Battery-SOC Estimation for Hybrid-Power UAVs Using Fast-OCV Curve with Unscented Kalman Filters

Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hy...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 14; s. 6429
Hlavní autoři: He, Zhuoyao, Martín Gómez, David, de la Escalera Hueso, Arturo, Flores Peña, Pablo, Lu, Xingcai, Armingol Moreno, José María
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 15.07.2023
MDPI
Témata:
Accuracy
Batteries
battery-parameter identification
Drone aircraft
Efficiency
hybrid power
Investigations
Kalman filters
Methods
open-circuit voltage
Parameter identification
Researchers
state-of-charge estimation
unmanned aerial vehicles
unscented Kalman filter
battery-parameter identification
state-of-charge estimation
unmanned aerial vehicles
open-circuit voltage
unscented Kalman filter
hybrid power
ISSN:1424-8220, 1424-8220
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Shrnutí:Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23146429