Hybrid Tree-based Machine Learning Models for State-of-Charge and Core Temperature Estimation in EV Batteries

Accurate estimation of State-of-Charge (SoC) and core temperature is fundamental to optimizing the performance, safety, and longevity of Lithium-Ion Batteries (LiBs), particularly in Electric Vehicles (EVs). Traditional estimation methods fail to account for the complex, non-linear interactions between thermal and electrical dynamics and the challenges posed by data uncertainty. This paper introduces a comprehensive framework to estimate core temperature and SoC, considering diverse charging levels and uncertainties. For the data generation phase, first, features are extracted from a control-oriented electro-thermal coupling model, offering a computationally efficient alternative to resource-intensive experiments and avoiding a lack of data. Then, a correlation analysis between the ambient temperature and each feature (e.g., internal resistances, thermal capacity, and time) is performed, with linear regression applied to generate features showing strong linear relationships, and a Gaussian Multivariate Copula model is used to generate features with weak or non-linear dependencies. For the estimation phase, hybrid tree-based models were employed due to their robustness in handling complex and noisy datasets, and computational efficiency while integrating the complementary strengths of individual models. Among the combinations tested, the Extra Trees Regressor-Random Forest (ETR-RF) model delivered the highest estimation accuracy, while the Decision Tree-LightGBM (DT-LGBM) model exhibited the fastest training time. The ETR-RF model consistently reduced estimation errors, achieving RMSE values of 0.047°C and 1.25°C for core temperature and 0.5% and 0.56% for SoC estimation across white Gaussian noise levels, with standard deviations of 0.02 and 0.2, respectively. In contrast, the DT-LGBM model prioritized computational efficiency, requiring 1 second (average training time) for SoC estimation and 0.66 seconds for core temperature estimation, performed on a system equipped with an Intel Core i7-7500U CPU (2.70GHz base, 2.90GHz boost).