Smart grid and its integration of vehicle to grid service, renewable energy, electric vehicles batteries and energy storages, artificial intelligence methods for modelling and optimization, particular neural network modelling and meta-heuristic optimisation with their applications to power system scheduling, load forecasting and intelligent manufacturing. 

2013.02~2017.03	PhD of Electronic and Electrical Engineering, Queen's University Belfast, Belfast, United Kingdom. ·           EPSRC   International Doctoral Studentship ·           Thesis   title: Advanced Computational   scheduling methods for integrating plug-in electric vehicles and renewable   energy into power systems ·           Supervisors:   Prof. Kang Li & Dr. Aoife Foley
2010.09~2013.01	Master   of Control Theory and Engineering,   Shanghai University, Shanghai, China ·           Thesis title:   Real-time analysis and implementation   for hybrid industrial network for renewable energy system monitoring ·           Supervisors:   Prof. Minrui Fei & Prof. Jingqi Fu ·           Top Ten   graduates and academic star
2006.09~2010.06	Bachelor   of Automation, Shanghai   University, Shanghai, China ·           Thesis   title: Design and implementation for two-layers   wireless industry network protocol for renewable energy system monitoring ·           Supervisor:   Prof. Minrui Fei & Dr. Haikuan Wang ·           Top Ten   student in Shanghai University

   

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2017.09~ Present

Assistant Professor - Associate Professor, Director of Smart Energy and Intelligent Control Group, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences

• Lead a group with 2 research staffs, 2 PhD students and 3 MSc students

• Delivering lectures in multidisciplinary modules for MSc students

• Develop artificial intelligence approaches and optimisation/modelling technologies for renewable energy and mechatronics systems

• Successfully obtained a number of research grants

• Establish strong research links with domestic and international scholars

   

Journal papers

J1.    G. Hou, L. Gong, Z. Yang*, J. Zhang, Multi-objective economic model predictive control for gas turbine system based on quantum simultaneous whale optimization algorithm, Energy Conversion and Management, 2020, 207: 112498（JCR Q1, IF=7.181）

J2.    Z. Yang, K. Li, Y. Guo, S. Feng, Q. Niu, Y. Xue, A. Foley, A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles, Energy , 2019, 170: 889-905;（JCR Q1, IF=5.537）

J3.    Z. Yang, K. Liu, J. Fan, Y. Guo, Q. Niu, J. Zhang, A Novel Binary/Real-valued Pigeon Inspired Optimization for Economic/Environment Unit Commitment with Renewables and Plug-in Vehicles, Science China-Information Science (中国科学-信息科学), 2019, 62: 070213;（JCR Q2, IF=2.731）

J4.    Z. Yang, M. Mourshed, K. Liu, Y. Guo, S. Feng, A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting, Neurocomputing, accepted, （JCR Q1, IF=4.072）

J5.    J. Zhu, Z. Yang*, M. Mourshed, Y. Guo, Y. Chang, Electric vehicle charging load forecasting: a comparative study of deep learning approaches, Energies, 12(14):2692; （JCR Q2, IF=2.707）

J6.    Y. Wang, Z. Yang*, M. Mourshed, Y. Guo, Q. Niu, X. Zhu, Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method, Energy Conversion and Management, 2019, 196,: 935-949; （JCR Q1, IF=7.181）

J7.    J. Zhu, Z. Yang*, Y. Guo, J. Zhang, H. Yang, Short-term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches, Applied Sciences, 2019, 9(9), 1723; （JCR Q2, IF=2.217）

J8.    Y. Wang, Z. Yang*, Y. Guo, J. Zhu and X. Zhu, A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment, Applied Sciences, 9(9), 1776; （JCR Q2, IF=2.217）

J9.    K. Liu, X. Hu, Z. Yang*, Y. Xie, S. Feng, Lithium-ion battery charging management considering the economic costs of electricity-loss and battery degradation, Energy Conversion and Management, 2019, 195: 167-179; （JCR Q1, IF=7.181）

