Battery degradation remains a pivotal concern in the energy storage domain,
with machine learning emerging as a potent tool to drive forward insights and
solutions. However, this intersection of electrochemical science and machine
learning poses complex challenges. Machine learning experts often grapple with
the intricacies of battery science, while battery researchers face hurdles in
adapting intricate models tailored to specific datasets. Beyond this, a
cohesive standard for battery degradation modeling, inclusive of data formats
and evaluative benchmarks, is conspicuously absent. Recognizing these
impediments, we present BatteryML - a one-step, all-encompass, and open-source
platform designed to unify data preprocessing, feature extraction, and the
implementation of both traditional and state-of-the-art models. This
streamlined approach promises to enhance the practicality and efficiency of
research applications. BatteryML seeks to fill this void, fostering an
environment where experts from diverse specializations can collaboratively
contribute, thus elevating the collective understanding and advancement of
battery research.The code for our project is publicly available on GitHub at
this https URL

BatteryML 是一个一站式、全面、开源的平台，旨在统一数据预处理、特征提取和传统与先进模型的实施，以增强研究应用的实用性和效率。

BatteryML：一个用于电池衰减机器学习的开源平台

BatteryML:An Open-source platform for Machine Learning on Battery  Degradation

Neural networks have been widely applied in the power system area. They can
be used for better predicting input information and modeling system performance
with increased accuracy. In some applications such as battery degradation
neural network-based microgrid day-ahead energy scheduling, the input features
of the trained learning model are variables to be solved in optimization models
that enforce limits on the output of the same learning model. This will create
a neural network-embedded optimization problem; the use of nonlinear activation
functions in the neural network will make such problems extremely hard to solve
if not unsolvable. To address this emerging challenge, this paper investigated
different methods for linearizing the nonlinear activation functions with a
particular focus on the widely used rectified linear unit (ReLU) function. Four
linearization methods tailored for the ReLU activation function are developed,
analyzed and compared in this paper. Each method employs a set of linear
constraints to replace the ReLU function, effectively linearizing the
optimization problem, which can overcome the computational challenges
associated with the nonlinearity of the neural network model. These proposed
linearization methods provide valuable tools for effectively solving
optimization problems that integrate neural network models with ReLU activation
functions.

本文研究了针对常用的修正线性单元（ReLU）激活函数的线性化方法，为将神经网络模型与 ReLU 激活函数结合的优化问题提供了有效解决工具。