I received my M.S. degree (with honors) in Electrical and Control Engineering from Fudan University in 2022, China. I’m currently a final year Ph.D. candidate working on the interface of energy storage, power systems, electrochemistry and aritificail intelligence (AI), sponsored by a joint training program from Tsinghua University and UC Berkeley. I work with Prof. Xuan Zhang, Guangmin Zhou, Scott Moura with a balanced focus on real wolrd applicaiton and theoretical depth. I was also a research intern in Tencent AI Lab and Microsoft Research Asia (MSRA). | [CV] (updated 21 Jan 2025)

My research interest includes AI-enabled applications for sustainable use of retired electric vehicle batteries (reuse and recycling), e.g., power grid energy storage and critical material recycling. Special attention is paid to state estimation and aging pattern diagnosis under limited and heterogeneous data availability. I also work closely with material scientist on AI for Science (AI4S) topics.

I have published serveral articles on Nature Catalysis（2023）, Nature Communications（2024a；2024b；2023）, Energy and Environmental Science（2025）, ACS Energy Letters, Journal of Energy Chemistry, Journal of Power Sources and Applied Energy, etc.

🔥 News

2025.03: 🎉🎉🎉 I was invited to the Battery Modeling Webinar Series (BMWS) for a globally attended talk, hosted by Hongyi Lin (UMich). The talk title is Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions. | [More Info]
2025.01: 🎉🎉🎉 Our paper has been published at Energy Environ. Sci., and selected as the back cover paper! | [pic]
2025.01: 🎉🎉🎉 I am nominated Youth Talent Support Project (Special Program for Doctoral Students) sponsored by the China Association for Science and Technology (CAST) and specifically by the China Society of Automotive Engineers (CSAE) 中国科协青年人才托举工程博士生专项计划. My mentors are Professor Minggao Ouyang and Professor Jiangong Zhu. | [pic]
2024.12: 🪫♻️🔋 I am attending The 3rd Battery Sustainability Workshop 2024 initiated by Prof. Martin Bazant and others at MIT. | [pic]
2024.11: 🎉🎉🎉 Our project Large Scale Rapid Internal State Estimation Technology and Applications for Sustainable Utilization of Retired Batteries are granted the first prize (rank 1st) in the 3rd China Postgraduate “Carbon Peak and Carbon Neutrality” Innovation and Creativity Competition, hosted by China Postgraduate Innovation and Practice Competition Series (CPIPC). | [link]. Special congratulations to Prof. Xuan Zhang for the Excellent Supervisor Award! | [pic]
2024.09: 🎉🎉🎉 I am visiting the Energy, Controls and Applications Lab (eCAL) at UC Berkeley, working with Prof. Scott Moura.

📝 Publications

Energy Environ. Sci.,18, 1544 (2025)

Non-destructive degradation trajectory prediction needs observations that has not yet been established. Here, the authors propose a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly and time-consuming data curation for sustainable manufacturing, reuse, and recycling.

Tao, S., Zhang, M., Zhao, Z. et al.

Dataset here, Back Cover here

Nat Commun 15, 10154 (2024)

Data scarcity and heterogeneity impede the estimation of retired battery capacity. Here, the authors propose a generative learning method that extends measured data space, potentially reducing curation time, cost and facilitating their sustainable reuse and recycling.

Tao, S., Ma, R., Zhao, Z. et al.

Dataset here

Nat Commun 15, 7641 (2024)

Reuse and recycling of retired electric vehicle batteries offer sustainable waste management but face decision challenges. Here, the authors present a strategy with an accessible economic and environmental evaluation framework for treating these batteries.

Ma, R., Tao, S., Sun, X. et al.

Code here

Nat Commun 14, 8032 (2023)

Unsorted retired batteries pose recycling challenges due to diverse cathodes. Here, the authors propose a privacy-preserving machine learning system that enables accurate sorting with minimal data, important for a sustainable battery recycling industry.

Tao, S., Liu, H., Sun, C. et al.

Selected as Top 25 paper and Energy Focus paper.

