Microcontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference System
Küçük Resim Yok
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE COMPUTER SOC
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
All over the world, many portable devices need battery to run. Every expert has to use efficient hardware and software documentation to make battery last longer and make a correlation between microcontrollers' duties and the remaining energy of batteries. In order to make battery last longer, battery information must be evaluated continuously. In many devices, fluctuating current is used due to its own load so alternating current makes it hard to compute the remaining battery level. For many devices, there could be battery level indicator as solution. This solution gives clue about the remaining time for user but it does not give any hint for microcontroller about battery situation. For low cost devices, it could be very difficult to estimate the remaining storage energy in the battery. In this study, microcontroller compatible sealed lead acid battery remaining energy predictor based on adaptive neural fuzzy inference system has been designed and proposed. In order to test proposed method, mean absolute error and leave one out have been used to measure proposed system performance. The obtained mean absolute error results for leave one out is 10.55, epoch error is 11.72. Through the study, low adaptive neural fuzzy inference system rules and low microcontroller memory consumption were aimed.
Açıklama
International Conference on Web Information Systems and Mining -- NOV 07-08, 2009 -- Shanghai, PEOPLES R CHINA
Anahtar Kelimeler
Artificial neural fuzzy inference system, SLA batteries, microcontroller, prediction
Kaynak
WISM: 2009 INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND MINING, PROCEEDINGS
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
