13 citations · 24 across the 5 of their papers we have counts for
5 papers
Acceleration of Modelling with Physics Informed Learning: Frameworks and Perspectives for Real-Time Control of Electrochemical Devices
Remus Teodorescu, Yusheng Zheng, Yi Zhuang +2
Electrochemical devices (batteries, fuel cells, and electrolyzers) are in full development, driven by the green energy transition. Their real-time control requires ms predictions i…
Physics-informed neural network surrogate modeling of single particle model for lithium-ion batteries
Yi Zhuang, Yusheng Zheng, Yunhong Che +1
Physics-based models play a key role in battery management, yet face challenges in real-time applications due to the high computational cost of solving coupled algebraic-partial di…
Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Yusheng Zheng, Wenxue Liu, Yunhong Che +6
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal manag…
Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement
Abhijit Kulkarni, Ahsan Nadeem, Roberta Di Fonso +2
Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also use…
Data-Enabled Predictive Control for Fast Charging of Lithium-Ion Batteries with Constraint Handling
Kaixiang Zhang, Kaian Chen, Xinfan Lin +5
Fast charging of lithium-ion batteries has gained extensive research interests, but most of existing methods are either based on simple rule-based charging profiles or require expl…