利用物理信息递归神经网络预测二次寿命应用的电池容量

IF 17 1区 工程技术 Q1 ENERGY & FUELS
Sina Navidi , Kristupas Bajarunas , Manuel Arias Chao , Chao Hu
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引用次数: 0

摘要

准确预测锂离子电池容量退化对于优化锂离子电池的二次寿命利用率、实现可靠运行、降低维护成本和延长寿命周期性能至关重要。然而,由于从第一次使用寿命到第二次使用寿命的过渡过程中,实际使用条件的显著变化以及细胞之间的显著差异,实现跨细胞和随时间的一致预测准确性仍然具有挑战性。在这项研究中,我们提出了一种新的物理信息机器学习方法,该方法将衰老感知电化学模型与递归神经网络相结合,创建了一个物理信息递归神经网络(PI-RNN)。这种混合模型利用基于物理的洞察力和数据驱动的学习来预测不同使用条件下的容量衰退,包括从第一次使用到第二次使用的过渡。我们使用两个数据集来评估PI-RNN:一个是由28个钴酸锂/石墨电池组成的开源NASA数据集,另一个是新收集的39个商用磷酸铁锂/石墨电池的数据集,其中电池在第一次使用寿命中最初循环到80%的容量,然后在第二次使用寿命中进行更温和的循环。虽然PI-RNN在第一生命期的表现与数据驱动模型相当,但它在第二生命期的预测中表现出明显的优势,当预测周期跨越从第一生命期到第二生命期的过渡时,与基线模型相比,它将均方根误差降低了大约40%-70%,即使只训练了两个细胞。参数化研究强调了结合物理建模的优势,不确定性量化确保了长期产能预测的可靠性。此外,我们进行了基准研究,以系统地评估所提出模型的优点和局限性,从而确定该方法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting battery capacity for second-life applications using physics-informed recurrent neural networks
Accurately forecasting lithium-ion battery capacity degradation is crucial for optimizing the second-life utilization of these batteries, enabling reliable operation, reduced maintenance costs, and extended life cycle performance. However, achieving consistent forecasting accuracy across cells and over time remains challenging due to significant cell-to-cell variability and substantial changes in real-world usage conditions during the transition from first to second life. In this study, we propose a new physics-informed machine learning method that integrates an aging-aware electrochemical model with a recurrent neural network, creating a physics-informed recurrent neural network (PI-RNN). This hybrid model leverages both physics-based insights and data-driven learning to predict capacity fade under diverse usage conditions, including transitions from first- to second-life applications. We evaluate PI-RNN using two datasets: an open-source NASA dataset comprising 28 lithium cobalt oxide/graphite cells, and a newly collected dataset of 39 commercial lithium iron phosphate/graphite cells, where cells were initially cycled to 80% capacity in their first life before undergoing milder cycling in their second life. While PI-RNN performs comparably to data-driven models in the first-life phase, it demonstrates a clear advantage in second-life forecasting, reducing root mean squared error by approximately 40%–70% compared to baseline models when forecasting periods span the transition from first to second life, even when trained on as few as two cells. Parametric studies highlight the advantages of incorporating physics-based modeling, and uncertainty quantification ensures the reliability of long-term capacity forecasting. In addition, we conducted benchmarking studies to systematically assess the advantages and limitations of the proposed model, thus identifying the scenarios where this approach excels.
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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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