Smart BMS
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The rapid growth of electric vehicles (EVs) has increased so the need for efficient and reliable Battery Management Systems (BMS) to ensure safe and optimal battery operation. This paper presents the design and implementation of a smart BMS for a load-carrying electric vehicle powered by a 60 V, 60 Ah Lithium Iron Phosphate (LiFePO₄) battery. The system utilizes a 32-bit microcontroller integrated with a smart BMS to monitor key battery parameters such as voltage, current, and temperature. The proposed system incorporates features such as fault detection and alert mechanisms, adaptive on-board charging, and an LCD-based display for real-time monitoring. In addition, a multi-mode access system using NFC/Wi-Fi card, remote control, and key-based operation is implemented along with an anti-theft alarm to enhance vehicle security. The EV also includes a three-level gear system and reverse operation for improved usability. The system ensures reliable performance, enhanced safety, and efficient energy utilization. The proposed smart BMS provides a practical and effective solution for modern load-carrying electric vehicle applications.
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References
T. Duraisamy and D. Kaliyaperumal, “Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System,” IEEE Access, 2021. — ML for cell balancing (BMS feature/algorithm).
Abdullah, H. M., et al., “Reinforcement Learning Based EV Charging Management Systems — A Review,” IEEE Access, 2021. — RL for smart EV charging orchestration and scheduling
Hasib, S. A., et al., “A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management,” IEEE Access, 2021. — datasets, RUL/SOH estimation and advanced BMS topics.
P. Mondal, D. Bhavsar, et al., “Estimating State-of Charge in Lithium-Ion Batteries Through Deep Learning Techniques: A Comparative Evaluation,” IEEE Access, 2024. — deep-learning SOC estimation comparisons (useful for BMS SOC module).
Ismail, M. and Ahmed, R., “A Comprehensive Review of Cloud-Based Lithium-Ion Battery Management Systems for Electric Vehicle Applications,” IEEE Access, 2024. — cloud/telemetry aspects of modern BMS.
[6] Na, S.-J., Sim, J.-U., Kim, B.-J., Kwon, D.-H., & Cho, I.-H., “Design of Bluetooth Communication-Based Wireless Battery Management System for Electric Vehicles,” IEEE Access, 2024. — wireless BMS communications (BMS ↔ VCU/UI).
“Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review,” IEEE Access, 2025. — survey of data-driven SOC/SOH methods (helps pick estimation approach).
H. M. Abdullah and S. A. Khan, “Reinforcement Learning and Optimization for EV Charging Management,” IEEE Transactions / IEEE Access (related works 2021–2023). — (practical control/charging policy literature for adaptive charging).
Selected IEEE Transactions on Industrial Electronics / Power Electronics articles (2020–2024) addressing MPPT controller design and DC–DC converter topologies for PV EV charging (search IEEE Xplore for MPPT + EV + 2020–2024). — (useful for MPPT & DC-DC design).
EEE Vehicular Technology / IEEE Transactions on Transportation Electrification papers (2020–2024) discussing vehicle-integrated photovoltaics (VIPV) and PV-assisted EV charging systems. — (system integration & experimental studies)
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