Machine Learning Approaches for Battery Durability Prediction: Leveraging Diverse Datasets and Advanced Feature Engineering Techniques
Authors: Kumari Simran , Moon Jungwon , Yang Xin , Arihara Kazuki , Ono Masaki , Uchimura Masanobu , Yamamoto Kensuke , Radhakrishnan Balachandran , Ohma Atsushi , Kuwata Shigemasa 
Published: December 2024

Lithium-ion batteries play a pivotal role in various applications due to their high energy density and longevity. However, accurately predicting their lifespan remains a significant challenge due to the complex and varied degradation patterns inherent in battery chemistry. This study employs advanced machine learning (ML) techniques to forecast the State of Health (SOH) of lithium-ion batteries, focusing on a diverse range of chemistries.
The study encompasses a comprehensive analysis of battery datasets, combining in-house data and publicly available datasets featuring varied chemistries. By incorporating data from multiple sources, including chemistries such as NMC 811, NMC 532, LFP, etc the model's robustness is enhanced, enabling it to better handle the variability in battery chemistry.
Feature engineering is a critical component of the model development process. Various battery characteristics, including anode-cathode balance, resistance metrics, and charge/discharge behavior, are extracted and integrated. Advanced techniques such as Minimum Redundancy Maximum Relevance (MRMR) feature selection and Box-Cox transformation are employed to optimize feature relevance, reduce data dimensionality, and enhance model interpretability.
Model performance is rigorously evaluated using established metrics such as Mean Absolute Percentage Error (MAPE) and Mean Squared Percentage Error (MSPE). Initial predictions using the first 10 cycles reveal a trend of increasing prediction errors as SOH values rise. However, when focusing on SOH values above 0.75, prediction accuracy significantly improves, highlighting the effectiveness of this approach for batteries in good condition.
The study also explores the evolving importance of different feature sets across battery lifetimes. Capacity-based features prove crucial for early-cycle SOH prediction, reflecting the initial health status and degradation patterns of the battery. As the battery undergoes more cycles, the predictive power of CC/CV curve-based features becomes increasingly significant, capturing nuanced charging and discharging behaviors.
MRMR feature selection further refines the model, identifying the top 40 influential features that are most relevant to SOH prediction. This process enhances model interpretability and focuses on features that capture the complex relationships between battery characteristics and degradation dynamics.
Optimized Random Forest regression models demonstrate robust performance across various prediction scenarios. For SOH at 250 cycle predictions, the model achieves an MSPE of 0.13% and MAPE of 2.54%, underscoring its accuracy and reliability for batteries in good condition.
In conclusion, this research demonstrates the potential of ML techniques in enhancing lithium-ion battery durability prediction. By leveraging diverse datasets, advanced feature engineering techniques, and rigorous model evaluation, we can significantly improve prediction accuracy. This advancement holds promise for proactive battery management, optimization of battery performance, and acceleration of battery technology development.

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