Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries
Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries
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DOI：
10.1016/j.ensm.2022.05.047
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发表时间：
2022-05
影响因子：
20.4
通讯作者：
Adam Thelen;Y. Lui;Sheng Shen;S. Laflamme;Shan Hu;Hui Ye;Chao Hu
中科院分区：
文献类型：
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作者：
Adam Thelen;Y. Lui;Sheng Shen;S. Laflamme;Shan Hu;Hui Ye;Chao Hu
传统的锂离子（Li离子）电池健康状态（SOH）估计方法集中于估计当前电池容量，其不提供足够的信息来确定电池的生命周期阶段或在第二寿命使用中的价值。量化导致容量衰减的潜在退化模式可以进一步了解电池的电化学状态，并提供更详细的健康信息，例如剩余的活性材料和锂库存。然而，当前用于退化诊断的基于物理的方法需要长期循环数据，并且在设备上本地部署在计算上昂贵。为了改进当前的方法，我们提出并广泛测试了两种轻量级的物理信息机器学习方法，用于在线估计电池容量，并仅使用有限的早期实验退化数据诊断其主要退化模式。为了能够在不使用后期寿命实验数据的情况下进行后期寿命预测（例如，> 1.5年），使用来自基于物理学的半电池模型的模拟数据和从循环测试获得的早期寿命（例如，< 3个月）退化数据来训练每种方法。所提出的方法进行了全面评估，使用数据从长期（3.5年）循环实验的16个可拆卸级锂离子电池循环下两个温度和C率。四重交叉验证研究的结果表明，与纯粹的数据驱动方法相比，所提出的物理信息机器学习模型能够将电池容量的估计精度和三种主要退化模式的状态提高50%以上。此外，这项工作提供了深入了解温度和C-速率在细胞降解中的作用。
Traditional lithium-ion (Li-ion) battery state of health (SOH) estimation methodologies that focused on estimating present cell capacity do not provide sufficient information to determine the cell's lifecycle stage or value in second-life use. Quantifying the underlying degradation modes that cause capacity fade can give further insight into the electrochemical state of the cell and provide more detailed health information such as the remaining active materials and lithium inventory. However, current physics-based methods for degradation diagnostics require long-term cycling data and are computationally expensive to deploy locally on a device. To improve upon current methods, we propose and extensively test two light-weight physics-informed machine learning methods for online estimating the capacity of a battery cell and diagnosing its primary degradation modes using only limited early-life experimental degradation data. To enable late-life prediction (e.g. > 1.5 years) without the use of late-life experimental data, each of the methods is trained using simulation data from a physics-based half-cell model and early-life (e.g. < 3 months) degradation data obtained from cycling tests. The proposed methods are comprehensively evaluated using data from a long-term (3.5 years) cycling experiment of 16 implantable-grade Li-ion cells cycled under two temperatures and C-rates. Results from a four-fold cross-validation study show that the proposed physics-informed machine learning models are capable of improving the estimation accuracy of cell capacity and the state of three primary degradation modes by over 50% compared to a purely data-driven approach. Additionally, this work provides insights into the role of temperature and C-rate in cell degradation.