Machine learning in battery systems: Improving aging and performance modeling

Electrical equivalent circuit models (EECMs) are commonly used in battery management systems (BMS) for applications such as automotive and stationary energy storage. EECMs provide a robust framework for modeling battery behavior by representing the electrochemical dynamics of the battery in a simplified manner. However, EECMs often struggle to fully capture the complex and changing nature of battery aging, leading to discrepancies between model predictions and real-world data. This can affect the accuracy of aging predictions, which are critical for reliable battery performance and lifetime.

To address this problem, this thesis investigates the integration of machine learning techniques into classical EECMs, with a particular focus on exploratory data analysis (EDA) for aging prediction. EDA plays a central role in identifying important patterns and extracting significant features from large data sets, which are essential for accurately modeling battery aging. By analyzing variables such as cycle count, depth of discharge (DOD), temperature and calendar time, we can better understand the underlying factors that contribute to battery degradation.

At BatterieIngenieure, we use our extensive battery testing capabilities and comprehensive databases to perform in-depth EDA that provides valuable insights into aging behavior under different operating conditions. Through advanced clustering methods, we aim to classify ageing conditions and discover hidden correlations in the data to ultimately improve the predictive accuracy of our models. This approach allows us to develop more sophisticated ageing prediction models that adapt to real-world conditions and ensure more reliable battery management.

Our expertise in battery modeling, testing and data analysis positions us at the forefront of innovation in aging prediction. By combining classical EECMs with machine learning for feature analysis, we develop state-of-the-art solutions to improve prediction accuracy in intelligent battery monitoring and management systems. Our extensive testing and validation process, supported by real-world aging data, ensures that our models are fine-tuned and highly reliable in various applications.