Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. (February 2019)

• Record Type: Journal Article
• Authors: Sierra, G.; Orchard, M.; Goebel, K.; Kulkarni, C.
• Is Part Of: Reliability engineering & system safety. Volume 182(2019)
• Page Start: 166
• Page End: 178
• Publication Date: 2019-02
• Subjects: Multirotor unmanned aerial vehicles -- Li–Po battery end-of-discharge -- Bayesian parameter estimation -- Model-based prognostics -- Efficient on-board prognostics; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.04.030
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract : Hightlights: Efficient approach to accurately predict the End of Discharge in Li–Po batteries. A simplified battery model that allows improving efficiency in prognostics. A practical aerodynamic-based model for the power consumption in multirotors. A novel correction loop that diminishes bias in recursive Bayesian estimation. Abstract: This article presents a holistic framework for the design, implementation and experimental validation of Battery Management Systems (BMS) in rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multirotors by using a model-based prognosis architecture that is efficient and feasible to implement in low-cost hardware. The proposed framework includes a simplified battery model that incorporates the electric load dependence, temperature dependence and SOC dependence by using the concept of Artificial Evolution to estimate some of its parameters, along with a novel Outer Feedback Correction Loop (OFCL) during the estimation stage which adjusts the variance of the process noise to diminish bias in Bayesian state estimation and helps to compensate problems associated with incorrect initial conditions in a non-observable dynamic system. Also, it provides an aerodynamic-based characterization of future power consumption profiles. A quadrotor has been used as validation platform. The results of this work will allow making decisions about the flight plan and having enough confidence in those decisions so that the mission objectives can be optimally achieved.