Parametric analysis and prediction of energy consumption of electric vehicles using machine learning

Nabi, Nurun; Ray, Biplob; Rashid, Fazlur; Hussam, Wisam K.; Muyeen, S. M.

doi:10.1016/j.est.2023.108226

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…This substantiated the effectiveness of our optimization approach in heightening model performance, thereby emphasizing its value as a robust technique for hyperparameter fine-tuning. Also, we compared our dataset to those used in previous studies [14][15][16][17][18][19][20][21]. Our dataset was unique in that it included data from varying traffic conditions, which is a key factor in predicting crash outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…These techniques are purpose-built to reveal hidden patterns, unravel complex structures, and unveil intricate interactions within large datasets. One of their key strengths lies in their capacity to identify non-linear effects between variables [15]. Additionally, ML techniques have the significant advantage of requiring minimal assumptions about data structures.…”

Section: Machine Learing Approaches To Vehicle Crash Predictionmentioning

confidence: 99%

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Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Almahdi,

Al Mamlook,

Bandara

et al. 2023

Sustainability

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Freeway crashes represent a significant and persistent threat to road safety, resulting in both loss of life and extensive property damage. Effectively addressing this critical issue requires a comprehensive understanding of the factors contributing to these incidents and the ability to accurately predict crash severity under different traffic conditions. This study aims to improve the accuracy of crash classification by incorporating key traffic-related variables such as braking, weather conditions, and speed. To validate the effectiveness of proposed model, we utilize real-world crash data from Flint, Michigan. To achieve the objective, we employ an innovative Boosting Ensemble Learning approach, leveraging five advanced ensemble learning models: Gradient Boosting, Cat Boost, XGBoost, LightGBM, and SGD. Through the application of hyperparameter optimization techniques, we further enhance the performance of these models, improving their predictive capabilities. Our evaluation results demonstrated the effectiveness of our approach, with Gradient Boosting algorithms achieving an accuracy rate of up to 96% in crash classification. This research provides valuable insights into the potential of using Boosting Ensemble Learning as a tool for accurately and efficiently classifying freeway crashes across a spectrum of traffic conditions. Additionally, it sheds light on the nuanced variations in crash mechanisms observed when employing diverse ensemble learning models. The findings of this study underscore the significance of hyperparameter optimization as a critical factor in elevating the predictive precision of freeway crashes.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learing Approaches To Vehicle Crash Predictionmentioning

confidence: 99%

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Almahdi,

Al Mamlook,

Bandara

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…As it can be seen from a copious amounts of research, the machine learning models have been applied to a great array of aspects related to electric vehicle battery management. Particularly, they have been used to develop models predicting the state of the charge, the state of the health and the remaining useful life of the battery, to develop enhanced charging/discharging strategies and to reduce energy consumption through efficient on/off cycles and battery balancing [10], [11]. Thousands of papers have dealt with detecting battery cell anomalies and diagnosing faults, with the help of such algorithms.…”

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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“…The AIs and MLs aligned with the IOTs and DLs seem to be particularly important for their potential to estimate the level of waste, route optimization, traffic monitoring, and reduction of the efforts required to monitor the state of the roads. The major force about the use of the AI-driven driving systems is their chance to reduce the number of traffic-related accidents and traffic [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Integrating IoT and Artificial intelligent for Enhanced Electric Vehicle Charging and Autonomous Driving for sustainable transportation

Y.Madhu Sudhana Reddy

2024

jes

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This research explores the confluence of Internet of things and artificial intelligence towards fostering electric vehicle charging and autonomous driving for sustainable transportation. By meticulously collecting and preprocessing, and executing multiple machine learning models such as Artificial Neural Network , Decision Trees , Naive Bayes , and Random Forest , the research delves into charging infrastructure and easy autonomous driving. Particularly, real-time datasets, such as EV station data, road geography and mapping data, weather and telematics data, were properly collected and processed . Then, the preprocessing stage, which included data cleaning, normalization, and feature engineering had improved the datasets’ predictability. Consequently, the datasets such as charging recommendation, predicting energy use, predicting of optimal route, had produced highly accurate training and testing for forecasting elelctric vehicles’ demands. The ML models indicated their proficiency in predicting not only the optimal charging schedules, but also predictive energy consumption, and predictive routes. Specifically, ANN with 0.945 held the highest precision, while DT 0.912, NB 0.887, and RF 0.819 were almost identical . Likewise, the recall scores, F1 scores, and AUC ROC scores also coincided the prized outcome of the models. The findings of the research are groundbreaking for both transportation sector, policymakers, as well as urban planners and stakeholders at large. Following the data-fed recommendations would not be excessively challenging, and they would yield favourable results whether the stakeholders are dealing with EV charging infrastructure, or dealing with traffic congestions, or are establishing their own sustainable solution for future mobility via not just electric vehicles but also other tools and devices. Hence, we are hopeful that our research serves as a lighthouse for the transportation domain towards a greener future.

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Parametric analysis and prediction of energy consumption of electric vehicles using machine learning

Cited by 8 publications

References 25 publications

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

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

Integrating IoT and Artificial intelligent for Enhanced Electric Vehicle Charging and Autonomous Driving for sustainable transportation

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