Lebesgue-Sampling-based Li-Battery Whole-Service-Life SOC Estimation Using Simplified First Principle (SFP) Model



Published Oct 28, 2022
Enhui Liu


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

Liu, E. (2022). Lebesgue-Sampling-based Li-Battery Whole-Service-Life SOC Estimation Using Simplified First Principle (SFP) Model. Annual Conference of the PHM Society, 14(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/3410
Abstract 262 |



simplified first principle model (SFP), state of charge (SOC), remaining dischargeable time prediction (RDT), Lebesgue sampling (LS), Extended Kalman filter (EKF)

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