A Reproducible Deep Learning Workflow for Predicting Lithium-Ion Battery Performance Using Multichannel Cycling Data

Summary

This protocol presents a reproducible deep learning workflow using multichannel cycling data and engineered features to predict lithium-ion battery state of health, remaining useful life, and energy-storage performance.

Abstract

This protocol presents a reproducible deep-learning workflow for predicting lithium-ion battery performance using multichannel cycling data. To address limitations in existing data-driven models related to multisource information integration and feature representation, the method utilizes five publicly available battery datasets. The inputs include raw operational signals, including voltage, current, capacity, temperature, internal resistance, and coulombic and energy efficiency, together with engineered features such as incremental capacity, differential voltage, and energy throughput. The protocol details standardized preprocessing procedures for constructing battery-level temporal tensors across multiple observation windows. To improve reproducibility, the workflow evaluates candidate neural-network architectures, including deep neural networks, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, CNN–LSTM, and transformer-based models, using fixed random seeds, repeated evaluation runs, and environment tracking. Representative results demonstrated that the CNN–LSTM architecture with integrated multichannel data and engineered features achieved favorable predictive performance among the evaluated models. The selected configuration achieved a mean absolute error of 0.024 and an R² value of 0.955 for state-of-health estimation while maintaining high predictive performance for remaining useful life and dischargeable-energy estimation. Ablation and robustness analyses further demonstrated the contribution of multichannel feature integration and the stability of the reproducibility-focused workflow across repeated evaluations. This standardized methodology provides a reproducible framework for battery lifecycle management and predictive modeling under diverse operating conditions.

Introduction

Lithium-ion batteries are central to the global transition toward renewable energy because of their high energy density, extended cycle life, and low self-discharge rates across applications ranging from portable electronics to grid-scale energy storage systems. However, prolonged operation inevitably leads to gradual capacity degradation and increased internal resistance¹. This performance decline not only reduces economic viability but also introduces severe safety hazards, including thermal runaway. As the deployment of large-scale energy storage systems continues to accelerate, battery lifecycle management has become increasingly complex

Protocol

Institutional ethics approval was not required for this study because the research exclusively utilized publicly available lithium-ion battery cycling datasets and did not involve human participants, animal subjects, or biological samples.

1. Data Acquisition and Organization

1.  Acquire multichannel cycling datasets
   1. Download lithium-ion battery cycling datasets from publicly available battery repositories containing charge-discharge cycling records. Acquire the CALCE, SANYO, PANASONIC, KOKAM, and GOTION datasets directly from their respective institutional open-access repositories or d....

Results

Multichannel Cycling Signals and Data Distribution

Representative multichannel cycling signals and performance distributions were analyzed to characterize the cycling-response behavior of lithium-ion batteries. The study utilized five lithium-ion battery datasets representing multiple chemical systems, including lithium cobalt oxide (LCO), nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and LFP (Table 1). Core cycling variables, includ.......

Discussion

The reproducible DL workflow described in this protocol demonstrated accurate prediction of lithium-ion battery performance using multichannel cycling data. The workflow achieved a test MAE of 0.024 and a R² value of 0.955 for SOH estimation under the evaluated experimental conditions, supporting its potential utility for battery lifecycle management and energy-storage monitoring applications^11,12,13.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CALCE battery dataset	Center for Advanced Life Cycle Engineering (CALCE), University of Maryland	https://calce.umd.edu/battery-data	Source of multichannel lithium-ion battery cycling data for model training and validation
CUDA Toolkit	NVIDIA	Version 11.8	GPU acceleration for DL model training
GOTION IFP20100140A dataset	Mendeley Data	https://doi.org/10.17632/vpw4t7ytbx.2	External validation and multichannel battery degradation analysis
KOKAM SLPB533459H4 dataset	Mendeley Data	https://doi.org/10.17632/7w4y4fzzbb.1	Multichannel battery degradation modeling and validation
Matplotlib	Matplotlib Development Team	Version 3.7.1	Visualization of model performance, sensitivity analysis, and reproducibility results
NumPy	NumPy Developers	Version 1.24.3	Numerical computation and tensor construction
PANASONIC NCR18650BD dataset	Mendeley Data	https://doi.org/10.17632/wykht8y7tg.1	Source of multichannel cycling data and engineered-feature extraction
Pandas	Pandas Developers	Version 2.0.3	Data organization, preprocessing, and tabular-data management
PyTorch	PyTorch Foundation	Version 2.0.1	Construction and training of deep-learning architectures, including the CNN–LSTM model
Python	Python Software Foundation	Version 3.10.12	Data preprocessing, feature engineering, model training, and evaluation
SANYO UR18650E dataset	NASA Prognostics Data Repository	https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/	Source of multichannel cycling data for reproducibility evaluation and model validation
Scikit-learn	Scikit-learn Developers	Version 1.2.2	Baseline-model implementation, dataset partitioning, and performance-metric calculation