On-Line Detection of State-of-Charge in Lead Acid Battery Using Both Neural Network and On-Line Identi cation

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 using both neural network and on-line identification is proposed. In order to increase the detection accuracy of degraded batteries, the on-line identified parameters based on the simple battery model are used as input signal in the neural network. The detection accuracies for different sized batteries and various degradation states are investigated