conference on Mechatronics and Automation Technology (ICMAT 2022/2023)

    3)      IEEE会员、中国汽车工程学会会员

    4)      Renewable and Sustainable Energy ReviewsiScienceIEEE TIIIEEE TVTIEEE TTEEnergyIEEE AccessIET Power ElectronicsJournal of Green EnergyScientific Reportsenergiesmaterials等国际期刊审稿专家。


    教育经历

    2014.9 -- 2019.6
    上海交通大学       动力工程及工程热物理       博士研究生毕业       工学博士学位

    2010.9 -- 2014.7
    吉林大学       热能与动力工程(汽车发动机)       大学本科毕业       工学学士学位

    工作经历

    2022.10 -- 至今

    电子科技大学机械与电气工程学院      特聘副教授

    2019.10 -- 2022.9

    重庆大学      博士后

    研究方向

  • 1、新能源汽车动力电池优化控制
    2、储能电池系统健康管理与预测运维
    3、电池系统故障诊断与安全预警