
Iago Dias contributed to the verg1lio/XangoER repository by developing two core features over a two-month period. He implemented Pulse Width Modulation (PWM) support for the Motor class, introducing inverter switch state definitions and PWM signal generation to enhance the realism of digital twin powertrain simulations. Later, he delivered a battery optimization and 3D visualization module, enabling calculations for battery pack dimensions, weight, and energy capacity, with interactive data visualization using Plotly. Working primarily in Python, Iago applied skills in control systems, embedded systems, and data visualization, producing well-integrated features that improved simulation fidelity and supported data-driven design workflows.
January 2026 monthly summary for verg1lio/XangoER highlighting a new battery design capability and visualization features. Key deliverable: Battery Optimization and 3D Visualization Module with calculations for battery pack dimensions, weight, and energy capacity, plus a Plotly-based visualization. No major bugs fixed this period.
January 2026 monthly summary for verg1lio/XangoER highlighting a new battery design capability and visualization features. Key deliverable: Battery Optimization and 3D Visualization Module with calculations for battery pack dimensions, weight, and energy capacity, plus a Plotly-based visualization. No major bugs fixed this period.
November 2024: Delivered PWM support for the Motor class in the verg1lio/XangoER digital twin powertrain simulation, enhancing realism and configurability of motor drive models. The feature introduces inverter switch state definitions, PWM signal generation based on modulated voltages, new parameters and methods, and updated simulation examples. This work strengthens the platform’s digital twin fidelity and enables faster validation of control strategies for powertrain projects.
November 2024: Delivered PWM support for the Motor class in the verg1lio/XangoER digital twin powertrain simulation, enhancing realism and configurability of motor drive models. The feature introduces inverter switch state definitions, PWM signal generation based on modulated voltages, new parameters and methods, and updated simulation examples. This work strengthens the platform’s digital twin fidelity and enables faster validation of control strategies for powertrain projects.

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