Smart Battery Protection and Efficient Charging for Electric Vehicles

International Journal of Electrical and Electronics Engineering

© 2026 by SSRG - IJEEE Journal

Volume 13 Issue 3

Year of Publication : 2026

Authors : Amaraja Aniket Dhamangaonkar, Ramchandra P. Hasabe, Rishikesh More

10.14445/23488379/IJEEE-V13I3P101

How to Cite?

Amaraja Aniket Dhamangaonkar, Ramchandra P. Hasabe, Rishikesh More, "Smart Battery Protection and Efficient Charging for Electric Vehicles," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 3, pp. 1-10, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I3P101

Abstract:

In the new era of technology, Electric Vehicles (EVs) are playing an important role in sustainable transportation. However, there are some challenges in the adoption. These technologies face the problem of efficiency and safety. The proposed system gives a solution. By implementing a liquid cooling-based temperature management system, the battery system can operate within a safe operating temperature range. The autonomous charging systems use a dynamo motor to convert the vehicle's kinetic energy into electricity. A microcontroller-based system is implemented to monitor main parameters of the battery, such as cell and pack voltage, current, and temperature. A program is developed to maintain the temperature of the battery and switch between the primary and secondary batteries. For maintaining the temperature of copper tubing, liquid coolant, and a temperature sensor are used to prevent overheating, reduce the risk of hazards, and extend battery life. There are two batteries (primary & secondary) used to provide power. According to the SOC level, the charged battery is connected to the system, and the depleted battery is connected to the charger. Using autonomous charging, a dynamo motor converts a vehicle's kinetic energy to electrical energy, and it is used to charge the battery. The emergency grid charging option is included for situations when both batteries are depleted. By using a dual approach, the proposed scheme enhances safety and improves efficiency.

Keywords:

Battery Protection, Autonomous Charging, Electric Vehicles, SOC, KERS.

References:

[1] Jeevak S. Lokhande, P.M. Daigavhane, and Mithu Sarkar, “A Critical Approach towards a Smarter Battery Management System for Electric Vehicle,” 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India, pp. 232-235, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jiaxi Qiang et al., “Battery Management System for Electric Vehicle Application,” 2006 IEEE International Conference on Vehicular Electronics and Safety, Shanghai, China, pp. 134-138, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shahid A. Hasib et al., “A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management,” IEEE Access, vol. 9, pp. 86166-86193, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] V. Vaideeswaran, S. Bhuvanesh, and M. Devasena, “Battery Management Systems for Electric Vehicles using Lithium Ion Batteries,” 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] R. Ranjith Kumar et al., “Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications,” IEEE Access, vol. 11, pp. 105761-105809, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] M. Brandl et al., “Batteries and Battery Management Systems for Electric Vehicles,” 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, pp. 971-976, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Philip Dost, and Constantinos Sourkounis, “Battery Management System Realisation for Electric Vehicles,” 22nd Mediterranean Conference on Control and Automation, Palermo, Italy, pp. 704-709, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Md Ohirul Qays et al., “Recent Progress and Future Trends on the State of Charge Estimation Methods to Improve Battery-Storage Efficiency: A Review,” CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 105-114, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Manjot S. Sidhu, Deepak Ronanki, and Sheldon Williamson, “State of Charge Estimation of Lithium-Ion Batteries using Hybrid Machine Learning Technique,” IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, pp. 2732-2737, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Martin Murnane, and Adel Ghazel, “A Closer Look at State of Charge (SOC) and State of Health (SOH) Estimation Techniques for Batteries,” Analog Devices, 2023.
[Google Scholar] [Publisher Link]
[11] Yizhao Gao et al., “Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries using an Enhanced Electrochemical Model,” IEEE Transactions on Industrial Electronics, vol. 69, no. 3, pp. 2684-2696, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] J. Preetha Roselyn et al., “Optimal SoC Estimation Considering Hysteresis Effect for Effective Battery Management in Shipboard Batteries,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 5, pp. 5533-5541, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Taesic Kim et al., “A Real-Time Condition Monitoring for Lithium-Ion Batteries using a Low-Price Microcontroller,” 2017 IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, USA, pp. 5248-5253, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Syed Rahman et al., “A Study of EV BMS Cyber Security based on Neural Network SOC Prediction,” 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, USA, pp. 1-5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Amin Bavand et al., “Online Estimations of Li-Ion Battery SOC and SOH Applicable to Partial Charge/Discharge,” IEEE Transactions on Transportation Electrification, vol. 8, no. 3, pp. 3673-3685, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Noman Khan et al., “Batteries State of Health Estimation via Efficient Neural Networks with Multiple Channel Charging Profiles,” IEEE Access, vol. 9, pp. 7797-7813, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Qian Huo et al., “Bayesian Network based State-of-Health Estimation for Battery on Electric Vehicle Application and its Validation through Real-World Data,” IEEE Access, vol. 9, pp. 11328-11341, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Youngki Kim et al., “The Estimation of Temperature Distribution in Cylindrical Battery Cells under Unknown Cooling Conditions,” IEEE Transactions on Control Systems Technology, vol. 22, no. 6, pp. 2277-2286, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yi Xie et al., “A Comprehensive Study on Influence of Battery Thermal Behavior on Degradation and Consistency,” IEEE Transactions on Transportation Electrification, vol. 8, no. 3, pp. 3707-3724, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Haakon Karlsen et al., “Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions,” IEEE Access, vol. 7, pp. 142203-142213, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Yusheng Zheng et al., “Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics with Machine Learning,” IEEE Transactions on Transportation Electrification, vol. 10, no. 2, pp. 2643-2652, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Farzaneh Ebrahimi, Ryan Ahmed, and Saeid Habibi, “State of Temperature Estimation of Li-Ion Batteries using 3rd Order Smooth Variable Structure Filter,” IEEE Access, vol. 11, pp. 119078-119089, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Xinghao Du et al., “Sensorless Temperature Estimation of Lithium-Ion Battery based on Broadband Impedance Measurements,” IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 10101-10105, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] T. Allen Prasad, and Lokesh Ramesh, “Powering the Electric Cars with Dynamos,” IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), vol. 3, no. 2, pp. 01-05, 2012.
[Google Scholar]
[25] Ripfumelo Humphrey Malele, Bongumsa Mendu, and Bessie Baakanyang Monchusi, “Microcontroller-Driven Battery Management in Hybrid Energy Systems: A Systematic Review of Applications, Control Strategies, and Emerging Trends,” IEEE Access, vol. 13, pp. 29341-29360, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[26] J.S. Drusti, and S.K. Girish, “Development of Two Directional DC / DC Converter with Dual-Battery Energy Storage for Hybrid Electric Vehicle System,” International Journal of Engineering Research & Technology, vol. 11, no. 08, pp. 142-148, 2022.
[CrossRef] [Google Scholar] [Publisher Link]