4 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…
Data-Selective Online Battery Identification Using Extended Time Regular Expressions
Nicolai A. Weinreich, Marco Muñiz, Marius MikuÄionis +2
In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehi…
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…