Motion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models
Joonhee Kim, Sanghyun Park, Donghyeong Kim, Eunseon Choi, Soohee Han
AI summary
Problem
Most quadrotor energy planning treats batteries as simple reservoirs, ignoring how motion-induced transient current loads accelerate specific electrochemical degradation mechanisms. A physical bridge connecting dynamic flight currents to internal degradation pathways remains missing.
Approach
The authors developed an end-to-end framework that captures real-flight current profiles with a custom wide-range sensor and replays them in a calibrated, high-fidelity electrochemical battery model to quantify motion-specific degradation mechanisms.
Key results
- End-to-end framework integrating real-flight sensing, model calibration, and virtual degradation assessment
- High-fidelity degradation-coupled P2D battery model calibrated via metaheuristic optimization
- Systematic analysis revealing divergent degradation pathways under identical average energy consumption
- Vertical maneuvers uniquely accelerate loss of active material and SEI growth, while all motions promote micro-crack formation
Why it matters
This work provides quadrotor designers and autonomy researchers with a mechanistic tool to balance flight efficiency with battery longevity, enabling more reliable and longer-lasting aerial missions.
Abstract
Quadrotor endurance is ultimately limited by battery behavior, yet most energy-aware planning treats the battery as a simple energy reservoir and overlooks how flight motions induce dynamic current loads that accelerate battery degradation. This work presents an end-to-end framework for motion-aware battery health assessment in quadrotors. We first design a wide-range current sensing module to capture motion- specific current profiles during real flights, preserving transient features. In parallel, a high-fidelity battery model is calibrated using reference performance tests and a metaheuristic based on a degradation-coupled electrochemical model. By simulating measured flight loads in the calibrated model, we systematically resolve how different flight motions translate into degradation modes—loss of lithium inventory and loss of active material—as well as internal side reactions. The results demonstrate that even when two flight profiles consume the same average energy, their transient load structures can drive different degradation pathways, emphasizing the need for motion-aware battery management that balances efficiency with battery degradation.