Creating electric vehicle battery management with IoT: Using intelligent algorithms to enhance safety, efficiency, and charging time
| dc.contributor.author | Balapriya, S. | |
| dc.contributor.author | Pandi, V. Samuthira | |
| dc.contributor.author | Sabitha, M. | |
| dc.contributor.author | Veena, K. | |
| dc.contributor.author | Balasubramaniyan, R. | |
| dc.contributor.author | Ali, Guma | |
| dc.date.accessioned | 2025-12-19T12:17:07Z | |
| dc.date.available | 2025-12-19T12:17:07Z | |
| dc.date.issued | 2025-10-28 | |
| dc.description | This paper proposes a hybrid deep learning model for crop disease detection using drone imagery and IoT sensors, enabling early diagnosis and precision agriculture. The system integrates convolutional neural networks with edge computing for real-time analysis, reducing crop losses and improving food security. It directly supports SDG 2 (Zero Hunger) and SDG 9 (Industry, Innovation, and Infrastructure) by promoting sustainable agricultural practices and technological innovation. The research aligns with Uganda’s National Development Plan IV aspirations for agricultural modernization, value-chain efficiency, and rural income growth, fostering economic transformation through smart farming and digital solutions. | |
| dc.description.abstract | The expanding electric vehicles (EV) market has raised the demand for better-optimized intelligent battery management systems (BMS) that can improve safety, performance, and charging time. Older BMS representative solutions employ earlier monitoring and control techniques, often without real-time adaptability and predictive capabilities. This paper investigated Internet of Things (IoT)-specialized and smart algorithms-integrated EV battery management to increase efficiency, safety, and a superior charging process. The proposed architecture uses machine learning model, data analytics, and IoT-enabled sensors to enable real-time monitoring of critical battery parameters such as state of charge (SoC), state of health (SoH), temperature, and voltage variations. Predictive analytics enable the early detection of potential battery degradation, minimize thermal runaway, and enhance energy distribution among individual battery cells. Furthermore, advanced charging algorithms optimize charging rates in response to instantaneous battery states and grid demand, maximizing charging speed while avoiding overcharging and wearout. Cloud-hosted IoT platforms enable remote monitoring and data-based decision-making, improving user experience and prolonging battery life. We develop a prototype implementation to showcase the effectiveness of the system, which results in efficient energy management, early faults detection, and reduced charging cycles. Application of the proposed system was compared against the state-of-the-art BMS and used as a reference standard and the comparison results revealed excellent performance in terms of safety, compactness and flexibility of the system with the existing BMS. The study utilizing IoT as well as artificial intelligence helps to further emerge smart electric vehicle technology that leads human being for sustainable and rich electric mobility solution. | |
| dc.identifier.citation | Balapriya, S., Pandi, V. S., Sabitha, M., Veena, K., Balasubramaniyan, R., & Ali, G. (2025). Creating electric vehicle battery management with IoT: Using intelligent algorithms to enhance safety, efficiency, and charging time. In 2025 International Conference on Smart & Sustainable Technology (INCSST), 1–6. https://doi.org/10.1109/INCSST64791.2025.11210276 | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/848 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.subject | Temperature sensors | |
| dc.subject | Fault detection | |
| dc.subject | Battery management systems | |
| dc.subject | Prediction algorithms | |
| dc.subject | Real-time systems | |
| dc.subject | Batteries | |
| dc.subject | Safety | |
| dc.subject | Thermal analysis | |
| dc.subject | State of charge | |
| dc.subject | Predictive analytics | |
| dc.title | Creating electric vehicle battery management with IoT: Using intelligent algorithms to enhance safety, efficiency, and charging time | |
| dc.type | Other |