Abstract
Vehicle HVAC (Heating, Ventilation, and Air Conditioning) systems rely on solar load sensing to bias cabin cooling and maintain occupant thermal comfort. Conventional implementations use a single dash-mounted sunload sensor to estimate an aggregate solar heat load across the entire cabin, applying a uniform or, at best, a fixed left/right cooling bias. This approach does not account for the vehicle's actual heading relative to the sun, transient shading events, or differences in glass geometry and tint across occupant zones. This publication discloses a sensor fusion method that combines GPS-derived solar position geometry, vehicle heading, the existing single sunload sensor, and camera-based cloud/shading classification with a machine-learned residual correction model to estimate true per-zone solar heat load and to automatically bias zonal HVAC settings accordingly, without requiring additional per-zone sunload sensor hardware.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Sensor Fusion Method for In-Cabin Zonal Solar Load Estimation and HVAC Mode Control", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11475