Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

ElAzab, Heba-Allah I.; Swief, R.A.; El-Amary, Noha H.; Temraz, H.K.

doi:10.1016/j.egyai.2023.100285

Cited by 9 publications

(4 citation statements)

References 44 publications

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“…To forecast the load, the prevailing approaches like GRU and genetic algorithm achieved 88.60% accuracy and 0.0028 RMSE, correspondingly. Likewise, the approaches of [26,28], including Multinomial Logistic Regression (MLR)-Support Vector Machines (SVM) and GRU-LSTM achieved an accuracy of 94.87% and 98.04%, respectively. The prediction accuracy is enhanced for the JFL-BiLSTM approach than the other existing techniques since the proposed model handles the seasonal impact of load demand and Bidirectional processing.…”

Section: Performance Evaluationmentioning

confidence: 99%

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

Rajender,

Dubey,

Kumar

2024

Preprint

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The surge in Electric Vehicle (EV) adoption has intensified energy demand, prompting the integration of Solar Photovoltaic (S-PV) systems for optimization. To address the challenge of fluctuating load demand with seasonal changes, a novel approach is proposed. This system combines Laguerre Polynomial-based Ramp Rate Method (LP-RRM) to smooth DC power from S-PV, Generalized Space Vector Modulation-based Switching Regulators (GSVM-SR) to tackle grid interruptions, and Rosenbrock Function-based Sea-Horse Optimization (RF-SHO) for optimal Charging System (CS) selection. Pre-processing the dataset identifies seasons and extracts features, which are fed into Joint Fusion Layer – Bidirectional Long Short Term Memory (JFL-BiLSTM) for load demand forecasting. The chosen CS is determined from a constructed graph based on user requests. The proposed technique effectively mitigates blackout risks and facilitates efficient EV charging operations, yielding promising results according to simulation outcomes.

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Section: Performance Evaluationmentioning

confidence: 99%

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

Rajender,

Dubey,

Kumar

2024

Preprint

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“…In my project, too, machine learning models, such as ANN, DT and naive Bayes, will be applied to calculate optimal charging and discharging strategies, situations in which the performance criteria will be incureable and situations on when and how to re-balance batteries. They will also be applied to detecting abnormal battery conditions [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Machine learning Approach to Battery Management and Balancing Techniques for Enhancing Electric Vehicle Battery Performance

Basavaraju Bennehalli

2024

jes

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The objective of this research is to apply machine learning techniques to optimize electric vehicle battery management and balance to attain maximum battery performance. Here, we will assess and compare the efficiency and accuracy of decision tree classifier, ANN, and Naive Bayes classifiers in: predicting two optimal charging and discharging battery management strategies, prediction of the battery’s performance and detecting any abnormalities in it. . In this context, one machine learning model will be proposed to have the most advantageous performance. The experimental results details that such findings are attained, and the precision rates, recall rates, and F1-scores attained by net models exceed 98%. Additionally, the decision tree, as well as the Naive Bayes classifier, have impressive performance, and their accuracy rates exceed 90%. Decision trees along with Naive Bayes classifiers have also important impacts on the identification of classification of battery’s responses through probabilistic classification in the former. Our findings have important implications for ensuring electric vehicle batteries are managed more properly for optimal performance. Additionally, based on the results obtained, they can be utilized to help relevant stakeholders in the EV industry and other industries with battery usage implement practical measures that optimize battery performance, increase battery life, and reduce the incurred operational expenses of electric vehicle fleets.

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“…Weather conditions are significant aspects that must be included in EV charging load predictions, as they might effect EV users' travel behavior. In [14], a variety of characteristic machine learning algorithms and deep learning algorithms were applied to forecast the charging load distribution of EVs. This study used real data from the Spanish power system and accounted for the impact of the seasonal climate on EV trips.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

Zhang,

Lu,

Kuang

et al. 2024

WEVJ

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This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.

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Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

Cited by 9 publications

References 44 publications

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

Machine learning Approach to Battery Management and Balancing Techniques for Enhancing Electric Vehicle Battery Performance

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

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