Modeling of Wireless Charging Stations Based on Solar and Wind Power and Prediction of Electric Vehicle Charging Demand
The integration of electric vehicles(EVs)with new energy generation contributes to driving the transition to sustainable energy,mitigating climate change,and achieving environmentally friendly energy use and transportation.In order to completely replace traditional fuel vehicles in the coming years,the charging and range issues of EVs must be addressed.A simulation model for wireless charging of EVs based on solar and wind energy is proposed.Solar and wind energy is used to charge the batteries of wireless charging stations and EVs are wirelessly charged through the induced power between two coupled coils.Additionally,a predictive model for EV charging demand based on a hybrid deep learning algorithm is proposed.An attention-based gated recurrent unit(Att-BiGRU)framework is established based on the encoder-decoder structure to predict the charging demand of EVs at charging stations.The results indicate that new energy-based wireless charging stations can enhance the safety and comfort of EV charging,and the proposed method accurately predicts EV charging demand at different time intervals.In comparison to traditional models,the proposed improved model demonstrates faster convergence and lower error rates in charging demand prediction tasks,effectively addressing the stochasticity and volatility of EV charging demands.
Electric vehiclesWireless chargingNew energy generationDeep learningState predictionAttention-based Gated Recurrent Unit
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