Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach

Ramakrishna S.S. Nuvvula; Polamarasetty P. Kumar; Alighazi Siddiqui; S. Thamizharasan; Chai Ching Tan; Raaid Alubady; Baseem Khan

doi:10.1109/icSmartGrid61824.2024.10578265

Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach

Ramakrishna S.S. Nuvvula
, Polamarasetty P. Kumar
, Alighazi Siddiqui
, S. Thamizharasan
, Chai Ching Tan
, Raaid Alubady
, Baseem Khan

Electrical and Electronic Engineering Technology

NITTE
GMR Institute of Technology
Jazan University
Rajiv Gandhi College of Engineering and Technology
Nakhon Sawan Rajabhat University
Al-Ayen University
University of Babylon
Hawassa University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15% reduction in greenhouse gas emissions, substantial annual cost savings exceeding 550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.

Original language	English
Title of host publication	12th International Conference on Smart Grid, icSmartGrid 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	309-311
Number of pages	3
ISBN (Electronic)	9798350361612
DOIs	https://doi.org/10.1109/icSmartGrid61824.2024.10578265
Publication status	Published - 2024
Event	12th International Conference on Smart Grid, icSmartGrid 2024 - Hybrid, Setubal, Portugal Duration: 27 May 2024 → 29 May 2024

Publication series

Name	12th International Conference on Smart Grid, icSmartGrid 2024

Conference

Conference	12th International Conference on Smart Grid, icSmartGrid 2024
Country/Territory	Portugal
City	Hybrid, Setubal
Period	27/05/24 → 29/05/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 7 Affordable and Clean Energy
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

Electric Vehicles (EVs)
Fleet Operations
Predictive Analytics
Renewable Energy Integration
Sustainability

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Control and Optimization

Access to Document

10.1109/icSmartGrid61824.2024.10578265

Cite this

Nuvvula, R. S. S., Kumar, P. P., Siddiqui, A., Thamizharasan, S., Tan, C. C., Alubady, R., & Khan, B. (2024). Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach. In 12th International Conference on Smart Grid, icSmartGrid 2024 (pp. 309-311). (12th International Conference on Smart Grid, icSmartGrid 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icSmartGrid61824.2024.10578265

Nuvvula, Ramakrishna S.S. ; Kumar, Polamarasetty P. ; Siddiqui, Alighazi et al. / Optimizing Electric Vehicle Fleet Operations with Predictive Analytics : A Renewable Energy-Centric Approach. 12th International Conference on Smart Grid, icSmartGrid 2024. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 309-311 (12th International Conference on Smart Grid, icSmartGrid 2024).

@inproceedings{2a314e1bbbcd4d85bf1a0d0a3c60f39a,

title = "Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach",

abstract = "This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15\% reduction in greenhouse gas emissions, substantial annual cost savings exceeding 550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.",

keywords = "Electric Vehicles (EVs), Fleet Operations, Predictive Analytics, Renewable Energy Integration, Sustainability",

author = "Nuvvula, \{Ramakrishna S.S.\} and Kumar, \{Polamarasetty P.\} and Alighazi Siddiqui and S. Thamizharasan and Tan, \{Chai Ching\} and Raaid Alubady and Baseem Khan",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 12th International Conference on Smart Grid, icSmartGrid 2024 ; Conference date: 27-05-2024 Through 29-05-2024",

year = "2024",

doi = "10.1109/icSmartGrid61824.2024.10578265",

language = "English",

series = "12th International Conference on Smart Grid, icSmartGrid 2024",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "309--311",

booktitle = "12th International Conference on Smart Grid, icSmartGrid 2024",

address = "United States",

}

Nuvvula, RSS, Kumar, PP, Siddiqui, A, Thamizharasan, S, Tan, CC, Alubady, R & Khan, B 2024, Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach. in 12th International Conference on Smart Grid, icSmartGrid 2024. 12th International Conference on Smart Grid, icSmartGrid 2024, Institute of Electrical and Electronics Engineers Inc., pp. 309-311, 12th International Conference on Smart Grid, icSmartGrid 2024, Hybrid, Setubal, Portugal, 27/05/24. https://doi.org/10.1109/icSmartGrid61824.2024.10578265

Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach. / Nuvvula, Ramakrishna S.S.; Kumar, Polamarasetty P.; Siddiqui, Alighazi et al.
12th International Conference on Smart Grid, icSmartGrid 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 309-311 (12th International Conference on Smart Grid, icSmartGrid 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Optimizing Electric Vehicle Fleet Operations with Predictive Analytics

T2 - 12th International Conference on Smart Grid, icSmartGrid 2024

AU - Nuvvula, Ramakrishna S.S.

AU - Kumar, Polamarasetty P.

AU - Siddiqui, Alighazi

AU - Thamizharasan, S.

AU - Tan, Chai Ching

AU - Alubady, Raaid

AU - Khan, Baseem

PY - 2024

Y1 - 2024

N2 - This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15% reduction in greenhouse gas emissions, substantial annual cost savings exceeding 550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.

AB - This study introduces an innovative strategy to enhance electric vehicle (EV) fleet operations by combining predictive analytics and renewable energy sources. Leveraging real-world data from an active EV fleet, our method encompasses predictive maintenance, charging schedule optimization, and route planning, all empowered by advanced machine learning techniques. The results demonstrate a significant 15% reduction in greenhouse gas emissions, substantial annual cost savings exceeding 550,000, and noteworthy adaptability. This approach aligns with global sustainability targets and delivers valuable insights for fleet operators, policymakers, and researchers, promoting a more environmentally friendly and sustainable future.

KW - Electric Vehicles (EVs)

KW - Fleet Operations

KW - Predictive Analytics

KW - Renewable Energy Integration

KW - Sustainability

UR - https://www.scopus.com/pages/publications/85199454861

U2 - 10.1109/icSmartGrid61824.2024.10578265

DO - 10.1109/icSmartGrid61824.2024.10578265

M3 - Conference contribution

AN - SCOPUS:85199454861

T3 - 12th International Conference on Smart Grid, icSmartGrid 2024

SP - 309

EP - 311

BT - 12th International Conference on Smart Grid, icSmartGrid 2024

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 27 May 2024 through 29 May 2024

ER -

Nuvvula RSS, Kumar PP, Siddiqui A, Thamizharasan S, Tan CC, Alubady R et al. Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach. In 12th International Conference on Smart Grid, icSmartGrid 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 309-311. (12th International Conference on Smart Grid, icSmartGrid 2024). doi: 10.1109/icSmartGrid61824.2024.10578265

Optimizing Electric Vehicle Fleet Operations with Predictive Analytics: A Renewable Energy-Centric Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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