Integrated design and YOLO based control framework for autonomous EV charging robot platforms Article Swipe
V. C. Mahaadevan
,
R Narayanamoorthi
,
ShanmugamPillai Pushparaj Logeshwer
,
Harshit Jain
,
Sayantan Panda
,
Petr Moldřík
,
Tomáš Novák
,
Radomír Goňo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.105438
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.105438
The increase in demand for convenient and efficient charging solutions has experienced a significant upsurge due to the rapid adoption of electric vehicles (EVs). Consequently, the primary objective of this manuscript is to offer a comprehensive analysis of the development, execution, and assessment of an independent mechanical appendage specifically tailored for charging EVs. The demand for efficient and practical charging solutions is increasing as the demand for electric vehicle (EV) adoption increases. This investigation centers on the conception and engineering of a robotic arm mechanism that has been individually tailored to integrate with EVs. This robotic arm's design aimed to have autonomous capabilities, which include exact automobile identification, plug-in procedures, and real-time battery monitoring, ensure the charging process's security and efficacy. This study aims to give a complete examination of the modelling and simulation development of arm and evaluation of YOLOv8 based charging port detection exclusively for EV charging applications. The proposed model is capable of classifying charging ports precisely. The CCS1 metrics are: 100% recall, 0.962 mAP50; Type 1 – 100 % recall, 0.993 mAP50; CHAdeMO – 82.6% recall, mAP50; GB-T – 92.3% recall, mAP50; Tesla – 100% recall, mAP50; Type 2 ports – 94.1% recall, mAP50. The research comprises the design of and engineering of a robotic arm system that is optimized to interface with EVs intuitively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2025.105438
- OA Status
- gold
- Cited By
- 2
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410946704
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410946704Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.rineng.2025.105438Digital Object Identifier
- Title
-
Integrated design and YOLO based control framework for autonomous EV charging robot platformsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-01Full publication date if available
- Authors
-
V. C. Mahaadevan, R Narayanamoorthi, ShanmugamPillai Pushparaj Logeshwer, Harshit Jain, Sayantan Panda, Petr Moldřík, Tomáš Novák, Radomír GoňoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.rineng.2025.105438Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.rineng.2025.105438Direct OA link when available
- Concepts
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Robot, Control (management), Autonomous robot, Computer science, Automotive engineering, Mobile robot, Human–computer interaction, Systems engineering, Control engineering, Engineering, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.exact | 105 |
| abstract_inverted_index.model | 152 |
| abstract_inverted_index.offer | 33 |
| abstract_inverted_index.ports | 158, 193 |
| abstract_inverted_index.rapid | 18 |
| abstract_inverted_index.study | 122 |
| abstract_inverted_index.which | 103 |
| abstract_inverted_index.(EVs). | 23 |
| abstract_inverted_index.YOLOv8 | 140 |
| abstract_inverted_index.demand | 3, 54, 65 |
| abstract_inverted_index.design | 97, 202 |
| abstract_inverted_index.ensure | 114 |
| abstract_inverted_index.mAP50. | 197 |
| abstract_inverted_index.mAP50; | 167, 175, 180, 185, 190 |
| abstract_inverted_index.system | 210 |
| abstract_inverted_index.CHAdeMO | 176 |
| abstract_inverted_index.battery | 112 |
| abstract_inverted_index.capable | 154 |
| abstract_inverted_index.centers | 74 |
| abstract_inverted_index.include | 104 |
| abstract_inverted_index.metrics | 162 |
| abstract_inverted_index.plug-in | 108 |
| abstract_inverted_index.primary | 26 |
| abstract_inverted_index.recall, | 165, 173, 179, 184, 189, 196 |
| abstract_inverted_index.robotic | 82, 95, 208 |
| abstract_inverted_index.upsurge | 14 |
| abstract_inverted_index.vehicle | 68 |
| abstract_inverted_index.adoption | 19, 70 |
| abstract_inverted_index.analysis | 36 |
| abstract_inverted_index.charging | 8, 51, 59, 116, 142, 148, 157 |
| abstract_inverted_index.complete | 127 |
| abstract_inverted_index.electric | 21, 67 |
| abstract_inverted_index.increase | 1 |
| abstract_inverted_index.proposed | 151 |
| abstract_inverted_index.research | 199 |
| abstract_inverted_index.security | 118 |
| abstract_inverted_index.tailored | 49, 89 |
| abstract_inverted_index.vehicles | 22 |
| abstract_inverted_index.appendage | 47 |
| abstract_inverted_index.comprises | 200 |
| abstract_inverted_index.detection | 144 |
| abstract_inverted_index.efficacy. | 120 |
| abstract_inverted_index.efficient | 7, 56 |
| abstract_inverted_index.integrate | 91 |
| abstract_inverted_index.interface | 215 |
| abstract_inverted_index.mechanism | 84 |
| abstract_inverted_index.modelling | 131 |
| abstract_inverted_index.objective | 27 |
| abstract_inverted_index.optimized | 213 |
| abstract_inverted_index.practical | 58 |
| abstract_inverted_index.process's | 117 |
| abstract_inverted_index.real-time | 111 |
| abstract_inverted_index.solutions | 9, 60 |
| abstract_inverted_index.assessment | 42 |
| abstract_inverted_index.automobile | 106 |
| abstract_inverted_index.autonomous | 101 |
| abstract_inverted_index.conception | 77 |
| abstract_inverted_index.convenient | 5 |
| abstract_inverted_index.evaluation | 138 |
| abstract_inverted_index.execution, | 40 |
| abstract_inverted_index.increases. | 71 |
| abstract_inverted_index.increasing | 62 |
| abstract_inverted_index.manuscript | 30 |
| abstract_inverted_index.mechanical | 46 |
| abstract_inverted_index.precisely. | 159 |
| abstract_inverted_index.simulation | 133 |
| abstract_inverted_index.classifying | 156 |
| abstract_inverted_index.development | 134 |
| abstract_inverted_index.engineering | 79, 205 |
| abstract_inverted_index.examination | 128 |
| abstract_inverted_index.exclusively | 145 |
| abstract_inverted_index.experienced | 11 |
| abstract_inverted_index.independent | 45 |
| abstract_inverted_index.monitoring, | 113 |
| abstract_inverted_index.procedures, | 109 |
| abstract_inverted_index.significant | 13 |
| abstract_inverted_index.development, | 39 |
| abstract_inverted_index.individually | 88 |
| abstract_inverted_index.intuitively. | 218 |
| abstract_inverted_index.specifically | 48 |
| abstract_inverted_index.Consequently, | 24 |
| abstract_inverted_index.applications. | 149 |
| abstract_inverted_index.capabilities, | 102 |
| abstract_inverted_index.comprehensive | 35 |
| abstract_inverted_index.investigation | 73 |
| abstract_inverted_index.identification, | 107 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.96840879 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |