Symbolic regression plays a significant role in developing battery aging models by providing a data-driven, interpretable, and flexible way to model complex aging phenomena in lithium-ion batteries. Its key contributions are as follows:
Role of Symbolic Regression in Battery Aging Models
- Inferring Physically Interpretable Models: Symbolic regression, often implemented via genetic programming, enables the automated discovery of mathematical expressions that describe battery aging data without requiring prior domain knowledge. This results in models that are not only accurate but also physically meaningful and interpretable, bridging the gap between purely empirical machine learning models and traditional semi-empirical aging models.
- Enhancing Prediction Accuracy: By evolving mathematical models through an evolutionary algorithm, symbolic regression improves predictive accuracy for battery lifetime and state-of-health over complex aging phenomena such as cycle and calendar aging. Studies demonstrate significant error reductions — e.g., 38% improvement in predictions over storage time and up to 77% error reduction over other stress factors — compared to established methods.
- Capturing Complex Aging Mechanisms: Symbolic regression helps address limitations in conventional algebraic aging models that assume simplified mechanisms, such as the t0.5 dependence related to SEI (solid-electrolyte interphase) growth. It allows for the discovery of more accurate and nuanced models that reflect the non-linear and multi-factor nature of battery degradation processes.
- Low-Complexity and Generalizable Models: The evolutionary approach in symbolic regression balances model complexity with accuracy, yielding reduced-order models that are generalizable across different battery types and operating conditions, which is crucial for practical battery management and lifetime estimation.
- Comparison with Other Modeling Approaches: Symbolic regression models have been compared with semi-empirical and machine learning models such as Long Short-Term Memory (LSTM) networks, showing competitive or superior performance while maintaining interpretability, which is valuable for scientific understanding and industrial application.
- Application in State-of-Charge Estimation: Beyond aging, symbolic regression has been used to develop accurate and straightforward models for state-of-charge (SOC) estimation, demonstrating its versatility in battery management systems.
In summary, symbolic regression is a powerful tool in battery aging modeling, enabling the creation of accurate, interpretable, and computationally efficient models that capture the complex physics and chemistry of battery degradation. This leads to better lifetime prediction, improved battery management, and advances in electric vehicle technology and sustainability.
Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/what-role-does-symbolic-regression-play-in-battery-aging-models/
