Gaussian process regression for forecasting battery state of health
Gaussian process regression for forecasting battery state of health
复制标题
DOI：
10.1016/j.jpowsour.2017.05.004
复制
发表时间：
2017-07-31
影响因子：
9.2
通讯作者：
Howey, David A.
中科院分区：
文献类型：
--
作者：
Richardson, Robert R.;Osborne, Michael A.;Howey, David A.
准确预测电池的未来容量和剩余使用寿命对于确保系统可靠运行以及最小化维护成本是必要的。电池退化的复杂特性意味着，到目前为止，容量衰减的机理建模仍然难以处理；然而，随着云连接设备的出现，来自各种应用中的电池的数据变得越来越容易获取，并且数据驱动的电池预测方法的可行性正在增加。在此，我们提出高斯过程（GP）回归用于预测电池的健康状态，并强调了高斯过程相对于其他数据驱动和机理方法的各种优势。高斯过程是一种贝叶斯非参数方法，因此能够对复杂系统进行建模，同时以有原则的方式处理不确定性。先验信息可以通过多种方式被高斯过程利用：如果潜在退化模型的函数形式已知，则可以使用显式均值函数，并且多输出高斯过程可以有效地利用来自不同电池的数据之间的相关性。我们通过对一些锂离子电池的容量与循环数据集进行短期和长期（剩余使用寿命）预测，展示了高斯过程的预测能力。（C）2017作者。由爱思唯尔出版公司出版。
Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells. (C) 2017 The Authors. Published by Elsevier B.V.