Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea.
Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea.
Sensors (Basel). 2022 Dec 6;22(23):9522. doi: 10.3390/s22239522.
The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment's remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery's working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery's state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery's safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery's cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction.
锂离子电池的高效率和低成本问题带来了不稳定性和寿命变化。准确预测设备的剩余使用寿命对于基于需求的成功维护至关重要，可以提高可靠性并降低总维护成本。然而，评估电池的工作能力具有挑战性，特定的预测方法无法表示不确定性。科学评估和预测锂离子电池的健康状态（SOH），主要是其剩余使用寿命（RUL），对于确保电池在整个生命周期内的安全性和可靠性以及尽可能防止灾难性事故至关重要。已经开发出许多策略来确定锂离子电池的 RUL 和 SOH 预测，包括粒子滤波器（PF）。本文提出了一种基于 PF 的锂离子电池 RUL 估计新技术，将卡尔曼滤波器（KF）与 PF 相结合，以分析电池运行数据。PF 方法作为核心，极端梯度提升（XGBoost）用于观察 RUL 电池预测。由于具有强大的非线性拟合能力，XGBoost 用于映射检索特征与 RUL 之间的联系。生命周期测试旨在收集用于 RUL 预测的精确和可靠数据。RUL 预测结果表明，与其他方法相比，所提出策略的准确性有所提高。实验结果表明，当应用于锂离子电池的循环寿命数据集时，所提出的技术可以提高 RUL 预测的准确性。结果表明，该方法在实现更准确的剩余使用寿命预测方面具有优势。
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