J10. Z. Yang, K. Li, Y. Guo, H. Ma, M. Zheng, Compact Real-valued Teaching-Learning Based Optimization with the Applications to Neural Network Training, Knowledge-Based Systems, 2018,  159: 51-62; （JCR Q1, IF=5.101）

J11. H. Ma, Z. Yang*, P. You, M. Fei, Multi-objective Biogeography-based Optimization for Dynamic Economic Emission Load Dispatch Considering Plug-in Electric Vehicles Charging, Energy, 2017, Vol. 135:101-111; （JCR Q1, IF=5.537）

J12. Z. Yang, K. Li, Q. Niu, Y. Xue, A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem, Knowledge-Based Systems, 2017, 134:13-30; （JCR Q1, IF=5.101）

J13. Z. Yang, K. Li, Q. Niu, Y. Xue, A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles, Energy Conversion and Management, 2017, 132: 460-481; （JCR Q1, IF=7.181）

J14. Z. Yang, K. Li, A. Foley, Computational Scheduling Methods for Integrating Plug-in Electric Vehicles in the Power System: A Review, Renewable and Sustainable Energy Reviews, 2015, 51: 396-416.; （JCR Q1, IF=10.556）

J15. Z. Yang, K. Li, Q. Niu, Y. Xue, A. Foley. A Self-Learning Teaching-Learning Based Optimization for Dynamic Economic/Environmental Dispatch Considering Multiple Plug-in Electric Vehicle Loads. Journal of Modern Power System and Clean Energy, 2014, 2(4): 298-307; (Most citation award) （JCR Q2, IF=2.848）

J16. W. Liu, Z. Yang*, Kexin Bi, Forecasting the Acquisition of University Spin-outs: An RBF Neural Network Approach, Complexity, 2017; （JCR Q1, IF=2.591）

J17. C. Li, H. Wu, Z. Yang*, Y. Wang, Z. Sun, SHLNN based Robust Control and Tracking for Hypersonic Vehicle under Parameter Uncertainty, Complexity, 2017; （JCR Q1, IF=2.591）

J18.  Y. Guo, Z. Yang*, S. Feng, J. Hu, Complex power system status monitoring and evaluation using Big Data platform and Machine Learning algorithms: a review and a case study, Complexity, article ID 8496187, 2018; （JCR Q1, IF=2.591）

J19. 朱晓东，王颖，杨之乐*，郭媛君，启发式多目标优化算法在能源和电力系统中的典型应用综述，郑州大学学报（工学版），2019.09;

X. Zhu, Y. Wang, Z. Yang* and Y. Guo, A survey of featured applications of heuristic multi-objective optimization algorithms in power and energy systems, Journal of Zhengzhou University, 2019.09 (In Chinese);

J20. 杨之乐, 郑学理, 苏伟, 费敏锐, 付敬奇, 工业无线网络测控系统OPC数据服务器的设计实现, 计算机测量与控制, 2013. Vol 21 (04), pp 865-869

Z. Yang, X. Zheng, W. Su, M. Fei, J. Fu, A design of OPC data server for industrial wireless network measurement system, Computer Measurement and Control,  2013. Vol 21 (04), pp 865-869 (In Chinese)

J21. 杨之乐, 王秉臣, 费敏锐, 姚奇, 侯维岩, 基于令牌环的两层工业无线测控网络系统的设计与实现, 仪表技术, 2011.10

Z. Yang, B. Wang, M. Fei, Q. Yao, W. Hou, Design and implementation for a token based two-layers industrial wireless network control system, Instrumentation Technology, 2011.10 (In Chinese)

 J22. L. Li, Y. Liu, Z. Yang, J. Tan, Method to improve convergence performance of iterative learning control systems with bounded noise, Journal of the Franklin Institute, 2020, 357: 1644-1670.

J23. Y. Xu, X. Li, X. Yang, Z. Yang, L. Wu, Q. Chen. A two-stage model for rate-dependent inverse hysteresis in reluctance actuators. Mechanical Systems and Signal Processing, 2020, 135: 106427.

