Predictive Modeling of Lithium-Ion Battery Degradation Using Supervised Machine Learning Algorithms
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As the number of EVs and renewable energy storage systems grows, the need for dependable battery
management systems (BMS) becomes more critical. Accurately predicting the State of Health (SOH) and
Remaining Useful Life (RUL) of Lithium-Ion (Li-ion) batteries is crucial to ensure operational safety, prevent
catastrophic failures, and optimize maintenance schedules. The conventional degradation assessment
approaches are, however, very complex and highly non-linear, which complicates the assessment of
degradation using conventional approaches. Thie research question that requires investigation: What is the
best machine learning or deep learning architecture to be used to extract these non-linear patterns and to be
able to make accurate, scalable, and interpretable battery health predictions?
In response to this, recent studies have focused on data-driven approaches based on sophisticated
computational models that can map battery degradation, avoiding the need to solve complex electrochemical
equations. Classical tree-based ensemble models like Random Forest (RF) or Extreme Gradient Boosting
(XGBoost) are widely used for their high performance and relatively low computational requirements, whereas
sequential deep learning models like Long Short-Term Memory (LSTM) networks have theoretically better
potential to capture temporal dynamics among continuous time-series data. The use of explainable artificial
intelligence (XAI) systems like Shapley Additive explanations (SHAP) provides a valuable
This study compares and tests the performance of these two different architectural archetypes, based on a
thorough toolkit of data-driven metrics developed from open, publicly available data sets from NASA and
Stanford. Work on feature engineering involved extracting multi-cycle dynamic characteristics, and the models
were rigorously evaluated on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The
comparative analysis shows that the sequential models based on LSTM work well for extracting long-term
temporal dynamics between continuous-time signals, but XGBoost is able to achieve a high level of accuracy
with much less computational load.