Keeping an electric car warm draws on the same battery that powers its wheels. Researchers in China have examined how recovered braking energy and machine learning could help manage that demand, using data from 72 real-world BMW i3 trips.

The study was conducted by Baofan Chen of Chongqing Water Resources and Electric Engineering College and Chunrong Zhou of Chongqing Vocational College of Transportation, who compared four algorithms for predicting final battery charge and travel distance.

Their best results came from combining CatBoost with the Horned Lizard Optimization algorithm.

The framework improved prediction performance over the baseline model, while separate modeling examined how regenerative braking energy could support cabin heating.

The researchers began with recorded battery and heating circuit data from the BMW i3 journeys.

These measurements captured actual operating conditions, including changing temperatures and driving behavior, rather than relying entirely on standardized laboratory cycles.

They constructed vehicle and heating circuit models, then checked their simulations against the recorded trips to assess whether they reproduced the car’s electrical and thermal behavior.

The team also developed strategies for directing regenerative braking energy into cabin heating. Regenerative braking recovers part of a vehicle’s kinetic energy as electricity during deceleration.

Using that recovered energy for heating can reduce demand on the traction battery. The study reported improvements in driving range and battery life from these strategies, although the supplied account did not specify the size of those gains.

The machine learning analysis used environmental, vehicle, battery, and thermal system variables to predict final state of charge and distance traveled.

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