A Data-Driven Method for Battery Charging Capacity Abnormality Diagnosis in Electric Vehicle Applications

Enabling charging capacity abnormality diagnosis is essential for ensuring battery operation safety in electric vehicle (EV) applications. In this article, a data-driven method is proposed for battery charging capacity diagnosis based on massive real-world EV operating data. Using the charging rate, temperature, state of charge, and accumulated driving mileage as the inputs, a tree-based prediction model is developed with a polynomial feature combination used for model training. A statistics-based method is then used to diagnose battery charging capacity abnormity by analyzing the error distribution of large sets of data. The proposed tree-based prediction model is compared with other state-of-the-art methods and is shown to have the highest prediction accuracy. The holistic diagnosis scheme is verified using unseen data.

Automated summary

In this article, a data-driven method based on massive real-world EV operating data is proposed for diagnosing battery charging capacity abnormalities. By utilizing multiple input parameters and a tree-based prediction model for training, along with a statistical method for abnormality diagnosis, the proposed method demonstrates the highest prediction accuracy.