UAV Battery Prognostics and Flight Time Estimation

dc.contributorAalto Universityen
dc.contributor.advisorSuvitie, Arto
dc.contributor.authorSood, Anshul
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.supervisorZhou, Quan
dc.description.abstractLithium-based battery systems are extensively used in the electric mobility industry. The safety, prognostics, and longevity of the batteries are assured by battery management systems. One of the features of these management systems is to accurately determine the charge in a battery which is used to estimate the remaining run time of the electric vehicle, in this case, a drone. It is currently not possible to evaluate the charge of a battery by simply measuring the external parameters such as voltage or current. This problem is also known as the state of charge estimation in scientific literature. \\In this thesis, a highly accurate battery state of charge estimation method is developed and the result from this estimation is further used to predict the remaining flight time of the drone. This is done by developing an estimation algorithm based on data-driven approach. An Artificial Neural Network takes the voltage, current, and temperature information as input to predict the State of Charge. Since this is a time-series forecasting problem, the estimation algorithm specifically utilizes a type of Neural Network called the Recurrent Neural Network. This can capture long-term dependencies and model sequential data without requiring any accurate physics-based system modeling knowledge. Using the battery charge estimation, the remaining battery charging or discharging time can be predicted based on the current consumption of the drone. The performance of the proposed model is compared to existing methods that use various variations and combinations of Recurrent Neural Networks and other types of neural networks to predict the state of charge of a lithium-based batteries. The results showed that the proposed model achieved superior accuracy for state of charge prediction in UAV batteries.en
dc.programmeAEE - Master’s Programme in Automation and Electrical Engineering (TS2013)fi
dc.programme.majorControl, Robotics and Autonomous Systemsfi
dc.subject.keyworddeep learningen
dc.subject.keywordstate of chargeen
dc.subject.keywordtime-series forecastingen
dc.titleUAV Battery Prognostics and Flight Time Estimationen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen