Low Data Machine Learning Framework for Accelerated Lithium-Ion Battery Degradation Prediction

Lithium-ion batteries (LIBs) experience degradation influenced by various operational and environmental factors, including temperature, depth of discharge (DoD), state of charge (SoC), and current rates. Among these, temperature is a critical driver, as elevated temperatures accelerate chemical reactions, induce thermal stress, and promote side reactions such as lithium plating. These mechanisms degrade battery components, reducing capacity and performance over time. Understanding and predicting capacity fade under such conditions is essential for improving LIB safety, efficiency, and longevity. Traditional physics-based models have been widely used for degradation prediction but face challenges in operational settings due to limited and non-standardized input data. Recent advancements in machine learning (ML), particularly low-data ML models, offer promising alternatives for accelerated degradation prediction under such constraints. This study presents a novel framework integrating two advanced low-data ML models—a cycle-based autoencoder (CD-Net) and a time-based mixture of experts (MoE) model—to address the challenges of predicting LIB degradation while accounting for thermal effects. The CD-Net autoencoder tracks capacity degradation across operational cycles by incorporating cell-specific chemistry information. It captures cycle-specific degradation patterns and correlates them with temperature variations, enabling accurate real-time state-of-health (SoH) predictions with minimal latency. This model is particularly suited for integrating battery management systems (BMS), offering insights into the interplay between thermal conditions and battery performance. Complementing this, the MoE model provides long-term capacity degradation forecasts using limited early-cycle data. Its ensemble architecture adapts to diverse cathode chemistries and thermal profiles, effectively generalizing even under extreme temperature conditions or data scarcity. Additionally, the MoE model generates high-fidelity synthetic datasets validated through statistical techniques such as Wasserstein Distance and Kolmogorov-Smirnov tests. These synthetic datasets enhance predictive accuracy and support the exploration of emerging chemistries and thermal scenarios. By combining these models, the framework delivers a comprehensive approach to LIB degradation analysis. The CD-Net excels in real-time monitoring, while the MoE model offers robust long-term forecasting and synthetic data generation capabilities. Together, they address challenges posed by extreme conditions such as rapid temperature fluctuations, overcharge/discharge scenarios, and high C-rate operations. This integrated methodology facilitates accelerated testing of new chemistries and operational profiles while reducing reliance on extensive experimental data. The framework provides actionable insights into thermal management system design by simulating temperature-driven degradation mechanisms. It supports safer and more efficient LIB systems in demanding applications like fast charging, low-temperature environments, and high-performance scenarios. This approach advances predictive modeling for LIBs, contributing to improved safety, energy storage efficiency, and the development of next-generation battery technologies.