Existing open-source traffic tools accurately reproduce driver behavior and congestion for conventional internal-combustion vehicles. However, in the case of electric vehicles (EVs), they often fail to incorporate critical electrical variables, such as battery voltage, power demand, and State-of-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.