Hybrid Battery Model for Prognostics in Small-size Electric UAVs
Hybrid Battery Model for Prognostics in Small-size Electric UAVs
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DOI：
10.36001/phmconf.2018.v10i1.454
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发表时间：
2018-09
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影响因子：
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通讯作者：
Gina Katherine Sierra Paez;M. Orchard;Chetan S. Kulkarni;K. Goebel
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文献类型：
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作者：
Gina Katherine Sierra Paez;M. Orchard;Chetan S. Kulkarni;K. Goebel
电动无人机（UAV）经历与电池老化和滥用效应相关的问题和风险。因此，电池健康管理（BHM）系统是必要的，使电池成为安全，可靠和具有成本效益的解决方案。BHM系统对于确保使命目标的实现和辅助在线决策活动（如故障缓解和使命重新规划）是必不可少的。为了完成这些任务，我们采用了基于模型的电池供电的无人机，其中电池模型被用作两个连续的任务，（i）充电状态（SOC）估计，和（ii）放电结束（EOD）预测的基础上的性能体系结构。小型飞机通常受到重量、尺寸和成本的限制。因此，需要准确地（i）估计SOC，以及（ii）预测可以在受限环境中操作的小型UAV中的Li-Po电池的EOD时间。这项工作提出了一个修改的电化学为基础的电池模型，允许减少计算资源，而不会失去预测结果的准确性。由此产生的混合动力电池模型进行了验证，并适用于预测的EOD时间在放电周期的锂-Po电池的小尺寸四轴飞行器，执行交付任务。使用所提出的混合电池模型的预测结果被证明是非常准确的，而其估计和预测处理时间显着低于使用基于电化学的电池模型的处理时间。
Electric Unmanned Aerial Vehicles (UAVs) experience problems and risks associated with battery aging and abuse effects. Therefore, a Battery Health Management (BHM) system is necessary to make the battery a safe, reliable, and cost-efficient solution. BHM systems are essential to ensure that the mission goal(s) can be achieved and to aid in online decision-making activities such as fault mitigation and mission replanning. To accomplish these tasks, we have adopted a model-based prognostics architecture for battery-powered UAVs where a battery model is used as the basis of two sequential task, (i) the State of Charge (SOC) estimation, and (ii) the End of Discharge (EOD) prediction. Small-size aircraft usually have weight, size and cost constraints. Therefore, there is a need to accurately (i) estimate the SOC, and (ii) predict the EOD time of Li-Po batteries in small-size UAVs that can operate in constrained environments. This work proposes a modification to an electrochemistry-based battery model that allows reducing computational resources without losing accuracy in prognostic results. The resulting hybrid battery model is validated and applied to prognostic of the EOD time in discharge cycles of a Li-Po battery of a small size quadcopter that performs delivery missions. Prediction results using the proposed hybrid battery model are shown to be very accurate while its estimation and prediction processing times are significantly lower than processing times using the electrochemistry-based battery model.