Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

• Published in: Energy technology (Weinheim, Germany) Germany), 2024-03, Vol.12 (3), p.n/a
• Main Authors: Qiang, Xingzi, Tang, Yi, Wu, Longxing, Lyu, Zhiqiang
• Format: Article
• Language: English
• Subjects: Aging; Back propagation networks; battery management systems; Bayesian analysis; Bayesian optimization; Datasets; decision tree; Decision trees; Electric vehicles; Estimation; hybrid programming; Lithium-ion batteries; Neural networks; Optimization; state of health; Support vector machines
• ISSN: 2194-4288; 2194-4296
• DOI: 10.1002/ente.202301065

Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs. Based on the standard charging process, two battery aging features are extracted by employing the incremental capacity analysis. To capture the underlying relationship between aging features and capacity, the hyperparameters of a decision tree are determined through Bayesian optimization. The decision tree model is trained offline and then deployed on the LabVIEW and MATLAB platforms for online SOH estimation.