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Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

机译：带有增量学习的优化的关联向量机算法，可估算锂离子电池的剩余使用寿命

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摘要

Lithium-ion battery plays a key role in most industrial systems, which is critical to the system availability. It is important to evaluate the performance degradation and estimate the remaining useful life (RUL) for those batteries. With the capability of uncertainty representation and management, Relevance Vector Machine (RVM) becomes an effective approach for lithium-ion battery RUL estimation. However, small sample size and low precision of multi-step prediction limits its application in battery RUL estimation for sparse RVM algorithm. Due to the continuous on-line update of monitoring data, to improve the prediction performance of battery RUL model, dynamic training and on-line learning capability is desirable. Another challenge in on-line and real-time processing is the operating efficiency and computational complexity. To address these issues, this paper implements a flexible and effective on-line training strategy in RVM algorithm to enhance the prediction ability, and presents an incremental optimized RVM algorithm to the model via efficient on-line training. The proposed on-line training strategy achieves a better prediction precision as well as improves the operating efficiency for battery RUL estimation. Experiments based on NASA battery data set show that the proposed method yields a satisfied performance in RUL estimation of lithium-ion battery. (C) 2014 Elsevier Ltd. All rights reserved.
机译：锂离子电池在大多数工业系统中起着关键作用，这对系统可用性至关重要。评估性能下降并估算这些电池的剩余使用寿命（RUL）非常重要。相关性向量机（RVM）具有不确定性表示和管理的能力，成为锂离子电池RUL估计的有效方法。然而，样本量少，多步预测精度低限制了其在稀疏RVM算法的电池RUL估计中的应用。由于监视数据的连续在线更新，为了提高电池RUL模型的预测性能，需要动态训练和在线学习能力。在线和实时处理的另一个挑战是操作效率和计算复杂性。为了解决这些问题，本文在RVM算法中实现了一种灵活有效的在线训练策略，以增强预测能力，并通过有效的在线训练向模型提出了一种增量式优化RVM算法。所提出的在线训练策略实现了更好的预测精度，并提高了电池RUL估计的工作效率。基于NASA电池数据集的实验表明，该方法在锂离子电池的RUL估计中具有令人满意的性能。 （C）2014 Elsevier Ltd.保留所有权利。

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