Battery-health-aware UAV mission planning using a cognitive battery management system

Abstract

Lithium-ion and Lithium Polymer batteries have been widely used in electric and unmanned aircraft vehicles, enabling many applications and developing a highly commercialized and demanding market. Precisely estimating the battery capacity (State of Charge (SOC)) is still a challenging problem due to many limitations. Prior work assessing battery capacity relies more on the battery’s internal physical model and less on surrounding factors, which makes the accuracy of the estimation of capacity fluctuate under different scenarios. Therefore, we present a cognitive battery management system to empower intelligence in the battery so that it can justify its current capacity and whether it will be enough for the mission and a safe landing. Our system leverages the battery temperature as the essential factor for estimating the capacity during flight. We evaluated our capacity estimation function parameters using the least squares method. Results reveal that battery temperature has a substantial impact on capacity assessment, which perfectly accomplishes the first step toward a cognitive battery management system.