Publications

Journal Articles

Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging

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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Early prediction of battery life by learning from both time-series and histogram data

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.

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State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion

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.

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A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data

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.

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Patents

Y. Zhang, T, Wik, C, Zou. “A method for estimation state of health of a battery,” EU Patent App. EP22216134.1

Y. Zhang, T, Wik, C, Zou. “A method for real-time estimation of battery anode potential,” EU Patent App. EP24170319.8