Performance scoring model for new energy vehicles based on Hadoop
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Zina Li
und
Shuang Zhao
Abstract
A vehicle that runs on electricity rather than conventional fossil fuels is known as an Electric Vehicle (EV). They have advantages for the environment making them more pollution-free and helping to reduce the cost. However, there are still certain obstacles to overcome, such as cost and a poor network for charging. Utilizing Hadoop helps maximize the effectiveness of charging, cut expenses, and enhance the EV experience in general. So, this paper presents the performance scoring model for new energy vehicles based on Hadoop for improving the charging efficiency of PHEVs and reducing fuel costs. By leveraging the power of Hadoop and implementing optimization algorithms, this model can determine the best charging hours for PHEVs, leading to more efficient and cost-effective charging. By utilizing Hadoop’s distributed computing framework, the model can process and analyze the data in parallel, enabling faster and more accurate. The developed Hadoop model can consider various parameters to determine the best charging hours for PHEVs. Moreover, the Enhanced Bird Swarm Optimization Algorithm (EBSO) integrated with the Hadoop model to minimize the operation cost of the Battery Swapping Stations (BSS) while EV charging. This can provide insights into the effect of PHEV fuel costs. By analyzing data on fuel consumption, electricity prices, and charging patterns; it can calculate the cost savings achieved by optimizing charging schedules. This information could be valuable for PHEV owners, helping them make informed decisions about when and how to charge their vehicles to minimize the operation cost of the BSS. With further advancements in technology, the developed model has the potential to significantly contribute to the widespread adoption of PHEVs and the transition to a more sustainable transportation system.
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Research ethics: This study did not involve human subjects, animal experiments, or other scenarios requiring ethical review, thus no ethical approval was needed.
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Informed consent: Not applicable.
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Author contributions: Zina Li is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Shuang Zhao is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
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Use of Large Language Models, AI and Machine Learning Tools: No large language models, AI, or machine learning tools were employed in this research. All tasks, such as data analysis, model construction, and manuscript drafting, were independently completed by the authors, ensuring compliance with academic integrity and research ethical standards.
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Conflict of interest: No conflict of interest to declare.
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Research funding: Authors did not receive any funding.
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Data availability: No datasets were generated or analyzed during the current study.
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