Battery Datasets for Neural Network-Based State-of-Health Estimators: A Review and Implementative Workflow

Management of battery systems for electric vehicles has great importance to ensure safe and efficient operation. State-of-Charge and State-of-Health (SoH) are fundamental parameters to be taken under control even though they cannot be directly measured during vehicle operation. Some control approaches have gained increasing interest thanks to advances in sensor availability, edge computing and the development of big data. In particular, SoH estimation through machine learning (ML) and neural networks (NNs) has been thoroughly investigated due to their great flexibility and potential in mapping non-linear relations within data. The numerous studies available in the literature either employ different extracted features from data to train NNs, or directly use measurement signals as input. Additionally, many studies available in the literature are based on a limited number of publicly available datasets, which mainly encompass cylindrical battery cells with small capacity. Starting from the workflow analysis for developing and implementing ML SoH estimators, this work aims to give an overview of the latest application studies in this field, with a special focus on the analysis of the main datasets available in the literature. In the end, the workflow for the implementation of NN-based SoC estimation is demonstrated with a step-by-step procedure on a publicly available dataset, and a final comparison with non-neural regression algorithms is performed.