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
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 struct...
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2020-05-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.606 |
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doaj-77f1c7044b744cafa9268f29c7738bd32020-11-25T03:05:52ZengWileyEnergy Science & Engineering2050-05052020-05-01851484150010.1002/ese3.606A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteriesShunli Wang0Carlos Fernandez1Yongcun Fan2Juqiang Feng3Chunmei Yu4Kaifeng Huang5Wei Xie6Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang ChinaRobert Gordon University Aberdeen UKRobot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang ChinaSchool of Mechanical and Electrical Engineering Huainan Normal University Huainan ChinaRobot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang ChinaSchool of Mechanical and Electrical Engineering Huainan Normal University Huainan ChinaSichuan Huatai Electric Co., Ltd. Suining ChinaAbstract 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.https://doi.org/10.1002/ese3.606compound equivalent modelinglithium ion batteriesreduce particle‐adaptive Kalman filteringstate of charge estimationunmanned aerial vehicle |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shunli Wang Carlos Fernandez Yongcun Fan Juqiang Feng Chunmei Yu Kaifeng Huang Wei Xie |
| spellingShingle |
Shunli Wang Carlos Fernandez Yongcun Fan Juqiang Feng Chunmei Yu Kaifeng Huang Wei Xie 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 Energy Science & Engineering compound equivalent modeling lithium ion batteries reduce particle‐adaptive Kalman filtering state of charge estimation unmanned aerial vehicle |
| author_facet |
Shunli Wang Carlos Fernandez Yongcun Fan Juqiang Feng Chunmei Yu Kaifeng Huang Wei Xie |
| author_sort |
Shunli Wang |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| publisher |
Wiley |
| series |
Energy Science & Engineering |
| issn |
2050-0505 |
| publishDate |
2020-05-01 |
| description |
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. |
| topic |
compound equivalent modeling lithium ion batteries reduce particle‐adaptive Kalman filtering state of charge estimation unmanned aerial vehicle |
| url |
https://doi.org/10.1002/ese3.606 |
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