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 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.

Automated summary

Unmanned aerial vehicles (UAVs) face limitations in flight endurance due to the limited energy density of their batteries. This paper proposes a novel method, fast open circuit voltage (OCV), for obtaining battery OCVs, which offers advantages in simplicity, duration, and cost over traditional methods. Additionally, a batch mode is proposed for data sampling and battery-parameter identification, which effectively reduces noise compared to the single mode approach.