Heat-Mileage AI-BMS

Bringing usage history into battery management

Heat-Mileage AI-BMS is a team study exploring how accumulated use can complement current temperature and state of charge. Batteries in similar present states may have experienced different fast-charging, parking and regenerative-braking histories.

Indicator and control profiles

The proposed Heat-Mileage indicator accumulates temperature, current, SOC and event-related stress. Usage patterns and candidate profiles inform charging current, cooling, balancing and regenerative limits within vehicle-side safety constraints.

Simulation comparison

The final presentation covers five scenarios: fast charging, highway fast charging, urban regeneration, hot parking and cold charging. Six controllers and 30 repetitions per condition form 900 Monte Carlo runs. The comparison considers accumulated stress alongside delivered service, using MATLAB and Simulink models to examine the control structure.

Contribution and outcome

I participated in the User-Adaptive Heat-Mileage AI-BMS study and final presentation. The team received an Encouragement Award in the future-mobility competition.

Validation limits

This is a simulation-based proof of concept. Heat-Mileage is not a measured, calibrated remaining-life estimate. The study does not establish actual life extension, real-vehicle performance or superiority across every operating condition. Calibration, sensing validation and vehicle-specific safety checks remain necessary.