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.
