A Review of Data-Driven Approaches and Datasets for Battery State of Health Estimation

Abstract

<div>This article surveys the most recent data-driven methods of lithium-ion (Li-ion) battery state of health (SOH) estimation methods and dataset resources utilized in electrified vehicles (EV) and their potential adoption for automotive battery management systems. These include regression-based models, ensemble learners, deep neural networks, and physics-informed hybrid methods. The review describes estimation methods found in articles published between 2023 and 2025, and investigates their differences in terms of estimation accuracy, data requirement, interpretability, and real-time deployment ability. The article traverses the dataset space, focusing on laboratory aging datasets, vehicle field–based datasets, telematics-derived records, and synthetic or augmented datasets, to underline that model performance in the estimation of SOH cannot be disentangled from the quality of the data, the operating coverage, and the transfer conditions. Apart from the model design, this work reviews the large-scale estimation pipeline, which involves preprocessing under sensor noise and irregular timestamps, feature extraction from incremental capacity, differential voltage, relaxation response and impedance-related indicators, and uncertainty handling for diagnostics and safety-based decision support. Practical constraints to the deployment of embedded BMS are covered. Such as ECU memory and computing limits, communication overhead, calibration effort, update approach, and functional-safety requirements. The review determines that the distance between laboratory validation and field robustness is large raising a need for more work in this area and also, that domain adaptation, federated learning, and improving benchmarking practice turn out to be promising directions for improving generalization and reproducibility. The article concludes that future advances in automotive SOH estimation will not only rely on better learning algorithms but also on improvement in the availability of realistic and field representative data, the application of robust evaluation mechanisms, and methods that are developed under real BMS constraints.</div>