－Brief Paper－ ON-LINE DETECTION OF STATE-OF-CHARGE IN LEAD ACID BATTERY USING RADIAL BASIS FUNCTION NEURAL NETWORK

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 char-acteristics 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 de-graded 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