• Ishrat Javid
• Vikas

In this paper, we use a neural network mapping function to fit in the range of reference and actual output, to improve efficiency by reducing the harmonics in the outputs. The system basically describes the hybrid energy storage system for lithium ion batteries and super capacitors. The application is for DC electric vehicle. So it is required to maintain a constant voltage without any flickers in the output of the DC voltage and currents. another hybrid energy storage system for electric vehicles is structured dependent on a Li-ions battery power dynamic constraint rule-based HESS energy the executives and another bi-directional DC/DC converter. The system is contrasted with conventional hybrid energy storage system, demonstrating it has huge preferred standpoint of diminished volume and weight. Besides, the swell of yield current is decreased and the life of battery is improved. a HESS dimensioning procedure that goes for getting a most good hybridization rate, thinking about the control methodology and the driving cycle for a picked HESS topology. So this approach for neural network mapping proves to beneficial for electric vehicle topology used as hybrid electric energy storage system.

HESS, super capacitor, ultra-capacitor, electric vehicle

"Neural Network Mapping based Control System in Hybrid Energy Storage System for Electric Vehicles", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.583-587, May-2019, Available :http://www.jetir.org/papers/JETIR1905G85.pdf

"Neural Network Mapping based Control System in Hybrid Energy Storage System for Electric Vehicles", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp583-587, May-2019, Available at : http://www.jetir.org/papers/JETIR1905G85.pdf