Accurate battery lifetime prediction is important for preventative
maintenance, warranties, and improved cell design and manufacturing. However,
manufacturing variability and usage-dependent degradation make life prediction
challenging. Here, we investigate new features derived from capacity-voltage
data in early life to predict the lifetime of cells cycled under widely varying
charge rates, discharge rates, and depths of discharge. Features were extracted
from regularly scheduled reference performance tests (i.e., low rate full
cycles) during cycling. The early-life features capture a cell's state of
health and the rate of change of component-level degradation modes, some of
which correlate strongly with cell lifetime. Using a newly generated dataset
from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range
of conditions, we demonstrate a lifetime prediction of in-distribution cells
with 15.1% mean absolute percentage error using no more than the first 15% of
data, for most cells. Further testing using a hierarchical Bayesian regression
model shows improved performance on extrapolation, achieving 21.8% mean
absolute percentage error for out-of-distribution cells. Our approach
highlights the importance of using domain knowledge of lithium-ion battery
degradation modes to inform feature engineering. Further, we provide the
community with a new publicly available battery aging dataset with cells cycled
beyond 80% of their rated capacity.

提出一种基于先期容量电压特征数据的电池寿命预测方法，在广泛不同充 / 放电速率和深度下，使用不超过 15% 的数据即可实现 15.1% 的平均绝对百分比误差，通过建立贝叶斯回归模型还可实现对超额样本的预测，文章同时提供了一个电池老化过程公共数据集，在电池的预防性维护、保修和电池设计制造方面具有重要意义。

基于早期老化数据预测不同使用条件下电池寿命

Predicting Battery Lifetime Under Varying Usage Conditions from Early  Aging Data

Most of previous image denoising methods focus on additive white Gaussian
noise (AWGN). However,the real-world noisy image denoising problem with the
advancing of the computer vision techiniques. In order to promote the study on
this problem while implementing the concurrent real-world image denoising
datasets, we construct a new benchmark dataset which contains comprehensive
real-world noisy images of different natural scenes. These images are captured
by different cameras under different camera settings. We evaluate the different
denoising methods on our new dataset as well as previous datasets. Extensive
experimental results demonstrate that the recently proposed methods designed
specifically for realistic noise removal based on sparse or low rank theories
achieve better denoising performance and are more robust than other competing
methods, and the newly proposed dataset is more challenging. The constructed
dataset of real photographs is publicly available at
https://github.com/csjunxu/PolyUDataset for researchers to investigate
new real-world image denoising methods. We will add more analysis on the noise
statistics in the real photographs of our new dataset in the next version of
this article.

通过构建一个包含具有综合性的不同自然场景下的真实噪声图像的新基准数据集，并在其上评估了各种图像去噪方法（包括之前的数据集），证明近期针对稀疏或低秩理论设计的方法实现了更好的去噪效果，更为稳健，并且新提出的数据集更具挑战性。该数据集现已公开，供研究人员研究新的现实世界图像去噪方法。