Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin, Deng; Lin, Bin

doi:10.20517/energymater.2021.10

Cited by 17 publications

(8 citation statements)

References 62 publications

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“…The focus factor α linearly increases with the maximum instantaneous capacitance c max . Substituting Equation ( 6) into (12), we can cancel out voltage amplification α and the scaling factor K is written in terms of the maximum instantaneous capacitance c max and the peak capacitance voltage V P as follows.…”

Section: 𝐾 𝑙𝑛mentioning

confidence: 99%

“…[1], a dual-scale cell SOC estimation complex fitting function was proposed for series-connected battery pack. For optimizing the complex parameters, the machine learning [12] and artificial intelligence [13] method can be used to help training data in battery models. Due to the complexity of the parameters obtained from abovementioned literatures, the shifted sigmoid SOC-VOC model was proposed and derived for estimating SOC in this work.…”

Section: Introductionmentioning

confidence: 99%

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Lithium Battery Model and Its Application to Parallel Charging

Shieh

Liu

et al. 2022

Energies

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A new SOC (State-Of-Charge)–VOC (Voltage-of-Open-Circuit) mathematical model was proposed in this paper, which is particularly useful in parallel lithium battery modeling. When the battery strings are charged in parallel connection, the batteries can be deemed as capacitors with different capacitances, and the one with larger capacitance always obtains the higher current. According to this mathematical model, the parallel battery charging with different peak capacitances can result in different voltage slew rates on different battery strings during the constant current control. Different parallel battery strings are charged with different currents, of which the battery string under higher current can induce higher power loss and higher temperature. The conventional solution can use this model to switch the constant current charging into the constant voltage charging with the correct timing to avoid overcurrent charging. Other battery pack protection methods including current sense resistor, resettable thermal cutoff device, or resettable fuse can also use this mathematical model to improve the protection. In the experiments, three kinds of batteries including LiFePO4 battery, EV Type-1 battery, and ternary battery were examined. The experiments showed good consistency with the simulation results derived from the mathematical model.

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Section: 𝐾 𝑙𝑛mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lithium Battery Model and Its Application to Parallel Charging

Shieh

Liu

et al. 2022

Energies

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“…In Ref. [14], a novel strategy for algorithmic machine learning was suggested to dynamically identify the structure-composition-property connections of the perovskite oxide materials of fuel cells.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

et al. 2022

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The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

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“…Moreover, the synthesis conditions also play an important role in the amount of defects (Fe(CN) 6 vacancies and interstitial and coordinated water) in the as-prepared MnHCF. In view of the close structure–composition–property relationships of the materials, it is necessary to conduct a systematic study to give a comprehensive understanding about the influence of particle size and structural defects on the electrochemical performance and kinetics of the as-prepared MnHCF …”

Section: Introductionmentioning

confidence: 99%

“…In view of the close structure−composition−property relationships of the materials, it is necessary to conduct a systematic study to give a comprehensive understanding about the influence of particle size and structural defects on the electrochemical performance and kinetics of the as-prepared MnHCF. 32 Herein, three groups of high-quality K-containing MnHCF (KMHCF1, KMHCF2, KMHCF3) are prepared using the SC assisted coprecipitation method by adjusting the reactant concentrations (e.g., 5, 10, and 20 mM for Mn 2+ solution, respectively) with a constant concentration of SC. Furthermore, three different aging times (12,24, and 48 h) are performed for each group of KMHCF samples, respectively.…”

Section: Introductionmentioning

confidence: 99%

Reactant Concentration and Aging-Time-Regulated Potassium Manganese Hexacyanoferrate as a Superior Cathode for Sodium-Ion Batteries

Liu

Lam

Hou

2021

ACS Appl. Energy Mater.

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In this study, the sodium citrate assisted coprecipitation method was adopted to prepare high-quality potassium manganese hexacyanoferrate (KMHCF) by adjusting the reactant concentration and aging time. The influences of reactant concentration and aging time on the microstructures and electrochemical performance were investigated systematically. With the variations in preparation conditions, the as-prepared samples showed similar monoclinic crystal structure but different morphologies as well as electrochemical behaviors. The results proved that the lower reactant concentration is more beneficial to the formation of large particles with fewer defects of Fe(CN)6 vacancies and interstitial and coordinated water in the crystal structure. As a result, better cycling stability was obtained for the KMHCF1 samples prepared with a lower reactant concentration when compared to the KMHCF3 samples prepared with a higher reactant concentration. However, the small particles (KMHCF3) benefited the electrochemical kinetics, resulting in a larger specific capacity when compared with the large particles (KMHCF1). Similarly, the aging time also exhibited significant impacts on the microstructure and electrochemical performance of the as-prepared samples. The 24 h was confirmed to be the optimal aging time for the KMHCF samples. Among all samples, the KMHCF2_24h exhibited the best electrochemical performance with a large initial specific capacity of 140.3 mA h g–1 and a high capacity retention of 80.61% after 100 cycles at 20 mA g–1. A very high capacity retention of 85.02% after 500 cycles was achieved even at a high current density of 200 mA g–1. This should be ascribed to the regular cubic morphology of particles with the optimal size (1 μm) and less defects resulted from the optimal reactant concentration and aging time. This study provides a valuable and practical guidance for the synthesis of high-performance Prussian blue analogue cathode materials for sodium-ion batteries.

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Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Abstract: How to cite this article: Deng Q, Lin B. Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage.

Cited by 17 publications

References 62 publications

Lithium Battery Model and Its Application to Parallel Charging

Lithium Battery Model and Its Application to Parallel Charging

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

Reactant Concentration and Aging-Time-Regulated Potassium Manganese Hexacyanoferrate as a Superior Cathode for Sodium-Ion Batteries

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