scholarly journals New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques

2009 ◽  
Vol 58 (8) ◽  
pp. 3905-3916 ◽  
Author(s):  
C.R. Gould ◽  
C.M. Bingham ◽  
D.A. Stone ◽  
P. Bentley
Author(s):  
Sohel Anwar

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.


2019 ◽  
Vol 27 (5) ◽  
pp. 1862-1877 ◽  
Author(s):  
Adrien M. Bizeray ◽  
Jin-Ho Kim ◽  
Stephen R. Duncan ◽  
David A. Howey

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1620 ◽  
Author(s):  
Ngoc-Tham Tran ◽  
Abdul Khan ◽  
Thanh-Tung Nguyen ◽  
Dae-Wook Kim ◽  
Woojin Choi

2020 ◽  
Vol 67 (9) ◽  
pp. 7963-7972 ◽  
Author(s):  
Farshid Naseri ◽  
Ebrahim Farjah ◽  
Teymoor Ghanbari ◽  
Zahra Kazemi ◽  
Erik Schaltz ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4833
Author(s):  
Shida Jiang ◽  
Zhengxiang Song

Lithium-ion batteries are an attractive power source in many scenarios. In some particular cases, including providing backup power for drones, frequency modulation, and powering electric tools, lithium-ion batteries are required to discharge at a high rate (2~20 C). In this work, we present a method to estimate the state of health (SOH) of lithium-ion batteries with a high discharge rate using the battery’s impedance at three characteristic frequencies. Firstly, a battery model is used to fit the impedance spectrum of twelve LiFePO4 batteries. Secondly, a basic estimation model is built to estimate the SOH of the batteries via the parameters of the battery model. The model is trained using the data of six batteries and is tested on another six. The RMS of relative error of the model is lower than 4.2% at 10 C and lower than 2.8% at 15 C, even when the low-frequency feature of the impedance spectrum is ignored. Thirdly, we adapt the basic model so that the SOH estimation can be performed only using the battery’s impedance at three characteristic frequencies without having to measure the entire impedance spectrum. The RMS of relative error of this adapted model at 10 C and 15 C is 3.11% and 4.25%, respectively.


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