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.
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Efficient battery prognostics, Electric UAVs, Li-Po battery end-of-discharge
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