AI-Driven Enhancements for Vehicle Energy Efficiency
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Keywords

AI-Driven Enhancements
Vehicle Energy Efficiency

How to Cite

[1]
D. A. Belhajjami, “AI-Driven Enhancements for Vehicle Energy Efficiency”, Journal of Bioinformatics and Artificial Intelligence, vol. 2, no. 2, pp. 34–46, Oct. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/110

Abstract

A major drive in the global automotive industry is to improve vehicle systems' energy efficiency parallel to using cleaner alternatives, as these would ensure safer, smarter, and greener transport modes. In 2017, at least 5.1 million premature deaths were reported in Europe and Central Asia, mainly because of outdoor air pollution, around 18% of which came from road transport. Additionally, aggregated new car fuel consumption and CO2 emissions are increasing every year since 2016. The increase is more pronounced in the new European Union Member States, where fuel consumption in 2020 increased by approximately 6% relative to 2019. Furthermore, energy-related CO2 emissions are expected to increase by 1.8% in 2022, reaching a record high level of 5.9 billion metric tons. These devastating statistics demonstrate the need for much faster and stronger efforts to sustainably manage the energy used in the transport sector.

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References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Singh, Jaswinder. "The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 324-366.

Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.

S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021

Tamanampudi, Venkata Mohit. "NLP-Powered ChatOps: Automating DevOps Collaboration Using Natural Language Processing for Real-Time Incident Resolution." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 530-567.

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