锂离子和钠离子全电池开发的精确电压预测

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摘要

电池平衡、负极与正极(N:P)比率和电压限制决定了不同速率下的第一周期损耗和可逆容量,并可能影响降解机制和循环寿命。这种平衡需要针对每种电池化学成分、电极质量负载和电池形式进行优化,通常通过经验优化来实现。这项工作提供了一种精确的预测工具,在相同的工作条件下,通过解耦独立电极电位来计算全电池电压。根据低速率半电池电压曲线(伪开路电压)准确预测了 NMC622//石墨全电池电压,并针对不同的 N:P 比率、速率、材料类型和电池形式进行了验证。还演示了该方法在钠离子电池化学、高功率(NMC622//MoNbO)和高能量(NMC920305//Graphite-SiO)等几种化学中的应用。此外,还从观测到的不同速率下负极和正极的电压和过电位中提取了每个电极的关键热力学和动力学参数。阐明了限制速率的电极,并提供了进一步的细胞平衡信息,以实现高功率、高能量和高寿命。提取的参数可用于多尺度模型,进一步优化电池设计和性能限制。这种方法有望为不同化学成分和形式的电池优化提供更快的新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Accurate voltage prediction for lithium and sodium-ion full-cell development

Accurate voltage prediction for lithium and sodium-ion full-cell development

The cell balance, negative to positive (N:P) electrode ratio, and voltage limits determine the first cycle loss and reversible capacity at different rates and can influence degradation mechanisms and cycle life. This balance needs optimizing for each cell chemistry, electrode mass loading, and cell format, typically performed through empirical optimization. This work provides an accurate predictive tool for calculating full-cell voltages by decoupling the independent electrode potential under the same operating conditions. Full-cell NMC622//Graphite voltages are accurately predicted from low-rate half-cell voltage profiles (pseudo-open circuit voltages) and validated for different N:P ratios, rates, material types, and cell formats. The application of this methodology to several chemistries, including sodium-ion cell chemistry, high power (NMC622//MoNb12O33), and high energy (NMC920305//Graphite-SiOx), is also demonstrated. In addition, each electrode's key thermodynamic and kinetic parameters are extracted from the observed voltage and overpotentials for the negative and positive electrodes at different rates. Elucidating the rate-limiting electrodes and providing further cell balancing information to achieve high power, energy, and lifetime. The extracted parameters can be used in multi-scale models to optimise cell design and performance limitations further. This method promises new and quicker routes for cell optimization for different chemistries and formats.

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