The growing demand for efficient, reliable, and safe electric vehicles (EVs) has elevated the significance of advanced BMS, particularly for LFP (LiFePO₄) Battery Technologies. This paper presents a comprehensive examination of major design criteria that influence the efficiencies, reliability, and longevity of Battery Management Systems (BMS) intended for electric vehicle (EV) use. Emphasis is placed on accurately estimating the State of Charge (SOC), an essential component of proper battery health monitoring and management of energy used.Various SOC calculation methods are analyzed, The study explores how these methods respond under different operational conditions and battery aging scenarios. Additionally, Key parameters including voltage balance, thermal strategies, current limits, and state-of-health (SOH) indicators have been assessed for their contributions towards system performance. The effectiveness of the proposed SOC estimation and parameter optimization has been validated through modeling and real-time testing. The insights gained from this work will aid researchers in the development of a durable and adaptable BMS design tailor-made for LiFePO₄ batteries used in today’s electric vehicles. A potential divider-based current sensor monitors the load dynamics, while a temperature-stable OP-AMP filter design refines signal integrity. Experimental results validate system efficiency through 12 time- series plots (voltage, current, and power across PV modules), extracted from MATLAB and Excel-based post-processing. The solution is modular, programmable, cost-effective, and highly adaptable for smart-grid and off-grid solar applications.

A SOC-based charging strategy was implemented and experimentally evaluated for a photovoltaic-powered LiFePO₄ battery system operating under constrained power conditions. 2. Real-time SOC estimation using current integration enabled charging decisions to be made based on battery state rather than elapsed time alone. 3. Experimental results demonstrate controlled charging behavior near upper SOC limits, avoiding overcharge under variable solar input. 4. Measured voltage, current, and SOC profiles indicate consistent system behavior across repeated experimental runs despite measurement noise and environmental variability. 5. The proposed approach emphasizes practical implementation and constraint-based control rather than theoretical optimization.

