Neural network based novel controller for hybrid energy storage system for electric vehicles

Sagar Sharma, Shakuntla Boora


This manuscript deals with the various control strategies of storage system for an electrical vehicle. High demands in the electrical systems in the field of transportations leads to various challenges and more precise control and regulations techniques. Apart from the conventional grid system now a days the integration of renewable energy systems like solar, wind and fuel cell system leads to more complex system but these system shares the load from conventional generating system. This paper deals with the study and control aspects of the electrical vehicles associated with hybrid energy storage (HES) systems. In general, when systems are integrated with the main grid there are more distortions and ripples in the system. To reduce these distortions various control techniques are used. This paper proposes a neural network-based PI (NNPI) controller for HES system for electric vehicles for better distortion less outputs.


Battery charger; Electric vehicles; Full bridge IGBT converter; Magnetic circuit; Neural network; Three phase bridge rectifiers

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