Affiliations: [a] Intelligent Systems Lab (LSI), Department of Intelligent System and Automation, Universidad Carlos III de Madrid, Madrid, Spain | [b] Key Lab. for Power Machinery and Engineering of M. O. E., Shanghai Jiao Tong University, Shanghai, China | [c] Drone-Hopper Company, Madrid, Spain
Correspondence:
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Corresponding authors: Zhuoyao He, David Martín Gómez, Intelligent Systems Lab (LSI), Department of Intelligent System and Automation, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain. E-mail: [email protected].
Abstract: Unmanned aerial vehicles (UAVs) nowadays are getting soaring importance in many aspects like agricultural and military fields. A hybrid power system is a promising solution toward high energy density and power density demands for UAVs as it integrates power sources like internal combustion engine (ICE), fuel cell (FC) and lowcapacity lithium-polymer (LIPO) batteries. For robust energy management, accurate state-of-charge (SOC) estimation is indispensable, which necessitates open circuit voltage (OCV) determination and parameter identification of battery. The presented research demonstrates the feasibility of carrying out incremental OCV test and even dynamic stress test (DST) by making use of the hybrid powered UAV system itself. Based on battery relaxation terminal voltage as well as current wave excitation, novel methods for OCV determination and parameter identification are proposed. Results of SOC estimation against DST through adaptive unscented Kalman filter (AUKF) algorithm show that parameters and OCV identified with longer relaxation time don’t yield better SOC estimation accuracy. Besides, it also discloses that OCV played the vital role in affecting SOC estimation accuracy. A detailed analysis is presented showing that mean discharging rate and current wave amplitude are the major factors which affect the quality of OCV identified related to SOC estimation accuracy.
Keywords: Hybrid power system, state of charge, unmanned aerial vehicle, open circuit voltage, parameter identification
