Artificial Intelligence and Machine Learning in Engineering Applications
Himanshu Gupta1, Sanjeev Tayal2, Parag Jain3, Abhay Bhatia4, Lokesh Kumar5
1Dr. Himanshu Gupta, Assistant Professor, Department of Computer Science and Engineering, Roorkee Institute of Technology, Roorkee, India.
2Dr. Sanjeev Tayal, Department of Computer Applications, SD College of Management Studies, Muzaffarnagar, India.
3Dr. Parag Jain, Department of Computer Science and Engineering, Roorkee Institute of Technology, Roorkee, India.
4Dr. Abhay Bhatia, Researcher, Department of Computer Science and Engineering, Roorkee Institute of Technology, Roorkee, India.
5Dr. Lokesh Kumar, Associate Professor, Department of Computer Science and Engineering, Roorkee Institute of Technology, Roorkee, India.
Manuscript received on 22 October 2025 | First Revised Manuscript received on 28 October 2025 | Second Revised Manuscript received on 08 November 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 17-22 | Volume-12 Issue-11, November 2025 | Retrieval Number: 100.1/ijies.K113512111125 | DOI: 10.35940/ijies.K1135.12111125
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today, AI and ML are used in almost all domains, from engineering to medicine. AI is transforming the way researchers/engineers used to solve problems. AI is making processes more efficient, flexible and fast. AI needs data to make decisions. Basically, it captures the patterns in the data. Engineers today is using AI tools to address problems across almost every engineering domain, including manufacturing, urban planning, and transportation. The objective of this study is to explore AI applications across various engineering fields, with particular attention to electric vehicle (EV) charging. In this study, we used a dataset from a publicly available repository (Kaggle) in CSV format, containing data from 3,395 charging sessions by 85 EV users at 105 stations across 25 workplaces. We performed an exploratory analysis of this dataset and identified several interesting trends, including average and peak energy consumption, peak charging time, and the busiest charging stations. Some findings include that 5 kWh was consumed in most sessions, though a few drew noticeably more energy. From the analysis, it is found that on Thursdays, charging activities are more than usual, roughly around 11 a.m. This may be due to the regular office schedules. It is also observed that type 3 charging stations were used most frequently, and a large share of energy was consumed from these stations. These insights provide a practical understanding of how people charge their EVs at the workplace. By understanding this challenging pattern, organisations can schedule their charging facilities more effectively. Further organizations can make strategy to motivate their employees to charge their EV vehicles during non-peak hours.
Keywords: Artificial Intelligence (AI), Machine Learning (ML), AI Applications, Electric Vehicle (EV) Charging, AI in Engineering, Decision Making
Scope of the Article: Computer Science and Applications