scholarly journals State-of-Charge Monitoring and Battery Diagnosis of Different Lithium Ion Chemistries Using Impedance Spectroscopy

Batteries ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 17
Author(s):  
Peter Kurzweil ◽  
Wolfgang Scheuerpflug
Keyword(s):  
Impedance Spectroscopy ◽  
Lithium Iron Phosphate ◽  
Linear Trend ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Discharge Voltage ◽  
State Of Charge ◽  
Constant Current ◽  
Impedance Method ◽  
Battery Capacity

For lithium iron phosphate batteries (LFP) in aerospace applications, impedance spectroscopy is applicable in the flat region of the voltage-charge curve. The frequency-dependent pseudocapacitance at 0.15 Hz is presented as useful state-of-charge (SOC) and state-of-health (SOH) indicator. For the same battery type, the prediction error of pseudocapacitance is better than 1% for a quadratic calibration curve, and less than 36% for a linear model. An approximately linear correlation between pseudocapacitance and Ah battery capacity is observed as long as overcharge and deep discharge are avoided. We verify the impedance method in comparison to the classical constant-current discharge measurements. In the case of five examined lithium-ion chemistries, the linear trend of impedance and SOC is lost if the slope of the discharge voltage curve versus SOC changes. With nickel manganese cobalt (NMC), high impedance modulus correlates with high SOC above 70%.

Application and Performance Analysis of Lithium Iron Phosphate Battery in Photoelectric Complementary System

2019 ◽  
Vol 14 (12) ◽  
pp. 1709-1716 ◽  
Author(s):  
Weiping Liu ◽  
Ximing Zhang ◽  
Zhaofeng Wang ◽  
Ruijian Wang ◽  
Chen Chen
Keyword(s):  
Power Supply ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Internal Resistance ◽  
Green Energy ◽  
Supply System ◽  
Constant Current ◽  
Battery Capacity ◽  
Remaining Capacity

In order to make the device better capable of power supply, it is necessary to select a corresponding battery, and the lithium iron phosphate battery is favored for its excellent performance. In this study, the requirements of the lithium iron phosphate battery for optoelectronic complementary power supply system were analyzed at first, then the application of the battery in the supply system was further analyzed. Next, the structure and working principle of the battery were deeply studied, and a large number of experiments were designed to analyze the performance of the battery. It has been confirmed by a large number of experiments that the charging process of lithium iron phosphate battery is slow; the remaining capacity of lithium iron phosphate battery has a correlation with the voltage it carries. During the charging process, the internal resistance of the lithium iron phosphate battery will rapidly drop to a stable value, and the conversion of constant current charging to constant voltage charging will further reduce the internal resistance. Adjusting the battery temperature can adjust the internal capacity of the battery, that is, increasing the temperature can promote the expansion of the battery capacity to a certain extent, and lowering the temperature can impair the overall performance of the battery. In the battery AC (alternating current) impedance spectrum test, the impedance of the battery was affected by factors such as inductance, lithium ion diffusion, and internal polarization of the battery. Through the above research, the power consumption of lithium iron phosphate battery can be better understood to make better use of solar energy and provide people with stable green energy.

scholarly journals Advances in understanding Li battery mechanisms using impedance spectroscopy - Review

2020 ◽  
Vol 10 (2) ◽  
pp. 79-93
Author(s):  
Jože MoÅ¡kon ◽  
Sara Drvarič Talian ◽  
Robert Dominko ◽  
Miran Gaberšček
Keyword(s):  
Impedance Spectroscopy ◽  
Transmission Line ◽  
Lithium Iron Phosphate ◽  
Quantitative Description ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Diffusion Reaction ◽  
Metallic Lithium ◽  
Li Battery ◽  
Measured Impedance

The use of impedance spectroscopy in the field of modern batteries is demonstrated on three systems: lithium ion batteries represented by lithium iron phosphate (LFP) and lithium cobalt oxide (LCO) electrodes, a porous carbon cathode in contact with polysulfides and a metallic lithium anode exhibiting dendritic growth. In all cases, systematic experiments are shown where the type and composition of electrochemical cell is varied in order to identify the main processes contributing to the impedance response. The experiments are upgraded with appropriate models, using mainly the transmission line approach. The approach allows establishment of clear correlations between the composition and morphology of electrodes on one hand and the measured impedance features on the other. As the transmission line models are based on the use of physically well-defined elements, the approach allows a quantitative description of the main processes (diffusion, reaction, migration across films and contacts) taking place in modern battery electrodes.

