孟锦豪 副研究员/博士生导师/公司双百人才计划
研究方向：先进电力储能技术、退役电池梯次利用技术、锂电池应用、混合储能系统的能量管理。
电子邮箱：jinhao@scu.edu.cn, scmjh2008@163.com
地址：公司望江校区高压楼605实验室 邮编：610065
学习与工作经历
· 2014.03-2019.06 西北工业大学自动化学院电气工程专业博士（导师：骆光照）
· 2016.12-2018.12 奥尔堡大学能源技术系联合培养博士（导师：Remus Teodorescu）
· 2019.11-至今 澳门尼斯人娱乐网站(中国)有限公司 副研究员 团队：电力系统稳定与高压直流输电团队 实验室：澳门尼斯人娱乐网站新能源实验室
科研项目
主持或参与的科研项目：
[1] 退役动力锂离子电池非线性衰退轨迹的数据-经验协同预测研究，国家自然基金青年项目，2022.
[2] 新能源汽车快速充电策略研究，四川省应用基础研究面上项目，2021.
[3] 新能源汽车动力锂电池电-热-老化实时模型研究，中国博士后基金面上项目，2020.
[4] 轨道交通车载电池运维芯片、算法及智能云管理系统研发，湖南省高新技术产业科技创新引领计划（科技公关类），2020.
[5] 特种机器人电源系统健康状态在线评估研究,特殊环境机器人技术四川省重点实验室开放基金，2020.
[6] 动力锂电池健康状态的智能评估方法研究，公司引进人才启动经费，2020
[7] 光储电站安全域运行与主动支撑关键技术研究及示范，国家电网总部科技项目，2022
[8] 基于工业物联网的分布式储能集群云边协同诊断与学习型自趋优智能控制关键技术研究，四川省重点研发项目，2022
[9] 基于云储能架构体系的新能源消纳和故障应急响应的AI调度策略研究，国网总部科技项目，2021
教学工作
本科生：《新能源发电技术》、《工程师创新训练》；研究生课程：《可再生能源发电系统的建模、分析与控制》。
代表性论文
一作/通讯作者论文：
[1] J. Meng, G. Luo, and F. Gao, “Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine,” IEEE Transactions on Power Electronics, vol. 31, no. 3, pp. 2226–2238, Mar. 2016. (SCI, IF=6.153, ESI高被引论文)
[2] J. Meng et al., “An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery,” IEEE Transactions on Industry Applications, 2018, vol. 54, no. 2, pp. 1583–1591. (SCI, IF=3.654, ESI高被引论文)
[3] J. Meng, D. Stroe, M. Ricco, G. Luo, and R. Teodorescu, “A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model,” IEEE Transactions on Industrial Electronics, 2019, vol. 66, no. 10, pp. 7717-7727. (SCI, IF=8.236)
[4] J. Meng, M. Ricco, A. Acharya, G. Luo, M. Swierczynski, D. Stroe, R. Teodorescu, “Low-complexity Online Estimation for LiFePO4 Battery State of Charge in Electric Vehicles,” Journal of Power Sources, vol. 395, pp. 280–288, 2018. (SCI, IF=9.127)
[5] J. Meng, D. Stroe, M. Ricco, G. Luo, M. Swierczynski, R. Teodorescu, “A Novel Multiple Correction Approach for Fast Open Circuit Voltage Prediction of Lithium-ion Battery”, IEEE Transactions on Energy Conversion, 2019, vol. 34, no. 2, pp. 1115-1123. (SCI, IF=4.312)
[6] J. Meng, G. Luo, M. Ricco, M. Swierczynski, D.-I. Stroe, and R. Teodorescu, “Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles,” Applied Science., vol. 8, no. 5, p. 659, 2018. (SCI, IF=2.679, Feature Paper, Editor’s Choice)
[7] J. Meng, L. Cai, G. Luo, D.-I. Stroe, and R. Teodorescu, “Lithium-ion Battery State of Health Estimation with Short-term Current Pulse Test and Support Vector Machine,” Microelectronics Reliability, vol. 88–90, pp. 1216–1220, 2018. (SCI, IF=1.589, Highly cited paper of Microelectronics Reliability since 2018)
[8] J. Meng, L. Cai, D. Stroe, J. Ma, G. Luo, R. Teodorescu, “An Optimized Ensemble Learning Framework for Lithium-ion Battery State of Health Estimation in Energy Storage System,” Energy, vol.206, pp. 118140, 2020. (SCI, IF=7.147)
[9] J. Meng, L. Cai, D. Stroe, G. Luo, X. Sui, R. Teodorescu, “Lithium-ion Battery State-of-Health Estimation in Electric Vehicle Using Optimized Partial Charging Voltage Profiles,” Energy, vol.185, pp. 1054-1063, 2019. (SCI, IF=7.147)
