Energies 2017, 10(4), 512; doi:10.3390/en10040512

An Online SOC and SOH Estimation Model for Lithium-Ion Batteries

Shyh-Chin Huanga,b, Kuo-Hsin Tsenga, Jin-Wei Lianga, Chung-Liang Changc, and Michael G. Pecht d
a Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
b College of Engineering, Chang Gung University, Taoyuan 33302, Taiwan
c Center for Reliability Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
d CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


The monitoring and prognosis of cell degradation in lithium-ion (Li-ion) batteries are essential for assuring the reliability and safety of electric and hybrid vehicles. This paper aims to develop a reliable and accurate model for online, simultaneous state-of-charge (SOC) and state-of-health (SOH) estimations of Li-ion batteries. Through the analysis of battery cycle-life test data, the instantaneous discharging voltage (V) and its unit time voltage drop, V′, are proposed as the model parameters for the SOC equation. The SOH equation is found to have a linear relationship with 1/V′ times the modification factor, which is a function of SOC. Four batteries are tested in the laboratory, and the data are regressed for the model coefficients. The results show that the model built upon the data from one single cell is able to estimate the SOC and SOH of the three other cells within a 5% error bound. The derived model is also proven to be robust. A random sampling test to simulate the online real-time SOC and SOH estimation proves that this model is accurate and can be potentially used in an electric vehicle battery management system (BMS).

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