Department of Industrial and Systems Engineering, San Jose State University, San Jose, CA 95192, USA.
Sensors (Basel). 2023 Feb 26;23(5):2587. doi: 10.3390/s23052587.
State-of-health (SOH) is a measure of a battery's capacity in comparison to its rated capacity. Despite numerous data-driven algorithms being developed to estimate battery SOH, they are often ineffective in handling time series data, as they are unable to utilize the most significant portion of a time series while predicting SOH. Furthermore, current data-driven algorithms are often unable to learn a health index, which is a measurement of the battery's health condition, to capture capacity degradation and regeneration. To address these issues, we first present an optimization model to obtain a health index of a battery, which accurately captures the battery's degradation trajectory and improves SOH prediction accuracy. Additionally, we introduce an attention-based deep learning algorithm, where an attention matrix, referring to the significance level of a time series, is developed to enable the predictive model to use the most significant portion of a time series for SOH prediction. Our numerical results demonstrate that the presented algorithm provides an effective health index and can precisely predict the SOH of a battery.
健康状态（SOH）是电池容量与其额定容量相比的一种衡量标准。尽管已经开发出许多基于数据的算法来估计电池的 SOH，但它们在处理时间序列数据时往往效果不佳，因为它们无法在预测 SOH 时利用时间序列的最重要部分。此外，当前基于数据的算法通常无法学习健康指数，健康指数是衡量电池健康状况的一种度量，用于捕捉容量衰减和再生。为了解决这些问题，我们首先提出了一种优化模型来获取电池的健康指数，该指数可以准确捕捉电池的退化轨迹并提高 SOH 预测精度。此外，我们引入了一种基于注意力的深度学习算法，其中开发了一个注意力矩阵，指的是时间序列的重要性级别，以使预测模型能够使用时间序列的最重要部分进行 SOH 预测。我们的数值结果表明，所提出的算法提供了有效的健康指数，并可以精确地预测电池的 SOH。