scholarly journals State of Charge Estimation of Lithium-ion Battery Based on Improved Recurrent Neural Network

2021 ◽  
Vol 2109 (1) ◽  
pp. 012005
Author(s):  
Zhenya Wang ◽  
Xiaodong Li ◽  
Yahui Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Maximum Error ◽  
State Of Charge ◽  
Equivalent Model ◽  
Estimation Model ◽  
Soc Estimation

Abstract Aiming at the problem of gradient disappearance and gradient explosion in the recurrent neural network (RNN) algorithm in the battery estimation model, this paper proposes a state of charge (SOC) estimation model developed using an independent recurrent neural network (IndRNN). Firstly, the Thevenin equivalent model of the battery is established, and the parameters of the equivalent model are identified through experiments. Then, the Relu activation function is introduced into the RNN to separate the neurons in each layer. Finally, the model was trained on various experimental data sets collected from the lithium iron phosphate battery experimental platform under different discharge conditions. Without any prior knowledge about the battery interior, the proposed battery model successfully characterizes the non-linear behavior of the battery. The results show that under different discharge conditions, IndRNN is better than RNN in terms of maximum error, the mean error, and the root mean square error (RMSE), which greatly improves the accuracy of SOC estimation.

Graphenised Lithium Iron Phosphate and Lithium Manganese Silicate Hybrid Cathodes: Potentials for Application in Lithium‐ion Batteries

2020 ◽  
Vol 32 (12) ◽  
pp. 2982-2999
Author(s):  
Zolani Myalo ◽  
Chinwe Oluchi Ikpo ◽  
Assumpta Chinwe Nwanya ◽  
Miranda Mengwi Ndipingwi ◽  
Samantha Fiona Duoman ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Manganese Silicate ◽  
Lithium Manganese

scholarly journals A Novel Pyrometallurgical Recycling Process for Lithium-Ion Batteries and Its Application to the Recycling of LCO and LFP

Metals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Alexandra Holzer ◽  
Stefan Windisch-Kern ◽  
Christoph Ponak ◽  
Harald Raupenstrauch
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Iron Phosphate ◽  
Process Development ◽  
Removal Rate ◽  
Continuous Process ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Recycling Process ◽  
Subsequent Step ◽  
Pre Treatment

The bottleneck of recycling chains for spent lithium-ion batteries (LIBs) is the recovery of valuable metals from the black matter that remains after dismantling and deactivation in pre‑treatment processes, which has to be treated in a subsequent step with pyrometallurgical and/or hydrometallurgical methods. In the course of this paper, investigations in a heating microscope were conducted to determine the high-temperature behavior of the cathode materials lithium cobalt oxide (LCO—chem., LiCoO2) and lithium iron phosphate (LFP—chem., LiFePO4) from LIB with carbon addition. For the purpose of continuous process development of a novel pyrometallurgical recycling process and adaptation of this to the requirements of the LIB material, two different reactor designs were examined. When treating LCO in an Al2O3 crucible, lithium could be removed at a rate of 76% via the gas stream, which is directly and purely available for further processing. In contrast, a removal rate of lithium of up to 97% was achieved in an MgO crucible. In addition, the basic capability of the concept for the treatment of LFP was investigated whereby a phosphorus removal rate of 64% with a simultaneous lithium removal rate of 68% was observed.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Modification of liquid-phase synthesis of lithium-iron phosphate, a cathode material for lithium-ion battery

2012 ◽  
Vol 85 (6) ◽  
pp. 879-882 ◽  
Author(s):  
E. N. Kudryavtsev ◽  
R. V. Sibiryakov ◽  
D. V. Agafonov ◽  
V. N. Naraev ◽  
A. V. Bobyl’
Keyword(s):  
Liquid Phase ◽  
Cathode Material ◽  
Lithium Ion Battery ◽  
Lithium Iron Phosphate ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Phase Synthesis

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

2017 ◽  
Author(s):  
Robert R. Richardson ◽  
Christoph R. Birkl ◽  
Michael A. Osborne ◽  
David A. Howey
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Diagnostic Technique ◽  
Lithium Ion ◽  
Constant Current ◽  
Time Data ◽  
Capacity Estimation ◽  
Voltage Range ◽  
Battery Capacity

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

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