Core Concepts
BatteryML is a comprehensive open-source platform designed to unify data preprocessing, feature extraction, and model implementation for enhancing battery research applications.
Abstract
ABSTRACT:
Battery degradation is a critical concern in energy storage.
Machine learning offers insights and solutions but faces challenges in battery science integration.
BatteryML aims to standardize battery degradation modeling with an open-source platform.
INTRODUCTION:
Lithium-ion batteries revolutionize energy storage but face capacity degradation issues.
BatteryML streamlines data processing, feature extraction, and model application for practical research applications.
CHALLENGES:
Data heterogeneity poses challenges in battery research.
Domain knowledge complexity hinders machine learning application.
Model development requires expertise in both battery science and machine learning.
CONTRIBUTIONS:
Unified data representation method introduced by BatteryML.
Comprehensive open-source platform covering SOC, SOH, and RUL tasks.
Integration of traditional and cutting-edge models for efficient battery research.
RELATED WORK:
Various studies propose physical and semi-empirical models for lithium-ion battery lifetime prediction.
Machine learning provides a data-driven methodology for accurate battery degradation modeling.
BATTERYDATA:
Unified representation method encompassing meta information and charge/discharge cycles introduced by BatteryML.
FEATURE ENGINEERING:
Within-cycle features include QdLinear, Coulombic efficiency, and internal resistance.
Between-cycle features capture degradation patterns across multiple cycles.
AUTOMATIC LABEL ANNOTATION:
BatteryML supports automatic label annotation for supervised battery degradation modeling tasks like RUL, SOH, and SOC estimation.
MODEL DEVELOPMENT:
BatteryML incorporates various off-the-shelf baselines like linear models, tree-based models, neural networks, etc., for accurate lifetime predictions.
Stats
Battery degradation remains a pivotal concern in the energy storage domain.
Machine learning experts often grapple with the intricacies of battery science.
BatteryML introduces a standardized data representation method.
BatteryML covers essential battery research tasks like SOC, SOH, and RUL.
BatteryML seamlessly integrates a wide array of models.