A full set of publications can be found on Google Scholar.

[1]          Y. Zhang, T. Wik, J. Bergström, M. Pecht, and C. Zou, “A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data,” J. Power Sources, vol. 526, p. 231110, Apr. 2022, doi: 10.1016/j.jpowsour.2022.231110.

[2]          Y. Zhang, T. Wik, J. Bergström, and C. Zou, “State of health estimation for lithium-ion batteries under arbitrary usage using data-driven multi-model fusion,” IEEE Trans. Transp. Electrification, pp. 1–1, 2023, doi: 10.1109/TTE.2023.3267124.

[3]           Y. Zhang, T. Wik, J. Bergström, and C. Zou, “Practical battery State of Health estimation using data-driven multi-model fusion,” IFAC-Pap., vol. 56, no. 2, pp. 3776–3781, 2023, doi: 10.1016/j.ifacol.2023.10.1305.

[4]          Y. Zhang, T. Wik, Y. Huang, J. Bergström, and C. Zou, “Early prediction of battery life by learning from both time-series and histogram data,” IFAC-Pap., vol. 56, no. 2, pp. 3770–3775, 2023, doi: 10.1016/j.ifacol.2023.10.1547.

[5] Y. Zhang, “Data-driven battery aging diagnostics and prognostics,” Licentiate thesis, 2023. Available from: https://www.proquest.com/docview/2827701896?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses