Detection of State-of-Charge in Lead Acid Battery using RBF-NN
| Authors: | Morita Yoshifumi, Nagoya Institute of Technology, Japan
Sun Hee Lee, Nagoya Institute of Technology, Japan
Kozawa Takaharu, Nagoya Institute of Technology, Japan
Mizuno Naoki, Nagoya Institute of Technology, Japan |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Monitoring and Change Detection |
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| Keywords: | Neural networks, Radial base function networks, Nonlinear models, Detection systems, Automobiles, SOC(state of charge), Lead acid battery |
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Abstract
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 SOC detection method with a radial base function neural network is proposed. The detection accuracies for different sized batteries, various degradation states and driving patterns are investigated.
