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1. Sensorless parameter estimation of vanadium redox flow batteries in charging mode considering capacity fading

   Khaki, Bahman ; Das, Pritam ( November 2921 , Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   ABSTRACT State of Charge (SoC) and discharge capacity of the batteries are parameters that cannot be determined directly from the battery monitoring and control system and requires estimation. Current and voltage sensors have inherent error and delay leading to inaccurate measurements leading to inaccurate SoC and discharge capacity estimations. These sensors also have an additional cost to the battery system. This paper proposes a sensorless approach to estimate parameters of Vanadium Redox Flow Batteries (VRFBs) for both CC and CV charging methods by estimating battery current in CV mode and terminal voltage in CC mode. The results of estimations by the sensorless approach show a maximum relative error of 0.0035 in estimating terminal voltage in CC charging and a maximum relative error of 0.045 in estimating charging current in CV mode. Furthermore, long- term operation of vanadium redox flow batteries causes ion diffusions across the membrane and the depletion of active materials, which leads to capacity fading in VRFBs and inaccurate SoC estimation. To address the inaccuracy of SoC estimation in the long-term use of the battery, the capacity fading model is also considered for VRFBs in this paper. Experimental results show a 19% electrolyte volume change in the positive and negative tanks after 200 cycles of charge/discharge due to the bulk electrolyte transfer between the positive and negative sides of the battery system. This change of electrolyte volume results in 13.73% capacity fading after 200 cycles of charging/discharging. The SoC also changes by 7.1% after 200 cycles, due to the capacity and electrolyte volume loss, which shows the necessity of considering capacity fading in long-term use of the battery. 

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2. An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods

   Khaki, Bahman ; Das, Pritam ( January 2021 , Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   A B S T R A C T This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model’s discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%. 

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3. Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

   https://doi.org/10.1115/DETC2021-71407  

   Thelen, Adam ; Lui, Yu Hui ; Shen, Sheng ; Laflamme, Simon ; Hu, Shan ; Hu, Chao ( November 2021 , 47th Design Automation Conference (DAC))

   State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data. 

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4. Investigation of Iron(III)Tetraphenylporphyrin as a Redox Flow Battery Anolyte: UnexpectedSide Reactivity with the Electrolyte

   Mitchell, Nathan H. ; Elgrishi, Noémie ( July 2023 , Journal of physical chemistry C)

   Redox flow batteries (RFBs) present an opportunity to bridge the gap between the intermittent availability of green energy sources and the need for on-demand grid level energy storage. While aqueous vanadium-based redox flow batteries have been commercialized, they are limited by the constraints of using water as an electrochemical solvent. Nonaqueous redox flow battery systems can be used to produce high voltage batteries due to the larger electrochemical window in nonaqueous solvents and the ability to tune the redox properties of active materials through functionalization. Iron porphyrins, a class of organometallic macrocycles, have been the subject of many studies for their photocatalytic and electrocatalytic properties in nonaqueous solvents. Often, iron porphyrins can undergo multiple redox events making them interesting candidates for use as anolytes in asymmetrical redox flow batteries or as both catholyte and anolyte in symmetrical redox flow battery systems. Here the electrochemical properties of Fe(III)TPP species relevant to redox flow battery electrolytes are investigated including solubility, electrochemical properties, and charge/discharge cycling. Commonly used support electrolyte salts can have reactivities that are often overlooked beyond their conductivity properties in nonaqueous solvents. Parasitic reactions with the cations of common support electrolytes are highlighted herein, which underscore the careful balance required to fully assess the potential of novel RFB electrolytes. 

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5. State of Health Estimation of Electric Vehicle Batteries Using Transformer-based Neural Network

   Zhao, Yixin ; Behdad, Sara ( August 2023 , Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2023)

   Electric vehicles (EVs) are considered an environmentally friendly option to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and aid in the prevention of dangerous occurrences. Data-driven models with advantages in time series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The Transformer model is capable of capturing long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard Transformer and an encoder-only Transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction. 

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