Functional Tree Forest: A Hybrid Bagging-Boosting Ensemble Machine Learning Approach for Battery Fault Diagnosis in Electric Vehicles

Accurate battery fault detection and diagnosis (FDD) is essential for ensuring the safety, health monitoring, and operational performance of electric vehicles (EVs). Yet, existing research still suffers from issues such as limited classification accuracy due to non-linear feature interactions and insufficient interpretability. To address these limitations, this study proposes the Functional Tree Forest (FTF), a hybrid Bagging-Boosting ensemble machine learning approach that combines multiple functional trees with LogitBoost-based modeling, while preserving model explainability. Unlike conventional decision trees that rely solely on axis-aligned univariate attribute splits, the FTF architecture dynamically integrates multivariate linear regression functions at both internal nodes and terminal leaf nodes, capturing complex feature interactions. The proposed method was evaluated on five publicly available EV battery fault datasets. The experimental results showed that FTF achieved top numerical accuracy across 13 baseline machine learning approaches, including linear models, conventional tree-based classifiers, ensembles, and network-based topologies. Furthermore, when compared with results directly given in the state-of-the-art studies utilizing identical datasets under varying experimental protocols, FTF yielded competitive performance relative to previously reported literature, although the magnitude of the performance difference varied across datasets.