Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

By Rui Xiong, Yongzhi Zhang, Ju Wang, Hongwen He, Simin Peng, Michael G. Pecht. IEEE Transactions on Vehicular Technology, 2019

352 citations recorded by the source corpus.

This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 °C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/428e243dcd826bfd825b3967ec0a7ccdc3c3a06a

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In Automotive Engineering

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Citations in the corpus

Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019, background): “be fixed, and the battery must be charged to 100% SOC in the algorithm [36].”