J24. L. Yin, J. Chen, H. Zhang, Z. Yang, Q. Wan, L. Ning, J. Hu, Q. Yu, Improving emergency evacuation planning with mobile phone location data, Environment and Planning B: Urban Analytics and City Science, 2020;

J25. S Zhang, Z Yu, B Zhou, Z Yang, D Yang, A decentralized optimization strategy for distributed generators power allocation in microgrids based on load demand-power generation equivalent forecasting, Energies, 2020, 13(3): 648.

J26. Y. Xu, Z. Yang, X. Li and X. Yang, An Improved Teaching-Learning-based Optimization Approach using Dynamic Opposite Learning, Knowledge-based systems, 2020, 104966;

J27. J. Lin, S. Feng, Z. Yang, Y. Zhang, Y. Zhang, A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access, Neurocomputing, 2020, 382, 52-63;

J28. B Zhou, X Yang, D Yang, Z Yang, T Littler, H Li, Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration, Energies, 2020, 12(22), 1-24

J29. K. Xu, Z. Yang, Y. Xu, L. Feng, A Novel Interactive Fusion Method with Images and Point Clouds for 3D Object Detection. Applied Sciences, 2019, 9(6), 1065.

J30. Niu Q, Wang H, Sun Z, Z. Yang. An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants. Energies, 2019, 12(23): 4480.

J31. W. Liu, Y. Tao; Z. Yang; K. Bi, Exploring and visualizing the patent collaboration network:  A case study of smart grid field in China, Sustainability, 11(2), 465;

J32. Q Niu, K Jiang, Z Yang, An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem's Integration with Electric Vehicles, Sustainability, 2019, 11 (24), 6945

J33. L. Zhang, Q. Li, Y. Guo, Z. Yang, L. Zhang, An investigation of wind direction and speed in a featured wind farm using joint probability distribution methods, Sustainability, 2019, 10(12), 4338;

J34. H. Ma, S. Shen, H. Ye, Z. Yang, M. Fei, H. Zhou, Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey, Swarm and Evolutionary Computation, 2019, 44: 365-387;

J35.  J. Na, Z. Yang, S. Kamal, L. Hu, W. Wang, Y. Zhou, Bio-Inspired Learning and Adaptation for Optimization and Control of Complex Systems, Complexity, 2019, Editorial;

J36. F. Song, Y. Liu, X. Yang, H. Xu, P. He, Z. Yang, Iterative Learning Identification and Compensation of Space-Periodic Disturbance in PMLSM Systems with Time Delay, IEEE Transactions on Industrial Electronics, 2018, 65 (9): 7579-7589;

J37. L. Li, Y. Liu, Z. Yang, X. Yang, K. Li, A mean-square error constrained approach to robust stochastic iterative learning control, IET Control Theory & Applications, 2018, 1(12): 38 - 44;

J38. H. Ma, D. Simon, P. Siarry, Z. Yang, M. Fei, Biogeography-Based Optimization: A 10-Year Review, IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 10:391-407;

J39. K. Liu, K. Li, Z. Yang, C. Zhang, J. Deng, An advanced Lithium-ion battery optimal charging strategy, Electrochimica Acta, 2017, Vol. 225, 330-344;

J40. T. Cheng, M. Chen, P. J. Fleming, Z. Yang, S. Gan, A novel hybrid teaching learning based multi-objective particle swarm optimization and its application in optimal placement of distributed generation, Neurocomputing, 2017, 222, 12-25;

J41. H. Ma, M. Fei, Z. Yang. Biogeography-based optimization for identifying promising compounds in chemical process. Neurocomputing, 2016, 174: 494-499;

J42. J. Yan, K. Li, E. Bai, Z. Yang, A. Foley, Time series wind power forecasting based on variant Gaussian Process and TLBO, Neurocomputing, 2016, 189:135–144;

J43. W. Liu, X. Xu, Z. Yang, J. Zhao and J. Xing, Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance, Sustainability, 2016, 8, 846.;

J44. Y. Liu, Z. Chen, Z. Yang, K. Li, J. Tan, An Inline Surface Measurement Method for Membrane Mirror Fabrication Using Two-stage Trained Zernike Polynomials and Elitist Teaching-Learning Based Optimization, Measurement Science and Technology, 2016, 27(12): 124005;

