Modeling and adaptive control for supercapacitor in automotive applications based on artificial neural networks

• Comprehensive approach to modeling supercapacitors used in hybrid vehicles using ANN.
• Discover supercapacitor module structure with hierarchical multilevel approach.
• The model was applied to control the supercapacitor voltage.
• Accuracy of the neural model and controller are confirmed with validation tests.
• Accurate tools for SC monitoring and control suitable in energy management systems.

The behavior of a supercapacitor is a complex and nonlinear function of its current rate, temperature, chemistry and history, and hence cannot easily be determined. In this study, we use a one-layer feed-forward artificial neural network (ANN), trained using the back-propagation algorithm, to model the behavior of supercapacitors used in automotive applications. Possible improvements of the neural network model using a multilevel approach are discussed. Then, on the basis of this model, a neural controller is developed in order to control the supercapacitor voltage. Simulation results confirmed the accuracy of the model compared to measurements from supercapacitor module power-cycling.