Back close

Prediction of EV arrivals at battery swapping station using hybrid neural network models

Publication Type : Conference Proceedings

Source : Journal of Physics: Conference Series

Url :

Campus : Bengaluru

School : School of Engineering

Year : 2021

Abstract : There is an enormous increase in demand for Electric Vehicles (EV) in the present era, as they are environment-friendly when compared to conventional vehicles. Battery Swapping Stations (BSS) are gaining a lot of attention from the EV sector as it is like the gasoline stations. Forecasting of EV arrivals at BSS helps in optimally scheduling the depleted batteries to different charging piles without affecting the State of Health of the battery. Back Propagation Neural Network (BPNN) is widely used in the prediction of real-time data. Training of BPNN using metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) helps to overcome the local optima problem in BPNN. Thus, in the present work forecasting on the EV arrivals is carried out using GA-BPNN and PSO-BPNN hybrid models. Finally, a comparative study is carried out among BPNN, GA-BPNN, and PSO-BPNN models using the performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). From the results, it was obtained that GA-BPNN model is preferred in forecasting the EV arrivals at BSS as the model has less overfitting. The hybrid models have been simulated in MATLAB/Simulink software.

Cite this Research Publication : Manikanta Suri, Neha Raj and K Sireesha, Prediction of EV arrivals at battery swapping station using hybrid neural network models, Journal of Physics: Conference Series, Volume 2062, 2nd International Conference on Computational Intelligence and Energy Advancements (ICCIEA 2021) .

Admissions Apply Now