J45. Y. Guo, K. Li, Z. Yang, J. Deng, D. Laverty, A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring, Electric Power Systems Research, 2015, 127: 197-205;

J46. Z. Sun, K. Li, Z. Yang, Q. Niu, A. Foley , Impact of Electric Vehicles on a Carbon Constrained Power System - A post 2020 case study,  Journal of Power and Energy Engineering, 2015, 3: 114-122;

J47. L. Zhang, Q. Niu, Z. Yang and Kang Li, Integration of Electric Vehicles Charging in Unit Commitment, International Journal of Computer Science and Electronics Engineering, 2015, Vol.3, Iss.1, pp 22-27;

J48. H. Ma, M. Fei, Z. Yang, H. Wang, Wireless networked learning control system based on Kalman filter and biogeography-based optimization method. Transactions of the Institute of Measurement and Control, 2014, Vol. 36(2) 224–236;

J49. 杨东升，王道浩，周博文，陈麒宇，杨之乐，胥国毅，崔明建，泛在电力物联网的关键技术与应用前景，发电技术 40 (2), 107-114

J50. 朱俊丞，杨之乐，郭媛君，于坤杰，张建康，穆晓敏，深度学习在电力负荷预测中的应用综述，郑州大学学报（工学版），2019.09;

J. Zhu, Z. Yang, Y. Guo, J. Zhang, X. Mu, Deep Learning Applications in Power System Load Forecasting: a Survey, Journal of Zhengzhou University, 2019.09  (In Chinese);

J51. 吴帆, 杨之乐, 林小玲, 韩正之, 一种嵌入式无线车辆信息采集系统设计, 传感器与微系统, 2013, Vol. 32(02), pp 116-118

F. Wu, Z. Yang, X. Lin, Z. Han, An embedded system based wireless data system for collecting vehicle information, Transducer and Micro-system technologies, 2013, Vol. 32(02), pp 116-118. (In Chinese)

J52. 杨睿昕, 王任杰, 林小玲, 杨之乐, 基于无线磁阻传感器的车辆信息采集系统研究与实现, 仪表技术, 2011, Vol. 11, pp 23-26

R. Yang, R. Yang, X. Lin, Z. Yang, Design and implementation of a wireless data system based on magnetoresistive sensor for vehicle data collection, Instrumentation Technology, 2011, Vol. 11, pp 23-26 (In Chinese)

       Conference papers

C1.   Z. Yang, Y. Guo, Q. Niu, H. Ma, Y. Zhou and L. Zhang A Novel Binary Jaya Optimization for Economic/Emission Unit Commitment, in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2018: 1-8;

C2.   Z. Yang, Q. Niu, Y. Guo, H. Ma and B. Qu, A Fast Hybrid Meta-Heuristic Algorithm for Economic/Environment Unit Commitment with Renewables and Plug-In Electric Vehicles, International Conference on Swarm Intelligence, 2018, 477-486;

C3.   Z. Yang, K. Li, X. Xu, A Hybrid Meta-heuristic Method for Unit Commitment Considering Flexible Charging and Discharging of Plug-in Electric Vehicles, in 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2016: 1-8;

C4.   Z. Yang, K. Li, L. Zhang, Binary Teaching-Learning Based Optimization for Power System Unit Commitment, 11th International Conference on Control (Control2016), IEEE, 2016: 1-8;

C5.   Z. Yang, K. Li, Q. Niu, A. Foley, Unit Commitment Considering Multiple Charging and Discharging Scenarios of Plug-in Electric Vehicles, in International Joint Conference on Neural Networks (IJCNN), IEEE, 2015: 1-8;

C6.   Z. Yang, K. Li, A. Foley, and C. Zhang, Optimal scheduling methods to integrate plug-in electric vehicles with the power system: a review, in Proceedings of the 19th world congress of the International Federation of Automatic Control (IFAC’14), Cape Town, South Africa. 2014: 24-29;

