Citation :

Hari Prasad Bhupathi, 2023. "Deep Learning and EV Charging: Battery Life and Performance" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 1, Issue 1: 29-46.

Abstract :

From the abstract, the reader can make an understanding of the study aimed at improving the rate-capacity capability of EV batteries with the help of deep learning algorithms. While stressing the importance of EVs for less emissions of greenhouse gases and sustainable energy, it points out battery life and performance as key issues. The proposed study aims to enhance the battery management systems, using machine learning techniques employing CNN and RNN to forecast the desirable times and cycles for recharging of batteries. CNNs are useful for charging pattern analysis, and RNNs are used for time series information about charge and discharge. Deep learning models in BMS can thus improve battery health management and battery longevity thus lowering operating expenses as well as environmental footprint. Future research directions proposed in the paper are more data sources for the behavior of drivers, various machine learning strategies and comparing different charging schemes, which should enhance the improvement of EV systems. This paper also highlights the need to enhance deep learning for the improvement of EV battery efficiency and durability.

References :

[1] Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., & Nijim, M. (2021). Prediction of EV charging behavior using machine learning. Ieee Access, 9, 111576-111586.

[2] Optimizing Energy Storage: The Importance of Battery Management Systems, powerelectronicsnews, online. https://www.powerelectronicsnews.com/optimizing-energy-storage-the-importance-of-battery-management-systems/

[3] Hoke, A., Brissette, A., Smith, K., Pratt, A., & Maksimovic, D. (2014). Accounting for lithium-ion battery degradation in electric vehicle charging optimization. IEEE Journal of Emerging and Selected Topics in Power

Electronics, 2(3), 691-700.

[4] Balasingam, B., Ahmed, M., & Pattipati, K. (2020). Battery management systems—Challenges and some solutions. Energies, 13(11), 2825.

[5] Battery Health Monitor, Fleeto, online. https://fleeto.ae/modules/battery-health-monitor/

[6] The Evolution of Electric Car Batteries: Improving Range, Charging Time, and Durability, cyberswitching, online. https://cyberswitching.com/the-evolution-of-electric-car-batteries-how-advancements-in-battery-technology-are-improving-range-charging-time-and-durability/

[7] Sun, X., Li, Z., Wang, X., & Li, C. (2019). Technology development of electric vehicles: A review. Energies, 13(1), 90.

[8] Deng, Jie, Chulheung Bae, Adam Denlinger, and Theodore Miller. "Electric vehicles batteries: requirements and challenges." Joule 4, no. 3 (2020): 511-515.

[9] Szumanowski, A., & Chang, Y. (2008). Battery management system based on battery nonlinear dynamics modeling. IEEE transactions on vehicular technology, 57(3), 1425-1432.

[10] Falvo, M. C., Sbordone, D., Bayram, I. S., & Devetsikiotis, M. (2014, June). EV charging stations and modes: International standards. In 2014 international symposium on power electronics, electrical drives, automation and motion (pp. 1134-1139). IEEE.

[11] What is a Battery Management System?, synopsys, online. https://www.synopsys.com/glossary/what-is-a-battery-management-system.html

[12] Real-World Driving Scenarios for Optimal System Design, Mendeley data, online. https://data.mendeley.com/datasets/vt5rhnbspg/1

[13] Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., & Nijim, M. (2020). Machine learning approaches for EV charging behavior: A review. IEEE Access, 8, 168980-168993.

[14] Zhu, J., Yang, Z., Mourshed, M., Guo, Y., Zhou, Y., Chang, Y., ... & Feng, S. (2019). Electric vehicle charging load forecasting: A comparative study of deep learning approaches. Energies, 12(14), 2692.

[15] Online Electric Vehicle (EV) Charging Station Design Training, Advance Electrical Design, online. https://www.advanceelectricaldesign.com/Electric-Vehicle-Charging-Station-Design-Training

[16] Al-Karakchi, A. A. A., Putrus, G., & Das, R. (2017, August). Smart EV charging profiles to extend battery life. In 2017 52nd International Universities Power Engineering Conference (UPEC) (pp. 1-4). IEEE.

[17] Rutherford, M. J., & Yousefzadeh, V. (2011, March). The impact of electric vehicle battery charging on distribution transformers. In 2011 Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC) (pp. 396-400). IEEE.

[18] Kolawole, O., & Al-Anbagi, I. (2018, February). The impact of EV battery cycle life on charge-discharge optimization in a V2G environment. In 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.

[19] Gómez, J. C., & Morcos, M. M. (2003). Impact of EV battery chargers on the power quality of distribution systems. IEEE transactions on power delivery, 18(3), 975-981.

[20] Predictive Analysis Facilitated by On-Board BMS for Efficient EV Charging, powerelectronicsnews, online. https://www.powerelectronicsnews.com/predictive-analysis-facilitated-by-on-board-bms-for-efficient-ev-charging/

Keywords :

Deep Learning, Electric Vehicles (EVs), Battery Management Systems (BMS), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Battery Life, Charging Optimization.