scholarly journals High-Precision Monitoring of Volume Change of Commercial Lithium-Ion Batteries by Using Strain Gauges

2020 ◽  
Vol 12 (2) ◽  
pp. 557 ◽  
Author(s):  
Lisa K. Willenberg ◽  
Philipp Dechent ◽  
Georg Fuchs ◽  
Dirk Uwe Sauer ◽  
Egbert Figgemeier
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Volume Expansion ◽  
Characteristic Curve ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Strain Gauges ◽  
Curve Shape ◽  
Diameter Change

This paper proposes a testing method that allows the monitoring of the development of volume expansion of lithium-ion batteries. The overall goal is to demonstrate the impact of the volume expansion on battery ageing. The following findings are achieved: First, the characteristic curve shape of the diameter change depended on the state-of-charge and the load direction of the battery. The characteristic curve shape consisted of three areas. Second, the characteristic curve shape of the diameter change changed over ageing. Whereas the state-of-charge dependent geometric alterations were of a reversible nature. An irreversible effect over the lifetime of the cell was observed. Third, an s-shaped course of the diameter change indicated two different ageing effects that led to the diameter change variation. Both reversible and irreversible expansion increased with ageing. Fourth, a direct correlation between the diameter change and the capacity loss of this particular lithium-ion battery was observed. Fifth, computer tomography (CT) measurements showed deformation of the jelly roll and post-mortem analysis showed the formation of a covering layer and the increase in the thickness of the anode. Sixth, reproducibility and temperature stability of the strain gauges were shown. Overall, this paper provides the basis for a stable and reproducible method for volume expansion analysis applied and established by the investigation of a state-of-the-art lithium-ion battery cell. This enables the study of volume expansion and its impact on capacity and cell death.

Lithium-Ion Battery Cell Health Monitoring Using Vibration Diagnostic Test

2013 ◽  
Author(s):  
Huan L. Pham ◽  
J. Eric Dietz ◽  
Douglas E. Adams ◽  
Nathan D. Sharp
Keyword(s):  
Mechanical Properties ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Diagnostic Test ◽  
Excitation Frequency ◽  
High Capacity ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Cell

With their superior advantages of high capacity and low percentage of self-discharge, lithium-ion batteries have become the most popular choice for power storage in electric vehicles. Due to the increased potential for long life of lithium-ion batteries in vehicle applications, manufacturers are pursuing methodologies to increase the reliability of their batteries. This research project is focused on utilizing non-destructive vibration diagnostic testing methods to monitor changes in the physical properties of the lithium-ion battery electrodes, which dictate the states of charge (SOC) and states of health (SOH) of the battery cell. When the battery cell is cycled, matter is transported from one electrode to another which causes mechanical properties such as thickness, mass, stiffness of the electrodes inside a battery cell to change at different states of charge; therefore, the detection of these changes will serve to determine the state of charge of the battery cell. As mass and stiffness of the electrodes change during charge and discharge, they will respond to the excitation input differently. An automated vibration diagnostic test is developed to characterize the state of charge of a lithium-ion battery cell by measuring the amplitude and phase of the kinematic response as a function of excitation frequency at different states of charge of the battery cell and at different times in the life of the cell. Also, the mechanical properties of the electrodes at different states of charge are obtained by direct measurements to develop a first-principles frequency response model for the battery cell. The correlation between the vibration test results and the model will be used to determine the state of charge of the cell.

A novel voltage matching-adaptive extended Kalman filtering construction method for the state of charge prediction of lithium-ion batteries

2021 ◽  
pp. 014233122110294
Author(s):  
Weihao Shi ◽  
Shunli Wang ◽  
Lili Xia ◽  
Peng Yu ◽  
Bowen Li
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Extended Kalman Filtering

Accurately estimating the state of charge of lithium-ion batteries is of great significance to the development of the new energy industry. This research proposes a method for estimating the state of charge of lithium-ion batteries based on a voltage matching-adaptive extended Kalman filtering algorithm. The voltage matching part and the first-order resistance-capacitance (RC) part is combined into a new equivalent circuit model. This model improves the accuracy of voltage simulation at different charging and discharging stages through segment matching. Model-based adaptive extended Kalman filter algorithm adds a noise correction factor to adaptively correct the influence of noise on the estimation process and improve the estimation accuracy. The forgetting factor is introduced to improve the real-time performance of the algorithm. To verify the reliability of the model and algorithm, a multi-condition experiment is carried out on the lithium-ion battery. The verification results show that the simulation error of the circuit model to the working state of the lithium-ion battery is less than 0.0487V. The improved algorithm can accurately estimate the state of charge of lithium-ion batteries, the estimation accuracy of the discharge stage is 98.34%, and the estimation accuracy of the charging stage is 97.75%.