[10] J. Meng, L. Cai, D. Stroe, X. Huang, J. Peng, T. Liu, R. Teodorescu, “An Automatic Weak Learner Formulation for Lithium-ion Battery State of Health Estimation”, IEEE Transactions on Industrial Electronics, 2021, vol. 69, no. 3, pp. 2659-2668. (SCI, IF=8.236)
[11] L. Cai, J. Meng*, D.-I. Stroe, J. Peng, G. Luo and R. Teodorescu, “Multi-objective Optimization of Data-driven Model for Lithium-ion Battery SOH estimation with Short-term Feature,” IEEE Transactions on Power Electronics, 2020, vol. 35, no. 11, pp. 11855-11864. (SCI, IF=6.153)
[12] L. Cai, J. Meng*, D.-I. Stroe, G. Luo, and R. Teodorescu, “An Evolutionary Framework for Lithium-ion Battery State of Health Estimation,” Journal of Power Sources, vol. 412, pp. 615–622, 2019. (SCI, IF=9.127)
[13] X. Du, J. Meng*, Y. Zhang, X. Huang, S. Wang, P. Liu, “An Information Appraisal Procedure Endows Reliable Online Parameter Identification to Lithium-ion Battery Model,” IEEE Transactions on Industrial Electronics, 2022, vol. 69, no. 6, pp. 5889-5899. (SCI, IF=8.236)
[14] D. Chen, J. Meng*, H. Huang, J. Wu, P. Liu, J. Lu, and T. Liu, “An Empirical-data Hybrid Driven Approach for Remaining Useful Life Prediction of Lithium-ion Batteries Considering Capacity Diving”, Energy, vol. 245, p.123222, 2022. (SCI, IF=7.147)
[15] H. Huang, J. Meng*, Y. Wang, L. Cai, J. Peng, J. Wu, Q. Xiao, T. Liu, and R. Teodorescu, “An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge,” Automotive Innovation, pp. 1-12, 2022. (高质量T1级期刊)
[16] X. Du, J. Meng*, J. Peng, Y. Zhang, T. Liu, and R. Teodorescu, “Sensorless temperature estimation of lithium-ion battery based on broadband impedance measurements”, IEEE Transactions on Power Electronics, 2022, vol. 37, no. 9, pp. 10101-10105. (SCI, IF=6.153)
[17] A. Wen, J. Meng*, J. Peng, L. Cai, Q. Xiao, “Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation,” Complexity, vol. 2020, 2020. (SCI, IF= 2.833)
[18] K. Liu, X. Hu*, J. Meng*, J. M. Guerrero, R. Teodorescu, “RUBoost-Based Ensemble Machine Learning for Electrode Quality Classification in Li-ion Battery Manufacturing”, IEEE/ASME Transactions on Mechatronics, 2021, DOI: 10.1109/TMECH.2021.3115997, early access. (SCI, IF=5.303)
[19] F. Feng*, R. Yang, J. Meng*, Y. Xie, Z. Zhang, Y. Chai, and L. Mou. “Electrochemical Impedance Characteristics at Various Conditions for Commercial Solid-liquid Electrolyte Lithium-ion Batteries: Part 1. Experiment Investigation and Regression Analysis”. Energy, p. 123091, 2022. (SCI, IF=7.147)
[20] F. Feng*, R. Yang, J. Meng*, Y. Xie, Z. Zhang, Y. Chai, and L. Mou. “Electrochemical Impedance Characteristics at Various Conditions for Commercial Solid-liquid Electrolyte Lithium-ion Batteries: Part. 2. Modeling and Prediction,” Energy, vol. 243, p. 123091, 2022. (SCI, IF=7.147)
[21] H. Huang, J. Meng*, Y. Wang, F. Feng*, L. Cai, J. Peng, T. Liu. “A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve,” Applied Energy, vol. 322, p.119469. (SCI, IF=9.746)
Co-authors：
[1] M. Lin, D. Wu, J. Meng, J. Wu, H. Wu, “A Multi-feature-based Multi-model Fusion Method for State of Health Estimation of Lithium-ion Batteries”, Journal of Power Sources, 2022, vol. 518, 230774. (SCI, IF=9.127)
