Study and Investigation of Energy Management Techniques Used in Electric/Hybrid Electric Vehicles

Ramineni, Punyavathi; Pandian, A.

doi:10.18280/jesa.540409

Cited by 1 publication

(1 citation statement)

References 37 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One prominent example of prosumers in the current energy landscape is commercial buildings equipped with electric vehicle charging points and solar photovoltaic panels. The building energy management system (BEMS) within such structures can be optimized to maximize energy efficiency, largely driven by financial incentives offered by electricity rates [3]. This implies that commercial building energy management holds substantial potential to reduce power costs, balance loads, and enhance the consumption of distributed generation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Optimization Model for Energy Consumption of New Electric Vehicles

Kotapati,

Selvamani,

Lella

et al. 2023

RIA

View full text Add to dashboard Cite

The rapid proliferation and increasing adoption of electric vehicles (EVs) have rendered them a fundamental component of intelligent transportation networks, contributing significantly to the reduction of harmful greenhouse gas emissions. The surge in the number of EVs necessitates an equally expanding infrastructure to meet their charging requirements. Accurate prediction of EV charging demand, therefore, is critical to alleviate strain on power systems and associated costs. This study presents a novel hybrid deep learning model aimed at predicting the charging needs of electric vehicles. The Convolutional Neural Network (CNN), an integral part of this model, is employed for data collection. The CNN effectively extracts local features of the data, focuses on localized information, and reduces computational demands. The Bidirectional Gated Recurrent Unit (BGRU) contributes to superior performance with time-series data due to its inherent ability to analyze such data. The Empirical Mode Decomposition (EMD) is used to decompose the input time series data while preserving their characteristics. The parameters of the BGRU prediction model are then fine-tuned using a hybrid Jarratt-Butterfly optimization algorithm (JBOA) model. The innovative EMD-CNN-BGRU predictor is evaluated using the EV charging dataset collected from the Georgia Institute of Technology in Atlanta, Georgia, USA. The simulation results achieved an impressive 98% accuracy in prediction. A comparative analysis with existing methods in the literature reveals the superior predictive metrics of the proposed deep learning neural forecaster for the dataset under consideration.

show abstract

Section: Introductionmentioning

confidence: 99%