2.          

Optimization   control of solid-state battery charge and discharge by sensing key mechanism   characteristics, Postdoctoral Science Foundation, China, 2021-2023. (PI,    0.08 million CNY)

3.          

Electrochemical   modeling and charge-discharge optimization control of solid-state batteries   for vehicles, Science Foundation for Postdoctoral Researcher, Chongqing   Science and Technology Bureau, China, 2020-2022. (PI,  0.1 million CNY)

4.          

Key   research project on battery SOC and SOH estimation algorithm development,   Shangyi Xin Group, 2021. (PI, 0.2 million CNY)

5.          

Key   research project on life cycle health assessment of traction battery, China   Automotive Engineering Research Institute Co., Ltd., 2021. (PI, 0.35 million CNY)

6.          

The   thermal-electric coupling mechanism and efficient management of traction   battery systems of electric vehicles in cold regions, NSF, China, 2021-2023.   (key participant, 2.6 million CNY)

7.          

Critical   physical features estimation and optimization for EV lithium-ion batteries, a   joint project of NSF, China, and STINT, Sweden, 2021-2023. (key participant, 0.4 million CNY)

8.          

Key   research project on state estimation and thermal management of traction   battery packs without modules, Guangdong Science and Technology Bureau,   China, 2021-2023. (key participant,   1 million CNY)

9.          

Key   research project on thermal modeling, temperature estimation, and cell-pack   life predication of lithium-ion batteries, Huawei, 2021. (key participant, 1.2 million CNY)

10.       

Key   research project on development of electrochemical parameter identification   technology for lithium-ion batteries, ATL, 2021. (key participant, 0.55 million CNY)

11.       

Intelligent   management and optimal control of fuel cell bus dual power source system,   Science & Technology Department of Sichuan Province, 2020-2022. (key participant, 0.5 million CNY)

1.            

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, pp. 1-1, 2021. DOI:   10.1109/TPEL.2021.3134701

2.            

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.

3.            

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.

4.            

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 highly   cited paper, top 1% of papers )

5.            

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 highly cited paper, top 1% of papers )

6.            

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.

7.            

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.

8.            

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.

9.            

Z. Deng, L. Yang*, Y. Cai,   and H. Deng, "Online Identification with Reliability Criterion and State   of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for   Lithium-Ion Batteries," Energies,   vol. 9, no. 6, p. 472, 2016.

10.         

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.

11.         

L. Jiang, Z. Deng (equally   contribution), 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.

12.         

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.

13.         

J. Jia, X. Hu, Z. Deng*, H.   Xu, W. Xiao, F. Han, "Data-driven Comprehensive Evaluation of   Lithium-ion Battery State of Health and Abnormal Battery Screening," Journal of Mechanical Engineering,   vol. 57, no. 14, pp. 141-149+159, 2021.

14.         

Z. Deng, W. Xiao, Y. Li, Y. Huang, J. Jia, X. Hu*, " Cycle Mileage Prediction of Electric Vehicle   over Macro Time Scale," Journal of   Mechanical Engineering, 2021. (accepted)

15.         

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 Review, vol. 156, p. 111843, 2022.

16.         

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.

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.         

Z Deng*, X Lin, J Cai, X   Hu*. Battery Health Estimation with Degradation Pattern Recognition and   Transfer Learning. Journal of Power   Sources. 2021. (under   review)

19.         

Hu X, Deng   Z, Lin X, Xie Y, Teodorescu R. Research directions for next-generation   battery management solutions in automotive applications. Renewable and Sustainable Energy Reviews. 2021;152:111695.

20.         

Che Y, Deng   Z, Li P, Tang X*, Khosravinia K, Lin X, et al. State of health   prognostics for series battery packs: A universal deep learning method. Energy. 2022;238:121857.

Conference   papers

1.            

Z. Deng, L. Yang*, Y. Cai,   and H. Deng, "Maximum available capacity and energy estimation based on   support vector machine regression for lithium-ion battery," Energy Procedia, vol. 107, pp. 68-75,   2017.

2.            

X. Zeng, L. Xu, Z. Deng, F. Feng, and X. Hu, "Global Sensitivity Analysis of   Battery Single Particle Model Parameters," 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), 2019:   IEEE, pp. 1-6.

3.            

Xu L., Deng   Z., Hu X*. Battery Health Estimation Using Electrochemical Aging Model   and Ensemble Kalman Filtering, IEEE   International Future Energy Electronics Conference (IFEEC), Taipei, Nov.   16-19, 2021.

4.            

Deng X., Deng Z., Hu X*. Distributed Collaborative Control of Multiple   Smart Homes via Consensus ADMM, IEEE   International Future Energy Electronics Conference (IFEEC), Taipei, Nov.   16-19, 2021.

5.            

Che Y., Deng   Z., Hu X*. Battery pack state of health estimation with general health   indicators and modified gaussian process regression, The 34th International Electric Vehicle Symposium and Exhibition   (EVS34), NANJING, JUNE 25-28, 2021.

6.            

Wang P., Deng Z., Che Y., Hu X*. High-precision state of charge estimation   for lithium-ion battery packs by combining extended Kalman filter and   Gaussian process regression, The   34th International Electric Vehicle Symposium and Exhibition (EVS34),   NANJING, JUNE 25-28, 2021.