Design and Development of a Test Bench for LiPo Batteries for Residual Charge Capacity Prediction

As the use of Unmanned Aerial Vehicles (UAVs), especially quadcopters, continues to surge, the performance of lithium-ion batteries becomes increasingly significant. These batteries not only influence the operational autonomy of UAVs but also determine the overall success and longevity of aerial missions. Motivated by the pressing need to accurately evaluate and predict battery behavior over its lifecycle, this thesis starts on the development of a test bench. This bench is specifically designed to develop a predictive model for LiPo batteries, shining a light on their residual charge capacity. The study methodically outlines each step, from the initial design of the test bench to the selection of the right components, and finally, the software implementation. The rigor applied in this phase was indispensable, establishing foundational benchmarks for the study and ensuring that every data collection step was attuned to precision and reliability. Post data collection, the study focused on developing a modelcapable at estimating the future energy capacity of these batteries. Results from this endeavor were revealing curves derived from experimental tests signaled a decline in performance as C-rate increased. Furthermore, an observed performance gap of 5 to 10% in capacity between two tested batteries, with larger discrepancies linked to higher C-rates, underscored the behavior of these energy components. Through the developed predictive model, the research could pinpoint the battery's lifespan.