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

Accuracy analysis of the State-of-Charge and remaining run-time determination for lithium-ion batteries

Measurement ◽  
2009 ◽  
Vol 42 (8) ◽  
pp. 1131-1138 ◽  
Author(s):  
V. Pop ◽  
H.J. Bergveld ◽  
P.H.L. Notten ◽  
J.H.G. Op het Veld ◽  
P.P.L. Regtien
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Accuracy Analysis ◽  
Time Determination ◽  
Run Time

The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

2014 ◽  
Vol 63 (4) ◽  
pp. 1614-1621 ◽  
Author(s):  
Jun Xu ◽  
Chunting Chris Mi ◽  
Binggang Cao ◽  
Junjun Deng ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Proportional Integral ◽  
State Of Charge Estimation

scholarly journals State-of-Charge Monitoring by Impedance Spectroscopy during Long-Term Self-Discharge of Supercapacitors and Lithium-Ion Batteries

Batteries ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 35 ◽  
Author(s):  
Peter Kurzweil ◽  
Mikhail Shamonin
Keyword(s):  
Impedance Spectroscopy ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Charge Transfer Resistance ◽  
The State ◽  
State Of Charge ◽  
Transfer Resistance ◽  
Storage Devices ◽  
Electrochemical Storage

Frequency-dependent capacitance C(ω) is a rapid and reliable method for the determination of the state-of-charge (SoC) of electrochemical storage devices. The state-of-the-art of SoC monitoring using impedance spectroscopy is reviewed, and complemented by original 1.5-year long-term electrical impedance measurements of several commercially available supercapacitors. It is found that the kinetics of the self-discharge of supercapacitors comprises at least two characteristic time constants in the range of days and months. The curvature of the Nyquist curve at frequencies above 10 Hz (charge transfer resistance) depends on the available electric charge as well, but it is of little use for applications. Lithium-ion batteries demonstrate a linear correlation between voltage and capacitance as long as overcharge and deep discharge are avoided.

scholarly journals Implementation of Coulomb Counting Method for Estimating the State of Charge of Lithium-Ion Battery

2021 ◽  
pp. 1-8
Author(s):  
Kevin C. Ndeche ◽  
Stella O. Ezeonu
Keyword(s):  
Lithium Ion Battery ◽  
Coulombic Efficiency ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Counting Method ◽  
Voltage Measurement ◽  
Initial State ◽  
Battery Capacity

An accurate estimate of the state of charge, which describes the remaining percentage of a battery’s capacity, has been an important and ever existing problem since the invention of the electrochemical cell. State of charge estimation is one of the important function of a battery management system which ensures the safe, efficient and reliable operation of a battery. In this paper, the coulomb counting method is implemented for the estimation of the state of charge of lithium-ion battery. The hardware comprises an Arduino based platform for control and data processing, and a 16-bit analog to digital converter for current and voltage measurement. The embedded algorithm initializes with a self-calibration phase, during which the battery capacity, coulombic efficiency and initial state of charge are evaluated. The initial state of charge is determined at the fully charged state (100% state of charge) or the fully discharged state (0% state of charge). The cumulative error of this method was addressed by routine recalibration of the capacity, coulombic efficiency and state of charge at the fully-charged and fully-discharged states. The algorithm was validated by charging/discharging a lithium-ion battery through fifty complete cycles and evaluating the error in the estimated state of charge. The result shows a mean absolute error of 0.35% in the estimated state of charge during the test. Further analysis, considering prolonged battery operation without parameters recalibration, suggests that error in the coulombic efficiency term contributes the most to the increasing error in the estimated state of charge with each cycle.

scholarly journals Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

2022 ◽  
Vol 21 ◽  
pp. 1-19
Author(s):  
Wang Jianhong ◽  
Ricardo A. Ramirez-Mendoza
Keyword(s):  
State Estimation ◽  
State Space ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
External Noise ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Set Membership ◽  
State Of Charge Estimation

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

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