飞行动作不同，电池损耗路径也不同，该研究提出动态评估方法。
Motion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models
- 通过实测飞行电流数据与高精度电化学模型结合，捕捉动作相关耗电特征。
- 相同平均能耗下，不同飞行动作导致锂损失和活性材料衰减差异显著。
- 适合无人机续航优化与电池健康管理方向的研究者参考。
四旋翼无人机的续航终受限于电池行为，但多数能量感知规划将电池视为简单储能单元，忽视飞行动作引发的动态电流负载对电池退化的加速作用。本文提出端到端的四旋翼运动感知电池健康评估框架。首先设计宽范围电流传感模块，在真实飞行中捕捉具有瞬态特性的动作特定电流轮廓；同时，基于退化耦合电化学模型，利用参考性能测试与元启发式算法校准高保真电池模型。通过在已校准模型中仿真实测飞行负载，系统解析不同飞行动作如何转化为锂库存损失、活性材料损失及内部副反应等退化模式。结果表明，即使两种飞行轨迹平均能耗相同，其瞬态载荷结构仍可引致不同的退化路径，凸显了兼顾效率与电池退化的运动感知电池管理的重要性。
原文摘要 · Abstract (English)
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.
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