C7.   Z. Yang, K. Li, Q. Niu, C. Zhang, A. Foley, Non-convex Dynamic Economic/Environmental Dispatch with Plug-in Electric Vehicle Loads, in IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), 2014. IEEE, 2014: 1-7;

C8.   Z. Yang, K. Li, A. Foley, and C. Zhang, A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles, in Evolutionary Computation (CEC), 2014 IEEE Congress on. IEEE, 2014: 2685-2691;

C9.   Z. Yang, K. Li, Y. Guo, A New Compact Teaching-Learning-Based Optimization Method, Lecture Notes in Computer Science, Volume 8589, 2014, pp 717-726;

C10. Z. Yang, M. Fei, W. Hou, B. Wang, The Design and Simulation of a Two-Layer Network Protocol for Industrial Wireless Monitoring and Control System, Communications in Computer and Information Science, Volume 323, 2012, pp 405-413;

C11.             Y. Wang, Z. Yang*, Y. Guo, J. Zhu and X. Zhu, A novel multi-objective competitive swarm optimization algorithm for multi-modal multi objective problems, in 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2019: 1-8;

C12. J. Zhu, Z. Yang*, Y. Chang, Y. Guo and J. Zhang, A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction, 2019 IEEE PES Innovative Smart Grid Technologies Asia, IEEE, 2019: 1-6;

C13. X Zhou, D Yang, B Zhou, Z Yang, Regional Adaptability and Economic Evaluation Based on Electric Vehicle Policy Analysis, 8th Renewable Power Generation Conference (RPG 2019), 2019 page (7 pp.)

C14. Y. Xu, X. Li, L. Wu, Z. Yang, P. Zhang, X. Yang, A direct inverse hysteresis model and its application in reluctance actuators. 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 1-6

C15.  W. Lin, Y. Guo, Z. Yang, J. Zhu, Y. Wang, Y. Liu, Y. Wang, A Novel Big Data Platform for City Power Grid Status Estimation, The Fourth International Conference on Data Mining and Big Data (DMBD’2019), 1-8

C16.  W. Ding, Z. Yang and L. Feng, A Natural Scene Edge Detection Algorithm Based on Image Fusion, International Conference on Video and Image Processing (ICVIP 2018), IEEE, 2019: 1-7;

C17. K Xu, Z Yang, Y Xu, L Feng, Residual Blocks PointNet: A novel faster PointNet framework for segmentation and estimated pose, IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS 2018) , IEEE, 2018: 1-6;

C18.  K. Xu, Z. Yang, B. Feng, A novel interactive fusion method with images and point clouds for 3D object detection, IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS 2018) , IEEE, 2018: 1-6; (Best student paper nominated)

C19.  K. Deveerasetty, Y. Zhou, Z. Yang, Q. Wu, Robust control design for the trajectory tracking of a quadrotor, IEEE International Conference on Cyborg and Bionic Systems 2018, 351-356

C20. F. Zhou, H. Wang, Z. Yang, Human localization and tracking system based on multi depth cameras in VR scene, IEEE International Conference on Cyborg and Bionic Systems 2018, 340-345

C21.  Z. Liu, X. Zeng and Z. Yang, Demand Based Bidding Strategies under Interval Demand for Integrated Demand and Supply Management in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2018: 1-8;

C22.  L. Zhang, K. Li, Z. Yang, X. Li, Y. Guo, D. Du and C. Wong, Compact Neural Modeling of Single Flow Zinc-Nickel Batteries Based on Jaya Optimization, in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2018: 1-8;

C23.  Y. Guo, Z. Yang, Y. Zhou, J. Zhang and L. Zhang, Detection of Transient Signals in Smart Grid Using Artificial Neural Network Modeling and JAYA Optimization, 2018 IEEE PES Innovative Smart Grid Technologies Asia, IEEE, 2018: 1-6;

C24. Y. Zhou, Z. Yang, Y. Guo and Q. Wu, The College Compus Energy Monitoring Platform for Artificial Intelligence Application, 2018 IEEE PES Innovative Smart Grid Technologies Asia, IEEE, 2018: 1-6;

