Unmanned Aerial Vehicles (UAVs) are increasingly adopted in logistics and mission-critical applications, where reliable battery state-of-energy information is essential for safe planning and execution. However, robust voltage prediction for real-time operations remains underexplored, and existing approaches have not explored the possibility of combining offline prediction with discrepancy-triggered in-flight updates to handle the uncertainty raising from the lack of knowledge about future power profiles. In this sense, this paper proposes a dual-phase voltage prediction framework that (i) provides a first estimate of the voltage trend along a planned route using an offline multi-criteria segmentation of historical flights and a route-matching procedure, and (ii) refines predictions in-flight through a discrepancy-triggered update based on the Continuous Ranked Probability Score (CRPS), rerunning the model only when significant deviations are detected to limit computational burden and to adapt to unseen mission profiles. The method combines a hybrid physics-based/data-driven formulation with a Gated Recurrent Unit (GRU) trained on up to 200 flights to model battery behavior and to forecast the full voltage trajectory. Validation on a publicly available dataset shows that the proposed offline-online strategy substantially improves prediction accuracy, reducing MAE from 0.181 V to 0.087 V (51 % improvement). A sensitivity analysis on the CRPS threshold parameters highlights the trade-off between error reduction and computational cost. The overall work incorporates innovative methodologies with a focus on practical applicability, thus facilitating the advancement of ML strategies in these domains.

Existing open-source traffic tools accurately reproduce driver behavior and congestion for conventional internalcombustion vehicles. However, in the case of electric vehicles (EVs), they often fail to incorporate critical electrical variables, such as battery voltage, power demand, and Stateof-Health, which limits their applicability in operational planning and decision-making. This paper introduces a simulation platform tailored for EVs that bridges the gap between traditional transportation models and the needs of the PHM community in electromobility. The proposed platform combines power and energy consumption profiles derived from Gaussian Mixture Models with physics-based representations of battery behavior. Model parameters are calibrated using a publicly available dataset collected in Ann Arbor, Michigan. Each trip is partitioned into segments based on abrupt changes in speed, ensuring uniform operating conditions within segments and enhancing model transferability across routes. The platform simulates vehicle speed, electrical power demand, State-of-Charge (SoC), terminal voltage, and incremental capacity loss at each simulation step. Battery degradation is estimated through an empirical model fitted to long-term cycling data. A case study demonstrates the simulator’s ability to compare route alternatives between a shared origin and destination. Results show that the shortest path is not always the most energy-efficient nor the least degrading, highlighting the value of health-aware routing. The platform will be publicly released to enable reproducible testing of SoC estimation, range prediction, and degradation forecasting without requiring extensive instrumentation or prolonged field testing.

Accurately forecasting lithium-ion battery degradation is essential for safe and cost-effective electrification. This work presents a cycle-wise degradation model that estimates capacity loss based on usage conditions, using only data from a single reference degradation campaign. The model characterizes each equivalent cycle by features extracted from the battery’s State of Charge (SoC) profile; specifically, the SoC Range (SR) and Average Swing Range (ASR), and the average ambient temperature. A Similarity-Based Model maps SR and ASR to a normalized expected cycle life, which is further adjusted using a temperature correction factor derived from empirical studies. Unlike approaches requiring chemistryspecific testing, this method assumes, and validates, that cells under similar conditions degrade similarly, allowing generalization across battery types. The degradation rate also incorporates uncertainty through Kernel Density Estimation of observed cycle-to-cycle variations in supervised datasets. Validation was performed using a public lithium-ion degradation dataset, where the model predicted the State of Health (SoH) trajectory of a test cell with a Mean Absolute Error (MAE) of 0.27% of SoH percentage. Because the model uses only operational features readily measured in battery systems, it is practical for integration into battery management systems for real-time SoH tracking, predictive maintenance, and usage optimization. Future work will expand the feature set and refine uncertainty quantification to further improve predictive robustness.