COMPARATIVE STUDY OF MACHINE LEARNING METHODS FOR STATE OF HEALTH ESTIMATION OF MARITIME BATTERY SYSTEMS

C. Grindheim, Morten Stakkeland, Ingrid Glad, Erik Vanem
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Abstract

This paper tests two data-driven approaches for predicting the State of Health (SoH) of Lithium-ion-batteries (LIBs) for the purpose of monitoring maritime battery systems. First, nonsequential approaches are investigated and various models are tested: Ridge, Lasso, Support vector regression, and Gradient boosted trees. Binning is proposed for feature engineering for these types of models to capture the temporal structure in the data. Such binning creates histograms for the accumulated time the LIB has been within various voltage, temperature, and current ranges. Further binning to combine these histograms into 2D or 3D histograms is explored in order to capture relationships between voltage, temperature and current. Secondly, a sequential approach is explored where different deep learning architectures are tried out: long short-term memory (LSTM), Transformer, and Temporal convolutional network (TCN). Finally, the various models and the two approaches are compared in terms of their SoH prediction ability. Results indicate that the binning with ridge regression models performed best. The same publicly available sensor data from laboratory cycling tests are used for both approaches.
机器学习方法在海事电池系统健康状况评估中的比较研究
本文测试了两种数据驱动型方法,用于预测锂离子电池 (LIB) 的健康状况 (SoH),以达到监控海事电池系统的目的。首先,研究了非序列方法并测试了各种模型:Ridge、Lasso、支持向量回归和梯度提升树。为了捕捉数据中的时间结构,建议对这些类型的模型进行特征工程分选。这种分选可创建 LIB 在不同电压、温度和电流范围内累积时间的直方图。为了捕捉电压、温度和电流之间的关系,我们还探索了进一步的分选方法,将这些直方图组合成二维或三维直方图。其次,探索了一种序列方法,尝试了不同的深度学习架构:长短期记忆(LSTM)、变形器和时序卷积网络(TCN)。最后,比较了各种模型和两种方法的 SoH 预测能力。结果表明,采用脊回归的分选模型表现最佳。两种方法都使用了来自实验室循环测试的相同公开传感器数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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