Energy Storage Lifetime Estimation Using Moisture Diffusion

The patent applies preliminary action by measuring environmental relative humidity and temperature early in the device lifecycle, then using a moisture diffusion model to predict future moisture ingress and estimate consumed lifetime before actual degradation occurs. This allows proactive identification of devices at risk without waiting for actual failure symptoms, enabling timely but not premature replacement decisions.

The patent implements feedback by continuously monitoring environmental conditions (relative humidity and temperature) and using this data to update the moisture diffusion model predictions. The model feedback loop compares predicted moisture levels against safety thresholds, dynamically adjusting lifetime estimates and triggering alerts when devices approach critical moisture accumulation, thus optimizing the replacement timing balance between safety and lifetime.

The patent applies parameter changes by incorporating environmental variables (relative humidity and temperature) as dynamic inputs to the moisture diffusion model. The model adjusts its predictions based on measured environmental parameters, accounting for their varying influences on moisture diffusion rates. This allows accurate lifetime estimation across different environmental conditions by continuously updating predictions based on actual measured parameters rather than assuming constant conditions.

The patent replaces direct internal moisture measurement (which would be invasive and complex) with an environmental-based predictive model. Instead of mechanically probing internal device conditions, the system uses environmental sensor data combined with diffusion physics to infer internal moisture levels, substituting a complex measurement system with a simpler environmental monitoring and modeling approach that maintains precision across varying conditions.