Hybrid Machine Learning-Based Estimation of Remaining Useful Life (RUL) and SOH of Lithium-Ion Batteries for EV Applications

This research paper presents a hybrid machine learning-based approach for estimating the Remaining Useful Life (RUL) and State of Health (SOH) of Lithium-Ion (Li-Ion) batteries, specifically tailored for Electric Vehicle (EV) applications. As electric vehicles gain popularity, ensuring the longevity and efficient performance of their battery systems is critical. Accurate predictions of RUL and SOH are essential for enhancing battery management systems (BMS), optimizing energy usage, and improving safety.

The paper integrates various machine learning models to address the challenges in predicting battery performance over time. The proposed hybrid model combines traditional data-driven techniques with advanced machine learning algorithms, enhancing the accuracy of RUL and SOH estimations. Real-world data from EV battery tests are used to validate the effectiveness of the model, demonstrating its ability to provide reliable, real-time predictions of battery life and health.

This study's findings have significant implications for the development of more efficient and sustainable battery management systems, contributing to the growth of the EV industry by extending battery lifespan and improving overall vehicle performance.