Estimating battery state of health (SOH) from field data is essential to ensure successful operation and increase the uptime of battery electric vehicles (BEVs). Most studies in the literature propose methods relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, were collected over approximately 20 months of operation from 10 electric commercial vehicles. During this period, a maximum battery degradation of 4% is observed within the fleet. Firstly, a model-based framework was introduced, in which a second-order battery equivalent circuit model (ECM) was coupled with an extended Kalman filter (EKF) to estimate the battery SOH. Results confirmed that the EKF is able to accurately capture the battery's physical behavior and degradation trend, yielding a maximum root mean square error (RMSE) of 1.23% when compared with the SOH signal provided by the onboard BMS. However, a Kalman filter requires accurate model parameter identification and high-frequency measurement data, leading to increased computational costs. To bridge these gaps, this paper utilizes the SOH estimates obtained from the EKF to train and validate a feedforward neural network (FNN) model, specifically designed to operate on aggregated metrics. The FNN model can provide accurate SOH estimates, with a RMSE as low as 0.26% during the testing phase. The approach proposed in this work combines the interpretability of model-based methods with the scalability and reduced data dimensionality of machine learning (ML) ones, making it more suitable for monitoring battery SOH in large fleets of BEVs.