DOI: https://doi.org/10.14710/teknik.v46i1.67383

IoT and Machine Learning-Based Electric Vehicle Development Strategy to Maximize Vehicle Life and Promote Green Mobility

*Callista Fabiola Candraningtyas orcid  -  Department of Environmental Science, Faculty Mathematics and Natural Science, Universitas Sebelas Maret, Jl. Ir. Sutami, Jebres, Surakarta, Indonesia 57126, Indonesia
Fikri Arkan Maulana  -  , Indonesia
Alles Anandhita Achmad  -  , Indonesia
Open Access Copyright (c) 2025 TEKNIK

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Abstract
This research explores innovative strategies for developing electric vehicles based on the Internet of Things (IoT) and Machine Learning with the aim of maximizing service life and encouraging green mobility. In the face of the climate crisis and the increasing need for sustainable energy, electric vehicles offer a potential solution to reduce carbon emissions in the transportation sector. However, the challenges of optimizing battery life and energy efficiency require new, smarter and more connected approaches. This research integrates IoT technology with machine learning to create a more efficient electric vehicle ecosystem. This technology enables extended battery life through better usage management, increased energy efficiency through operational optimization, and predictive maintenance that reduces vehicle downtime. The research methodology includes testing prototypes of electric vehicles equipped with IoT technology, field trials to collect performance data, comprehensive analysis, and data processing to evaluate the effectiveness of the implemented strategies. The research results show that the integration of IoT and Machine Learning in electric vehicles can significantly increase battery life, energy efficiency, and make a positive contribution to green mobility. This development strategy is expected to advance electric vehicle technology in Indonesia, reduce dependence on fossil fuels, and create a cleaner and more sustainable environment.
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Keywords: Electric Vehicles; Internet of Things; Green Mobility; Machine Learning; Sustainable Transportation

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Section: Traction Energy
Language : EN
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