The state-of-charge (SOC) estimation and remaining-dischargeable-time (RDT) prediction are critical and challenging to the safe operation of Lithium-ion batteries (LIBs). The main challenges are the limited accuracy of traditional equivalent circuit models and the computation inefficiency of electrochemical battery models. Aligning with the objective, my Ph.D. research aims to propose a Lebesgue-sampling (LS) based SOC estimation and RDT prediction using a simplified first principle (SFP) model with the consideration of state of health (SOH) degradation, and eventually achieve an integration of LS and SFP model for
SOC estimation and RDT prediction with the consideration of SOH, which should have high performance in terms of accuracy, efficiency, and applicability. My proposed solution will be verified and validated by experimental data as well as some other public data sets.
How to Cite
simplified first principle model (SFP), state of charge (SOC), remaining dischargeable time prediction (RDT), Lebesgue sampling (LS), Extended Kalman filter (EKF)
 Z. Chen, H.Sun, G. Dong, J. Wei, and J. Wu. Particle filter-based state-of-charge estimation and remaining dischargeable-time prediction method for lithium-ion
batteries. Journal of Power Sources, 414:158–166, 2019.
 W. Yan, B. Zhang, G. Zhao, S. Tang, G. Niu, and X. Wang. A battery management system with a Lebesgue-sampling-based extended Kalman filter. IEEE Transactions on Industrial Electronics, 66(4):3227–3236, 2019.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.