Drone battery life prediction and nest intelligent charging management method and system
By constructing a digital twin and relationship diagram of the battery, and combining multimodal data and electrochemical models, the charging strategy is dynamically adjusted, which solves the problems of battery health status assessment error and safety risks in UAV nests, and realizes high-precision life prediction and safety management.
Patent Information
- Application Number
- CN202610525844.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-17
AI Technical Summary
Existing drone nests cannot accurately assess battery health, resulting in large errors in battery life prediction, delayed safety risk warnings, and the inability to dynamically adjust charging strategies based on battery aging and lithium plating risks, posing safety hazards.
By acquiring multimodal data, a digital twin of the battery is constructed. By combining a neural network model that integrates physical constraints with an electrochemical model, a battery relationship diagram is generated, and a personalized pulse charging scheme is output. The charging strategy is dynamically adjusted to improve the accuracy and safety of battery health status assessment.
It achieves high-precision battery life prediction with limited sensor data, reduces the impact of sensor noise, avoids the risks of overcharging, over-discharging and lithium plating, and extends battery life.
Smart Images
Figure CN122418928A_ABST