基本情况

教育经历：

(1) 2012-08 至 2016-11, 悉尼科技大学, 机械工程, 博士

(2) 2010-09 至 2012-06, 中国农业大学, 机械工程, 硕士

(3) 2006-09 至 2010-06, 中国农业大学, 机械工程, 学士

工作经历：

(1) 2020.4 至今， yl6809永利集团，教授

(2) 2017-11 至 2020-03, 北京理工大学，博士后

(3) 2016-11 至 2018-11, 悉尼科技大学, 博士后

学术兼职：

中国汽车工程协会汽车动力学分会委员、中国交通运输协会智库特聘专家、yl6809永利集团国际化发展青年导师、Sustainability期刊编委、International Journal Of Vehicle Design、Actuator等期刊客座主编

研究方向

1.基于机器学习与大数据的多动力系统能量管理与优化

2.数据-模型混合驱动的分布式驱动系统自适应控制

3.X-in-1电驱动系统深度集成与模块化

4.面向角模块的驱动-制动-转向-悬架一体化设计与控制

5.eVTOL电动推进系统设计、优化与自主飞行技术

科研项目

国家重点研发计划项目-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, 750万元, 主持

国家自然科学基金面上项目-分布式电驱动重载车辆行驶稳定性与能效优化协同控制研究，49万元, 主持

国家重点研发计划（子课题）-高频碳化硅驱动系统测评技术, 48万元, 主持

企业委托课题-氢燃料电池汽车电驱动系统模式切换控制策略优化，320万元

企业委托项目-多源动力系统参数匹配与能量管理策略设计软件项目，76万元

中国博士后科学基金特别资助-增程式电动车的多挡位串并联驱动系统动力学建模及控制, 18万元,主持

中国博士后科学基金面上资助-插电混动汽车的多挡位平行轴式高效动力耦合系统及控制, 8万元,主持

获奖情况

中国交通运输协会科技进步一等奖

北京市高层次留学归国人才

指导员工获得国家级竞赛“数字汽车”全国三等奖、北京市优秀毕业生、北工大优秀毕业生、优秀毕业论文、国家奖学金、校学习优秀奖、小米助学金、科技创新优秀奖等多项奖励。

代表性研究成果

[1]Li Y, Ruan J, Han Z, Hu J, Wan H. MADDPG-based energy management strategy for multi-mode distributed drive electric bus. Mech Mach Theory 2025;218. https://doi.org/10.1016/j.mechmachtheory.2025.106284.

[2]Ruan J, Xia J, Hu J, Wan H, Li Y, Qin Y. Continuous learning energy management strategy design based on EWC-DDPG for electric vehicles. Energy 2025;335. https://doi.org/10.1016/j.energy.2025.138158.

[3]Wan H, Ruan J, Xia J, Han Z, Li Y. The continuous training of machine learning-based energy management strategy for plug-in hybrid electric vehicle, part I: electric driving mode. Energy 2025;333. https://doi.org/10.1016/j.energy.2025.137467.

[4]Li T, Ruan J, Zhang K. The investigation of reinforcement learning-based end-to-end decision-making algorithms for autonomous driving on the road with consecutive sharp turns. Green Energy and Intelligent Transportation 2025;4. https://doi.org/10.1016/j.geits.2025.100288.

[5]Ruan J, Cao Z, Li Y, Hou T, Liang Z. Energy management strategy design for pure electric buses based on Adaptive-Advanced Neuro-Evolution of Augmenting Topologies. Energy 2025;329. https://doi.org/10.1016/j.energy.2025.136566.

[6]Wu C, Ruan J, Cui H, Zhang B, Li T, Zhang K. The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle , part I : Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid mode. Energy 2023;262:125084. https://doi.org/10.1016/j.energy.2022.125084.

[7]Cui H, Ruan J, Wu C, Zhang K, Li T. Advanced deep deterministic policy gradient based energy management strategy design for dual-motor four-wheel-drive electric vehicle. Mech Mach Theory 2023;179:105119. https://doi.org/10.1016/j.mechmachtheory.2022.105119.

[8]Zhang K, Ruan J, Li T, Cui H, Wu C. The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus. Energy 2023;269:126760. https://doi.org/10.1016/j.energy.2023.126760.

[9]Ruan J, Wu C, Liang Z, Liu K, Li B, Li W, et al. The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode. Energy 2023;269:126792. https://doi.org/10.1016/j.energy.2023.126792.

[10]Ruan JG, Wu CC, Cui HH, Li WH, Sauer DU. Delayed Deep Deterministic Policy Gradient-Based Energy Management Strategy for Overall Energy Consumption Optimization of Dual Motor Electrified Powertrain. IEEE Trans Veh Technol 2023;72:11415–27. https://doi.org/10.1109/TVT.2023.3265073.

联系方式

ruanjiageng@bjut.edu.cn