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Published in Journal of Power Sources, 2022
Y. Zhang, T. Wik, J. Bergström, M. Pecht, C. Zou,
Using machine learning, we developed a powerful framework for predicting battery aging and extending battery life. Our approach, tested on real-world electric vehicle data, achieves high accuracy with low computational requirements, providing significant potential for improving battery management systems.
Published in Journal of Power Sources, 2022
Y. Zhang, T. Wik, J. Bergström, C. Zou,
We developed a state-of-health estimation method for lithium-ion batteries using a data-driven multi-model fusion approach. By combining machine learning models and a Kalman filter, our approach provides accurate and robust health estimates under varied operating conditions, enabling safer and more reliable battery management in electric vehicles.
Published in Journal of Power Sources, 2023
Y. Zhang, T. Wik, Y. Huang, J. Bergström, C. Zou,
Our study presents a novel approach for early battery life prediction by combining time-series and usage-related data, achieving robust predictions even with limited early data. This method provides a more accurate and practical solution for managing battery health across diverse conditions, paving the way for improved battery management systems.
Published in Energy Storage Materials, 2024
Y. Zhang, T. Wik, J. Bergström, C. Zou,
Our study introduces a machine learning-based framework for lifelong estimation of lithium plating potential, enabling faster and safer battery charging. By maintaining plating potential control, our approach achieves 30% faster charging and significantly extends battery life, offering a practical solution for efficient electric vehicle battery management.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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