Author(s)

Helena Schmidt^1*, Jan Neumann²

Affiliation(s)

¹University of Leipzig, Leipzig, Germany.

²University of Rostock, Rostock, Germany.

Corresponding Author

Helena Schmidt

ABSTRACT

Accurate prediction of battery degradation is crucial for ensuring the reliability, safety, and performance of electric vehicles (EVs). While data-driven machine learning (ML) models offer high prediction accuracy, they often lack physical interpretability, limiting their application in critical systems. On the other hand, purely physics-based models provide deeper understanding but struggle to generalize across diverse operating conditions. This paper proposes a hybrid modeling approach that combines physics-informed machine learning (PIML) with empirical battery aging data to achieve both accuracy and interpretability. The model incorporates domain knowledge—such as electrochemical degradation mechanisms, capacity fade laws, and thermal effects—into a learning framework based on recurrent neural networks (RNNs) and gradient boosting. Experimental results on real-world EV battery datasets demonstrate that the hybrid model outperforms standalone physics-based and ML models in both prediction precision and consistency. This approach opens new avenues for predictive maintenance, extended battery lifespan, and optimized battery usage strategies.

KEYWORDS

Electric Vehicle (EV); Battery degradation; Physics-Informed Machine Learning (PIML); Hybrid modeling; Recurrent Neural Network (RNN); Capacity fade; Battery aging; Predictive maintenance

CITE THIS PAPER

Helena Schmidt, Jan Neumann. Hybrid modeling of electric vehicle battery degradation using physics-informed machine learning. Journal of Manufacturing Science and Mechanical Engineering. 2025, 3(1): 22-27. DOI: https://doi.org/10.61784/msme3016.

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