Estimation of state of health for lithium-ion batteries using advanced data-driven techniques

Accurate estimation of the State of Health (SOH) is crucial for ensuring the performance, safety, and longevity of lithium-ion batteries in electric vehicles. Traditional methods, such as Coulomb Counting and the Extended Kalman Filter, often lack the accuracy and computational efficiency required for modern applications. This study proposes an advanced framework that leverages machine learning models to model the nonlinear degradation patterns of lithium-ion batteries by focusing on key features such as voltage, current, internal resistance, and temperature. The proposed framework incorporates optimized pre-processing techniques, including normalization, to improve data quality and ensure consistency across varying battery conditions. Advanced machine learning models, including Adaboost, Xgboost, Ridge Regression, Decision Trees, Random Forests, Artificial Neural Networks, and Long Short-Term Memory Networks (LSTM), are employed to analyze battery performance. Among these, the LSTM network demonstrates outstanding capability in capturing long-term dependencies in sequential battery data, achieving a mean squared error of 0.000115 and an R2 score of 0.9982. It also accurately predicts the remaining life cycle of the battery using temporal patterns derived from MATLAB model datasheets, significantly reducing estimation errors. A comprehensive comparison using performance metrics such as root mean squared error, mean absolute error, and R2 scores highlights the LSTM model's superiority while evaluating the suitability of other approaches. The proposed method not only improves estimation accuracy but also reduces computational demands through optimized feature selection and model training strategies, making it highly suitable for real-time applications in lightweight electric vehicles with limited computational resources. This research bridges the gap between theoretical advancements in data-driven techniques and their practical deployment in real-world battery management systems.