July 25, 2025Open Access

State of Charge Prediction for Li-Ion Batteries in EVs for Traffic Microsimulation

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Novel methodology integrates kinematic data with traffic microsimulation platforms, suggesting improvements in urban mobility planning.

The proposed method predicts state of charge with high accuracy (R² = 0.86) using only four input variables.
By combining Vissim's realism with SUMO's efficiency, the model offers a flexible framework for urban environments.
The minimalist design reduces reliance on expensive sensors while supporting large-scale simulations effectively.
Results enhance traffic modeling and infrastructure development, promoting sustainable urban mobility systems.

Maksymilian Mądziel (2025) studied this question.

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