Premium
On‐Line Detection Of State‐Of‐Charge In Lead Acid Battery Using Radial Basis Function Neural Network
Author(s) -
Morita Yoshifumi,
Yamamoto Sou,
Lee Sun Hee,
Mizuno Naoki
Publication year - 2006
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2006.tb00277.x
Subject(s) - battery (electricity) , state of charge , lead–acid battery , artificial neural network , line (geometry) , degradation (telecommunications) , power (physics) , signal (programming language) , radial basis function , engineering , function (biology) , computer science , control theory (sociology) , electronic engineering , artificial intelligence , mathematics , physics , geometry , control (management) , quantum mechanics , evolutionary biology , biology , programming language
To realize a stable supply of electric power in an automobile, an accurate and reliable detection method of SOC (state‐of‐charge) in a lead acid battery is required. However the dynamics of the battery is very complicated. The characteristics of the battery greatly change due to its degradation. Moreover a automobile has many driving patterns, which are unknown beforehand. Thus it is not easy to detect the SOC analytically. In this paper, to overcome this problem, a new on‐line SOC detection method with a radial basis function neural network is proposed. In order to increase the detection accuracy of degraded batteries, physical values related to the degradation degree are used as input signal in the neural network. The detection accuracies for different sized batteries and various degradation states are investigated.