[1] I. Miri, A. Fotouhi, and N. Ewin, “Electric vehicle energy consumption modelling and estimation—A case study,” Int. J. Energy Res., vol. 45, no. 1, pp. 501–520, Jan. 2021, doi: 10.1002/er.5700. [2] A. Sayyad, P. Ajagekar, M. Patil, R. E. Ambewadkar, and P. Kukade, “Solar Powered Electric Bicycle With Kinetic Energy Restoration System (KERS),” Int. J. Eng. Res. Appl., no. 6, pp. 27–31, 2016. [3] S. Chanagala, B. P. Eppe, N. Dudhe, and A. Akkewar, “A review on battery management system for electric vehicles and a hypothesis for an improvised battery management system,” Solid State Technol., vol. 63, no. 5, pp. 7864–7876, 2020. [4] M. Nizam, H. Maghfiroh, R. A. Rosadi, and K. D. U. Kusumaputri, “Design of battery management system (BMS) for lithium iron phosphate (LFP) battery,” in Proc. 6th Int. Conf. Electr. Veh. Technol. (ICEVT), 2019, pp. 170–174, doi: 10.1109/ICEVT48285.2019.8994002. [5] R. P. Tapaskar, P. P. Revankar, and S. V. Ganachari, “Advancements in battery management systems for electric vehicles: A MATLAB-based simulation of 4s3p lithium-ion battery packs,” World Electr. Veh. J., vol. 15, no. 6, p. 222, 2024. [6] M. Ismail and R. Ahmed, “A comprehensive review of cloud-based lithium-ion battery management systems for electric vehicle applications,” IEEE Access, 2024. [7] Z. Lyu et al., “Towards an intelligent battery management system for electric vehicle applications: Dataset considerations, algorithmic approaches, and future trends,” J. Energy Storage, vol. 101, p. 113827, 2024. [8] S. Umathe and R. Hiware, “Artificial intelligence and IoT based smart battery management system for electric vehicle,” in Proc. Int. Conf. Smart Generation Comput., Commun. Networking (SMART GENCON), IEEE, 2022, pp. 1–7. [9] S. Chanagala, R. Kant, Z. J. Khan, and N. Bhange, “A rate-capacity and recovery-effect aware battery management system for electric vehicles,” in Proc. 2nd Int. Conf. Green Energy Sustain. Dev., AICTE, 2021. [10] S. Sadagopan, S. Banerji, P. Vedula, M. Shabin, and C. Bharatiraja, “A solar power system for electric vehicles with maximum power point tracking for novel energy sharing,” in Proc. Texas Instruments India Educ. Conf. (TIIEC), 2014, pp. 124–130, doi: 10.1109/TIIEC.2014.029. [11] S. R. Chafle, U. B. Vaidya, and Z. Khan, “Design of Cuk converter with MPPT technique,” Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng., vol. 1, no. 4, pp. 161–167, 2013. [12] N. A. Zainurin, S. A. B. Anas, and R. S. S. Singh, “A review of battery charging–discharging management controller: A proposed conceptual battery storage charging–discharging centralized controller,” Eng. Technol. Appl. Sci. Res., vol. 11, no. 4, pp. 7515–7521, 2021. [13] J. Dunn, “Determining MOSFET driver needs for motor drive applications,” Texas Instruments Technical Paper, pp. 1–18, 2003. [14] A. Harrasi and A. F. Zobaa, “A cost-effective harmonic cancellation method for high-frequency silicon carbide MOSFET-based single-phase inverter,” IEEE Power Energy Technol. Syst. J., vol. 3, no. 4, pp. 128–142, 2016. [15] R. S. Hiware, S. K. Umathe, and S. Bire, “Design of sine filter for GTO-based auxiliary converter for electric locomotive using MATLAB/Simulink,” in Proc. Int. Conf. Smart Technol. Energy, Environ. Sustain. Dev., Springer, 2020, pp. 561–569. [16] R. M. Imran, Q. Li, and F. M. Flaih, “An enhanced lithium-ion battery model for estimating the state of charge and degraded capacity using an optimized extended Kalman filter,” IEEE Access, vol. 8, pp. 208322–208336, 2020. [17] J. Hou et al., “Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter,” Energy, vol. 271, p. 126998, 2023. [18] M. Yasin et al., “Improved capacity estimation method for Li-ion battery cells using a modified Kalman filter and a cell thermal model,” IEEE Trans. Instrum. Meas., 2025. [19] Y. Liu, X. Zhang, and X. Li, “Particle swarm optimization of Elman neural network applied to lithium-ion battery state of charge and health estimation,” Energy, vol. 278, p. 127897, 2023. [20] BatteryDesign.net, “Enhancing lithium-ion battery management with advanced Kalman filter tuning,” 2024. [21] W. Chen and L. Zhao, “Optimization strategies for battery management systems in electric vehicles: A survey,” Energy Reports, vol. 8, pp. 1105–1120, 2022. [22] Y. Zhang, X. Li, and J. Wang, “Modified particle swarm optimization-based powertrain energy management strategy for hybrid electric vehicles,” Energies, vol. 16, no. 13, p. 5082, 2023. [23] H. Li, Z. Wang, and Z. Chen, “Particle swarm-optimized fuzzy logic energy management strategy for a battery–ultracapacitor hybrid energy storage system in electric vehicles,” Energies, vol. 17, no. 9, p. 2163, 2024. [24] N. Rahmani and M. Mostefai, “Multi-objective MPSO/GA optimization of an autonomous PV–wind hybrid energy system,” Eng. Technol. Appl. Sci. Res., vol. 12, no. 4, pp. 8817–8824, 2022. [25] M. Ballal, Z. Khan, and R. Sonolikar, “ANN approach for the incipient faults detection in single-phase induction motor,” J. Inst. Eng. (India): Electr. Eng. Div., vol. 88, p. 10, 2008 [26] Y. Yang, X. Wang, J. Zhao, and L. He, “Optimization of the energy management system in hybrid electric vehicles considering cabin temperature,” Appl. Therm. Eng., vol. 242, p. 122504, 2024. [27] R. Hiware and S. Umathe, “Review on power system security of grid connected hybrid power system,” Solid State Technol., vol. 63, no. 5, pp. 1263–1267, 2020. [28] J. Smith and M. Lee, “A comprehensive review of battery management systems in electric vehicles,” IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 4501–4515, 2023.

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close