[2] K. Liu, X. Tang, R. Teodorescu, F. Gao, J. Meng, “Future Ageing Trajectory Prediction for Lithium-ion Battery Considering the Knee Point Effect”, IEEE Transactions on Energy Conversion, 2022, vol. 37, no. 2, pp. 1282-1291. (IF=4.312)
[3] X. Huang, W. Liu, J. Meng, Y. Li, S. Jin, R. Teodorescu, D. Stroe, “Lifetime Extension of Lithium-ion Batteries with Low-Frequency Pulsed Current Charging”, IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, DOI: 10.1109/JESTPE.2021.3130424. (IF=4.472)
[4] X. Huang, W. Liu, A. Acharya, J. Meng, R. Teodorescu, D. Stroe, “Effect of Pulsed Current on Charging Performance of Lithium-ion Batteries”, IEEE Transactions on Industrial Electronics, 2022, vol. 69, no. 10, pp. 10144-10153. (IF=8.236)
[5] X. Sui, S. He, J. Meng, et al. “Fuzzy Entropy-based State of Health Estimation for Li-ion Batteries”, IEEE Journal of Emerging and Selected Topics in Power Electronics, 2020, vol. 9, no. 4, pp. 5125-5137. (IF=4.472)
[6] X. Sui, S. He, S. B. Vilsen, J. Meng, R. Teodorescu, D.I. Stroe, “A Review of Non-probabilistic Machine Learning-based State of Health Estimation Techniques for Lithium-ion Battery”, Applied Energy, 2021, vol. 300, pp. 117346. (IF=9.746)
[7] J. Wu, X. Liu, J. Meng, M. Lin, “Cloud-to-edge based State of Health Estimation Method for Lithium-ion Battery in Distributed Energy Storage System”, Journal of Energy Storage, 2021, vol. 41, 102974. (IF=6.583)
[8] J. Wu, L. Fang, J. Meng, M. Lin, and G. Dong, “Optimized Multi-source Fusion based State of Health Estimation for Lithium-ion Battery in Fast Charge Applications,” IEEE Transactions on Energy Conversion, 2022, vol. 37, no. 2, pp. 1489-1498. (IF=4.312)
[9] J. Li, K. Liu, Q. Zhou, J. Meng, Y. Ge, and H. Xu, “Electrothermal Dynamics-Conscious Many-Objective Modular Design for Power-Split Plug-in Hybrid Electric Vehicles,” IEEE/ASME Transactions on Mechatronics, 2022, DOI: 10.1109/TMECH.2022.3156535. (IF=5.303)
[10] M. Lin, C. Yan, J. Meng, W. Wang, and J. Wu, “Lithium-ion Batteries Health Prognosis via Differential Thermal Capacity with Simulated Annealing and Support Vector Regression,” Energy, pp. 123829, 2022. (IF=7.147)
[11] 肖迁, 穆云飞, 焦志鹏, 孟锦豪, 贾宏杰. 基于改进LightGBM的电动汽车电池剩余使用寿命在线预测[J/OL].电工技术学报:1-11[2022-05-08].
[12] 巫春玲, 胡雯博, 孟锦豪, 刘智轩, 程琰清. 基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J].电工技术学报,2021,36(24):5165-5175.
代表性会议论文
[1] J. Meng, G. Luo, and F. Gao, “State-of-charge estimation for lithium-ion battery using AUKF and LSSVM”, In 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), pp. 1–6. IEEE, 2014.
[2] J. Meng, G. Luo, E. Breaz, and F. Gao, “A Robust Battery State-of-charge Estimation Method for Embedded Hybrid Energy System”, In IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, pp. 001205-001210. IEEE, 2015.’
[3] G. Luo, J. Meng, X. Ji, X. Cai, and F. Gao, “A Data Driven Model for Accurate SOC Estimation in EVs”, In 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 352-357. IEEE, 2017.
