Aging models are mathematical or physical models used to describe and predict the gradual decline of battery performance over time or during use. Batteries will experience capacity decline, internal resistance increase, power decline, etc. during long-term use. These changes are collectively referred to as “aging”. Aging models help researchers and engineers evaluate the service life and reliability of batteries by simulating these changes. Aging models are mainly divided into three categories: Empirical Models : Based on a large amount of experimental data, the battery degradation trend is obtained through fitting, which is suitable for life prediction under specific conditions.
Mechanistic or Physics-Based Models : Based on the physical and chemical reaction mechanisms inside the battery, such as electrode material degradation, electrolyte decomposition, etc., they have high explainability.
Data-Driven Models : Combine machine learning, big data and other methods to extract aging patterns from actual operating data, suitable for intelligent prediction and online health assessment.
