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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Novel Data-Efficient Mechanism-Agnostic Capacity Fade Model for Li-Ion Batteries
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Novel Data-Efficient Mechanism-Agnostic Capacity Fade Model for Li-Ion Batteries

机译：锂离子电池的新型数据有效机制 - 无社能容量褪色模型

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摘要

Accurate capacity fade prediction of Li-ion batteries is essential to reduce the time spent by manufacturers in performing quality assurance tests and to ensure the safety and durability of these batteries for end users. Various complicated aging mechanisms and the resulting capacity fade phenomena of Li-ion batteries make such predictions challenging; thus, mechanism-agnostic approaches using empirical and data-driven models are considered to be promising. This article proposes a mechanism-agnostic capacity fade empirical model called aging density function model (ADFM) for Li-ion batteries. Developed by innovating existing empirical models, the proposed ADFM predicts capacity fades for arbitrary battery input current trajectories, requires no additional experiments at the prediction phase, and reflects real batteries phenomena such as the varying amount of capacity fade for each cycle. As the proposed ADFM could generate a large amount of synthetic data, it was augmented with Bayesian neural networks (BNNs) to enhance its data efficiency. As a result, it can completely utilize the experimental data and achieve reasonable prediction accuracy regardless of the amount of experimental data. This BNN-augmented ADFM can also provide the reliability of the capacity fade prediction to ensure safety. Through charge/discharge cycle tests with an NCM/graphite Li-ion battery, the proposed BNN-augmented ADFM was shown to provide good performance in terms of the capacity fade prediction accuracy, with a mean absolute error of approximately 0.5% and maximum absolute error of approximately 2.5%.
机译：锂离子电池的精确容量淡化预测对于减少制造商在执行质量保证测试时花费的时间至关重要，并确保这些电池的最终用户的安全性和耐用性。各种复杂的老化机制和锂离子电池的所得容量衰落现象使得这种预测具有挑战性;因此，认为使用经验和数据驱动模型的机制可靠方法被认为是有前途的。本文提出了一种机制 - 无话量的能力淡入锂离子电池老化密度函数模型（ADFM）的机制 - 无话量褪色实证模型。通过创新现有的经验模型开发，提出的ADFM预测任意电池输入电流轨迹的容量衰落，不需要在预测阶段进行额外的实验，并反映每个循环的实际电池现象，例如变化的容量褪色。由于所提出的ADFM可以产生大量的合成数据，它被贝叶斯神经网络（BNN）增强了其数据效率。结果，无论实验数据的量如何，它都可以完全利用实验数据并实现合理的预测准确性。该BNN增强的ADFM还可以提供容量淡化预测的可靠性以确保安全性。通过充电/放电循环测试与NCM /石墨锂离子电池，所提出的BNN增强ADFM显示在容量淡出预测准确性方面提供良好的性能，其平均绝对误差约为0.5％和最大绝对误差大约2.5％。

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