J = Journal, S = Submitted

[J16] Tao, S., Zhang, M., Zhao, Z. et al. Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning. Energy \& Enviromental Science, (2025). Click to view.
[J15] Tao, S., Ma, R., Zhao, Z. et al. Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions. Nature Communications 15, 10154 (2024). Click to view.
[J14] Ma, R., Tao, S. et al. Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions. Nature Communications 15, 7641 (2024). Click to view.
[J13] Tao, S. et al. Rapid and sustainable battery health diagnosis for recycling pretreatment using fast pulse test and random forest machine learning. Journal of Power Sources 597, 234156 (2024). Click to view.
[J12] Tao, S., Liu, H., Sun, C. et al. Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning. Nature Communications 14, 8032 (2023). Click to view, selected as Editor’s highlight paper and the Focus paper.
[J11] Tao, S., Sun, C., Fu, S. et al. Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning. ACS Energy Letters 8, 3269-3279 (2023). Click to view.
[J10] Tao, S. et al. The proactive maintenance for the irreversible sulfation in lead-based energy storage systems with a novel resonance method. Journal of Energy Storage 42, 103093 (2021). Click to view.
[J9] Fu, S, Tao, S. et al. Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method. Applied Energy 353, 121991 (2024). Click to view.
[J8] Han, Z., Gao, R., Wang, T., Tao, S. et al. Machine-learning-assisted design of a binary descriptor to decipher electronic and structural effects on sulfur reduction kinetics. Nature Catalysis 6, 1073-1086 (2023). Click to view. Selected as cover paper in Nature Catalysis.
[J7] Tao, S. et al. Behavioral Economics Optimized Renewable Power Grid: A Case Study of Household Energy Storage. Energies 14, 4154 (2021). Click to view.
[J6] Talihati, B., Tao, S. et al. Energy storage sharing in residential communities with controllable loads for enhanced operational efficiency and profitability. Applied Energy 373, 123880 (2024). Click to view.
[J5] Liu, X., Tao, S. et al. Binary multi-frequency signal for accurate and rapid electrochemical impedance spectroscopy acquisition in lithium-ion batteries. Applied Energy 364, 123221 (2024). Click to view.
[J4] He, K. , Tao, S. et al. A Novel Quick Screening Method for the Second Usage of Parallel-connected Lithium-ion Cells Based on the Current Distribution. Journal of The Electrochemical Society 170, 030514 (2023). Click to view.
[J3] Li, T. , Tao, S. et al. V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model. Frontiers in Energy Research 9 (2021). Click to view
[J2] Hu, M. , Tao, S. et al. Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation. Sensors 21, 5366 (2021). Click to view
[J1] T. Cao, Y. Xu, G. Liu, Tao, S. et al. Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station. Applied Energy 371, 123751 (2024). Click to view.
[S5] Tao, S., et al., Immediate remaining capacity estimation of heterogeneous retired lithium-ion batteries via deep generative transfer learning.
[S4] Tao, S., et al., The role of hysteresis effect in battery energy storage systems on supporting grid frequency stability. Power and Energy Society General Meeting (PESGM)
[S3] Tao, S., et al., Artificial intelligence for sustainable battery reuse, recycling, and remanufacturing.
[S2] Piao, Z., Han, Z., Tao, S., et al., Deciphering failure trajectories in lithium metal anodes through electrochemical curve fingerprint.
[S1] Han, Z., Tao, S., Jia, Y., et al., Discovering catalysis principles by crowd intelligence.

🎖 Honors and Awards

2025.01: 🎉🎉🎉 I am nominated Youth Talent Support Project (Special Program for Doctoral Students) sponsored by the China Association for Science and Technology (CAST) and specifically by the China Society of Automotive Engineers (CSAE) 中国科协青年人才托举工程博士生专项计划. My mentors are Professor Minggao Ouyang and Professor Jiangong Zhu. | [pic]
2024.12: 🎉🎉🎉 I am granted the Wang Dazhong Scholarship [王大中奖学金] (0.01%).
2024.11: 🎉🎉🎉 My leading project Large Scale Rapid Internal State Estimation Technology and Applications for Sustainable Utilization of Retired Batteries are granted the first prize (rank 1st) in the 3rd China Postgraduate “Carbon Peak and Carbon Neutrality” Innovation and Creativity Competition, hosted by CPIPC.
2024.11: 🎉🎉🎉 I am nominated the Tsinghua Presidential Scholarship [清华大学特等奖学金] (0.01%, finalist).
2024.10: 🎉🎉🎉 I am granted the National Scholarship (Doctoral Students) (1%, rank first among all candidates) [国家奖学金(博士研究生)] (0.01%).
2024.08: 🎉🎉🎉 I am granted the Future Leaders Scholarship (Excellent Scholarship), Tsinghua Berkeley Shenzhen Institute (TBSI).

📖 Educations

2022.08 - 2025.6 (expected), Tsinghua University, Beijing, China Supervised by Prof. Guangmin Zhou and Xuan Zhang Ph.D. candidate in Electrical Engineering GPA: 3.9/4.0
2024.09 - now, UC Berkeley, CA, USA Supervised by Prof. Scott J. Moura Visiting Research Scholar in Energy, Controls and Applications Lab (eCAL)
2019.09 - 2022.6, Fudan University, Shanghai, China Supervised by Prof. Yaojie Sun M.S. Electrical and Control Engineering(with honors) GPA: 3.85/4.0 (1/64)
2015.09 - 2019.6, Shanghai Ocean University, Shanghai, China Supervised by Prof. Yue Zhou and Chen Yang B.S. Electrical and Control Engineering (with honors) GPA: 3.95/4.0 (1/62)

💬 Invited Talks

2025.03: 🎉🎉🎉 I was invited to the Battery Modeling Webinar Series (BMWS) for a globally attended talk, hosted by Hongyi Lin (UMich). The talk title is Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions. | [More Info]
2023.11, Tao, S., Generating the voltage response of retired batteries for data augmentation from pulse test using variational autoencoder, Best Paper, and Best Oral presentation at New Energy Science and Electrification of Transportation International Conference, Elsevier, eTransportation.
2023.10, Tao, S., Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning, Best Paper, and Best Oral presentation at closing ceremony of International Carbon Neutrality Forum for Doctoral Students, Tsinghua University.

💻 Internships