基于事件驱动采集和机器学习的锂离子电池容量预测

S. Qaisar, A. AbdelGawad
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引用次数: 3

摘要

电池是现代电力系统的关键要素,它被习惯性地用于不同的重要应用,如电动汽车、无人机、航空电子设备和移动电话。在各种电池技术中,锂离子电池的应用最为广泛。这主要是因为它们结构紧凑,寿命长,功率大。另一方面,由于锂离子电池价格昂贵,因此使用电池管理系统(bms)来监控其使用情况,以优化其性能并确保其使用寿命更长。现代bms所需的大量处理资源可能导致更高的开销功耗。本研究的重点是通过重新设计其相关数据采集和处理链,以不同的方式升级现有的锂离子bms。旨在增强锂离子电池容量的数据采集和估计机制。它利用一种新的事件驱动机制来提取预期的锂离子电池参数。与固定速率的传统方法相比,事件驱动的方法带来了显著的压缩增益。挖掘出的属性被传递给鲁棒机器学习算法进行预测。采用5重交叉验证方法进行预测性能评价。得到的相关系数、最小平均绝对误差(MAE)和最小均方根误差(RMSE)分别为0.9996、0.0038和0.0054。它显示了将所提出的方法纳入当代bms的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Li-Ion Battery Capacity by Using Event-Driven Acquisition and Machine Learning
The battery is a crucial element of modern power systems and it is utilized habitually in different vital applications such as electric vehicles, drones, avionics and mobile phones. Among various batteries technologies the Li-Ion batteries are widely used. It is mainly because of their compactness, longer life and high power capacity. On the other hand, due to the disadvantage of Li-ion batteries being expensive, their use is monitored using battery management systems (BMSs) to optimize their performance and ensure they last longer. The extensive processing resources that modern BMSs need can result in higher overhead power consumption. This study focuses on upgrading the present Li-ion BMSs through redesigning their associative data acquisition and processing chains differently. It aims at enhancing the data acquisition and estimation mechanisms for the Li-ion batteries' capacities. It utilizes a novel event-driven mechanism for extracting the intended Li-Ion cell parameters. The event-driven approach brings notable compression gain compared to fix-rate conventional counterparts. The mined attributes are onward conveyed to the robust machine learning algorithms for prediction. The 5-fold cross-validation approach is used for prediction performance evaluation. The achieved correlation coefficient and minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are respectively 0.9996, 0.0038 and 0.0054 respectively. It shows the feasibility of incorporating the proposed approach in contemporary BMSs.
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