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Rasa-Powered Conversational AI Framework for Intelligent Electric Vehicle Trip Planning and Energy Management

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 13 No. 4 (2025) · hlm. 911–922

DOI
10.52549/ijeei.v13i4.6553
ISSN
2089-3272, 2089-3272
Terbit
2025-12-01
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

The rapid growth of Electric Vehicles (EVs) calls for intelligent solutions to optimize grid stability and enhance user experience. This paper proposes a geo-optimized, user-centric EV management system integrating open-source geospatial tools with Rasa-based Natural Language Understanding (NLU). Through an interactive conversational interface, EV owners receive real-time trip planning recommendations based on parameters such as State of Charge (SoC), charging point availability, and route efficiency. The system utilizes APIs for real-time geolocation, charging station data, and Battery Management System (BMS) insights to determine optimal charging locations, durations, and trip costs. Extensive testing demonstrates improved energy management and route planning efficiency, highlighting the system’s potential for smart and sustainable EV infrastructure development.

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Rekan penulisOrganisasiArtikel bersama
Malligunta Kiran KumarKoneru Lakshmaiah Education Foundation3
G. Anjali DeviAfiliasi tidak diketahui2
Swapna GanapaneniAfiliasi tidak diketahui2
L. SirisaiahAfiliasi tidak diketahui2
Lokesh KothaAfiliasi tidak diketahui2
Subhash ManchikantiAfiliasi tidak diketahui2
Venu YarlagaddaAfiliasi tidak diketahui1
N. KavithaAfiliasi tidak diketahui1
Chava Sunil KumarAfiliasi tidak diketahui1
G. NaveenAfiliasi tidak diketahui1
Swetha MareddyAfiliasi tidak diketahui1
S. Venkata Rami ReddyAfiliasi tidak diketahui1
B. JyothiKoneru Lakshmaiah Education Foundation1
N. Sumana KeerthiAfiliasi tidak diketahui1
M. SharanyaAfiliasi tidak diketahui1
CH. SrinivasAfiliasi tidak diketahui1
K. ShravaniAfiliasi tidak diketahui1
V. Sanjeeva RaoAfiliasi tidak diketahui1
Teerdala RakeshAfiliasi tidak diketahui1
Nittala RamchandraAfiliasi tidak diketahui1
Devarakonda MahidharAfiliasi tidak diketahui1
Burthi Loveswara RaoAfiliasi tidak diketahui1
C. H. Rami ReddyAfiliasi tidak diketahui1
Rakesh TeerdalaAfiliasi tidak diketahui1
Kambhampati Venkata Govardhan RaoAfiliasi tidak diketahui1
T. Vijay MuniAfiliasi tidak diketahui1
CH. Venkata AmarnadhAfiliasi tidak diketahui1
T. Anuradha DeviAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Rasa-Powered Conversational AI Framework for Intelligent Electric Vehicle Trip Planning and Energy Management</dc:title>
	<dc:creator xml:lang="en">T, Phanendra</dc:creator>
	<dc:creator xml:lang="en">G, Swapna</dc:creator>
	<dc:creator xml:lang="en">M, Vishnuvardhan</dc:creator>
	<dc:creator xml:lang="en">Rao, K V Govardhan</dc:creator>
	<dc:creator xml:lang="en">Rakesh, T</dc:creator>
	<dc:creator xml:lang="en">Kumar, M Kiran</dc:creator>
	<dc:subject xml:lang="en">Battery Management System</dc:subject>
	<dc:subject xml:lang="en">Electric Vehicle (EV)</dc:subject>
	<dc:subject xml:lang="en">Geo-Optimization</dc:subject>
	<dc:subject xml:lang="en">Grid Stability</dc:subject>
	<dc:subject xml:lang="en">Rasa NLU</dc:subject>
	<dc:subject xml:lang="en">State of Charge (SOC)</dc:subject>
	<dc:description xml:lang="en">The rapid growth of Electric Vehicles (EVs) calls for intelligent solutions to optimize grid stability and enhance user experience. This paper proposes a geo-optimized, user-centric EV management system integrating open-source geospatial tools with Rasa-based Natural Language Understanding (NLU). Through an interactive conversational interface, EV owners receive real-time trip planning recommendations based on parameters such as State of Charge (SoC), charging point availability, and route efficiency. The system utilizes APIs for real-time geolocation, charging station data, and Battery Management System (BMS) insights to determine optimal charging locations, durations, and trip costs. Extensive testing demonstrates improved energy management and route planning efficiency, highlighting the system’s potential for smart and sustainable EV infrastructure development.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2025-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/6553</dc:identifier>
	<dc:identifier>10.52549/ijeei.v13i4.6553</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 13 No. 4: December 2025; 911-922</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/6553/1039</dc:relation>
</oai_dc:dc>