A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries. Issue 5 (6th January 2020) - Wang, Shunli; Fernandez, Carlos; Fan, Yongcun; Feng, Juqiang; Yu, Chunmei; Huang, Kaifeng; Xie, Wei
Abstract: The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle‐adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed‐circuit‐voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively. Abstract : The safety assurance is very important for the unmanned aerial vehicle lithium‐ion batteries, in which the state of charge estimation is the basis of its energy management. A new equivalent model is constructed for the mathematical expression of different structural characteristics, and an improved reduce particle‐adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is considered and absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current‐varying conditions, in which the hybrid pulse power characterization test is accommodated.