Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network
Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network
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DOI：
10.1016/j.apenergy.2021.116541
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发表时间：
2021-01-28
期刊：
影响因子：
11.2
通讯作者：
Qin, Shiyin
中科院分区：
文献类型：
--
作者：
Haris, Muhammad;Hasan, Muhammad Noman;Qin, Shiyin
超级电容器的使用寿命、功率密度和瞬态响应使其成为电动汽车和可再生能源行业的首选组件。超级电容器的长生命周期通常使设计人员很难评估系统在整个产品周期内的可靠性。在现有文献中，剩余使用寿命 (RUL) 估计利用高达 50% 的健康状态 (SOH) 退化数据，以合理的精度成功预测超级电容器的 RUL，这使得它们在收集数据所需的时间和资源方面不切实际。获取数据的时间限制了开发数据驱动的超级电容器 RUL 预测模型。本研究的目的是利用少于 10% 的退化数据可靠地预测超级电容器的 SOH 退化曲线，以避免耗时和成本高昂的生命周期测试。这项研究提出了一种深度学习算法——深度置信网络（DBN）与贝叶斯优化和 HyperBand（BOHB）的新颖组合，以预测超级电容器在退化早期阶段的 RUL。所提出的方法使用最初15000个循环的数据（在大多数情况下少于6％的训练数据）成功地预测了退化曲线，这是非常有前途的，因为超级电容器在这个阶段尚未表现出很大的退化，从而减少了高达54％的RUL预测模型开发时间。所提出的模型显示出良好的准确性，百分比误差和均方根误差 (RMSE) 范围分别为 0.05% 至 2.2% 和 0.8851 至 1.6326。模型的稳健性还通过在训练期间向训练数据中注入噪声来测试。
The lifespan, power density, and transient response make supercapacitors a component of choice for the electric vehicle and renewable energy industry. Supercapacitors' long lifecycle often makes it difficult for designers to assess the system's reliability over the complete product cycle. In the existing literature, the remaining useful life (RUL) estimations utilize up to 50% state of health (SOH) degradation data to successfully predict the RUL of the supercapacitors with reasonable accuracy, making them impractical in terms of time and resources required to collect the data. The time to acquire data imposes restrictions on developing a data-driven RUL prediction model for the supercapacitors. The objective of this study is to reliably predict the SOH degradation curve of the supercapacitors with the availability of less than 10% degradation data to avoid time and cost consuming lifecycle testing. This study presents a novel combination of deep learning algorithm-Deep Belief Network (DBN) with Bayesian Optimization and HyperBand (BOHB) to predict the RUL of the supercapacitors in the early phases of degradation. The proposed method successfully predicts the degradation curve using the data of the initial 15 thousand cycles (less than 6% data for training in most of the cases), which is very promising since the supercapacitor has yet to show much degradation at this stage, thus reducing up to 54% time for the development of the RUL prediction model. The proposed model shows good accuracy with percent error and root mean squared error (RMSE) ranging from 0.05% to 2.2% and 0.8851 to 1.6326, respectively. The robustness of the model is also tested by injecting noise in the training data during training.