[4] J. Meng et al., “An Overview of Online Implementable SOC Estimation Methods for Lithium-ion Batteries,” in Proceedings - 2017 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017, 2017, pp. 573–580.
[5] G. Luo, J. Meng, X. Ji, X. Cai, and F. Gao, “A Data Driven Model for Accurate SOC Estimation in EVs,” in 2017 IEEE International Conference on Industrial Technology (ICIT), 2017, pp. 352–357.
[6] J. Meng, G. Luo, E. Breaz, and F. Gao, “A Robust Battery State-of-charge Estimation Method for Embedded Hybrid Energy System,” in IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 001205–001210.
[7] J. Meng, G. Luo, and F. Gao, “State-of-charge Estimation for Lithium-ion Battery Using AUKF and LSSVM,” in 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014, pp. 1–6.
[8] T. Gherman, M. Ricco, J. Meng, R. Teodorescu, and D. Petreus, “Smart Integrated Charger with Wireless BMS for EVs,” in IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018, pp. 2151–2156.
[9] J. Peng, W. Liu, J. Meng, T. Meng, and G. Luo, “Initial Orientation and Sensorless Starting Strategy of Wound-Rotor Synchronous Starter/Generator,” in Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC, 2016.
[10] X. Sui, S. He, D. Stroe, X. Huang, J. Meng, R. Teodorescu, “A Review of Sliding Mode Observers based on Equivalent Circuit Model for Battery SoC Estimation,” in IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019.
[11] X. Huang, Y. Li, J. Meng, X. Sui, R. Teodorescu, and D.I. Stroe, “The Effect of Pulsed Current on the Performance of Lithium-ion Batteries,” in 2020 IEEE Energy Conversion Congress and Exposition (ECCE),2020.
国家发明专利
[1] 孟锦豪，刘平，王建武. 一种无电流传感器的电池荷电状态估计方法[P]，授权，ZL201910061004.3，2021-07-16.
[2] 孟锦豪，彭纪昌，马俊鹏，王顺亮，刘天琪. 一种退役动力锂电池的混合建模方法[P], 授权, ZL202010827778.5, 2021-10-22.
[3] 孟锦豪, 蔡磊, 彭纪昌, 马俊鹏, 王顺亮, 刘天琪. 基于充电电压曲线几何特征的锂电池健康状态估计方法[P], 授权，ZL202010826062.3, 2021-06-01.
[4] 孟锦豪，彭纪昌，马俊鹏，王顺亮，刘天琪. 一种用于分布式储能系统的锂电池状态估计方法[P], 授权, ZL202010826666.8, 2021-08-31.
[5] 孟锦豪，陈丹，黄焕炀，刘平，卢继武，刘天琪. 一种适用动力锂电池非线性衰退过程的RUL预测方法[P]，授权，ZL202111058653.1，2021-12-10.
[6] 孟锦豪, 黄焕炀, 蔡磊, 刘平, 刘天琪. 一种基于电压片段的锂电池健康状态估计方法[P], 授权，202110546304.8, 2021-05-19.
[7] 孟锦豪, 杜星皓, 张英敏, 王顺亮, 刘平, 刘天琪. 一种基于信息评估机制的锂电池模型参数辨识方法[P], 授权, 202110373019.0, 2021-04-07.
获奖情况
[1] 西北工业大学优秀博士毕业生
[2] 西北工业大学优秀硕士论文
[3] 西北工业大学优秀博士论文
[4] 陕西省自然科学论文三等奖，陕西省人民政府，2020，排名1/3
[5] 高空长航时无人机燃料电池系统关键技术研究及应用，陕西省高校科学技术二等奖，陕西省教育厅，2020，排名4/8。
学术兼职
IEEE会员，电工技术学会高级会员，IEEE PES电动汽车技术委员会（中国）动力电池系统技术分委会理事,中国仿真学会仿真技术应用专委会委员，四川省电机工程学会委员。
长期担任 IEEE Transaction on Power Electronics、IEEE Transaction on Industrial Electronics、IEEE Transaction on Energy Conversion、Journal of Power Sources等国内外高水平学术期刊审稿人。
欢迎各位同学联系报考我的研究生，进入电力系统稳定与高压直流输电团队，为澳门尼斯人娱乐网站新能源实验室的发展贡献力量。联系方式：jinhao@scu.edu.cn
