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- W2034291231 abstract "It is an important issue to assess the state-of-health (SOH) of battery, including aircraft. There are several methods on the assessment of SOH of battery having been reported in recent years. Herein, we proposed a hybrid PSO+SVM model, which employed particle swarm optimization (PSO) algorithm to search the best parameters of support vector machine (SVM) to estimate SOH of aviation lead-acid battery. Results showed that the proposed PSO-SVM model achieved 98.75% classification accuracy using a real dataset provided by Civil Aviation Flight University of China. Hence, the hybrid PSO+SVM model is very promising compared to the previous reports." @default.
- W2034291231 created "2016-06-24" @default.
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- W2034291231 date "2014-06-01" @default.
- W2034291231 modified "2023-09-23" @default.
- W2034291231 title "Aviation lead-acid battery state-of-health assessment using PSO-SVM technique" @default.
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- W2034291231 doi "https://doi.org/10.1109/icsess.2014.6933578" @default.
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