C25. F. Zhou, H. Wang, Z. Yang, A Novel 3D Head Multi-feature Constraint Method for Human Localization based on Multiple Depth Cameras, IMIOT & ICSEE 2018, Communications in Computer and Information Science, 2018;

C26. Y Liu, Y Guo, Z Yang, J Hu, G Lu, Y Wang, Power system transmission line tripping analysis using a big data platform with 3D visualization, Computational Intelligence (SSCI), 2017 IEEE Symposium Series on, 1-8;

C27. Y. Liu, H. Luo, Z. Fu, Z. Yang and X. Yang, Integral Sliding Mode Based Precision Motion Control for PMLM, Communications in Computer and Information Science, 2017;

C28. F. Song, Y. Liu, X. Yang, Z. Yang and P. He, Iterative Learning Identification with Bias Compensation for Stochastic Linear Time-Varying Systems, Communications in Computer and Information Science, 2017;

C29. H. Ma, P. You, K. Liu, Z. Yang, M. Fei, Optimal Battery Charging Strategy Based on Complex System Optimization, Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration, 371-378, Communications in Computer and Information Science, 2017;

C30. Z. Yang, Z. Yang, K. Li, W. Naeem, K. Liu, Heuristic Based Terminal Iterative Learning Control of ISBM Reheating Processes, Intelligent Computing, Networked Control, and Their Engineering Applications, Communications in Computer and Information Science, 2017;

C31. X. Li, K. Li, Z. Yang and C. K. Wong, A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries, Communications in Computer and Information Science, 2017;

C32. X. Li, K. Li and Z. Yang, Teaching-Learning-Feedback-Based Optimization, International Conference on Swarm Intelligence (ICSI’2017), accepted;

C33.             X. Li, C. K Wong and Z. Yang, A Novel Flowrate Control Method for Single Flow Zinc/Nickel Battery, International Conference for Students on Applied Engineering (ICSAE 2016). IEEE 2016: 30-35.

C34.             K. Liu, K. Li, Z. Yang, C. Zhang, J. Deng, Battery optimal charging strategy for a coupled thermoelectric model, in 2016 IEEE Congress on Evolutionary Computation (CEC) , 2016: 1-8;

C35.             T. Cheng, M. Chen, P. J. Fleming, Z. Yang and S. Gan, An Effective PSO-TLBO Algorithm for multi-objective Optimization, in 2016 IEEE Congress on Evolutionary Computation (CEC) , 2016: 1-8 (EI);

C36.             L. Zhang, K. Li, Z. Yang, Z. Yang and Q. Wang TRIZ Based Teaching Strategy for Wind Turbine Control, 11th International Conference on Control (Control2016), IEEE, 2016: 1-8;

C37.             H. Ma, Z. Yang, P. You and M. Fei, Complex System Optimization for Economic Emission Load Dispatch, 11th International Conference on Control (Control2016), IEEE, 2016: 1-8;

C38.             J. Yan, Z. Yang, K. Li and Y. Xue, A Variant Gaussian Process for Short-term Wind Power Forecasting Based on TLBO, Communications in Computer and Information Science, Vol. 463, 2014, pp 165-174 (Best paper award);

C39.             C. Zhang, Z. Yang, and K. Li, Modeling of electric vehicle batteries using rbf neural networks, in The 2nd International Conference on Computing, Management and Telecommunications. IEEE, 2014, 116-121;

C40.             C. Zhang, K. Li, S. Mcloone, and Z. Yang, Battery modelling methods for electric vehicles -a review, in 13th European Control Conference (ECC). IEEE, 2014, 2673-2678;

C41.             C. Zhang, K. Li, Z. Yang, L. Pei, and C. Zhu, A new battery modelling method based on simulation error minimization, in IEEE Power and Energy Society General Meeting 2014. 1-6;

已指导学生

杨猛  硕士研究生  085404-计算机技术  

邓忠辉  硕士研究生  085400-电子信息  

现指导学生

周文康  硕士研究生  085404-计算机技术  

杨谨宁  博士研究生  081104-模式识别与智能系统  

周佳靖  硕士研究生  085406-控制工程