DATABASE-DRIVEN ENERGY MANAGEMENT IN ELECTRIC VEHICLES

Year : 2025 | Volume : 12 | Issue : 03 | Page : 19 24
    By

    PRIYA SHUKLA,

  • MOHIT SHUKLA,

  1. Assistant Professor, Department of Computer Science and Engineering, Echelon Institute of Technology, Faridabad, Haryana, India
  2. M Tech, Micro Electronics BITS Pilani Product Development Leader Ericssoon Global India Pvt Limited, Gurugram, Haryana, India

Abstract

With the growing concern over environmental pollution, there is an increasing demand for sustainable and eco-friendly technologies. Among these, electric vehicles (EVs) have emerged as a promising alternative to conventional fossil-fuel-based transportation. However, as EV adoption accelerates, efficient energy management becomes critical to enhance vehicle performance, extend battery life, and ensure overall system reliability. This research presents a Database-Driven Energy Management System (DBEMS) that leverages real-time data from EV components to facilitate intelligent decision-making and optimization.

The proposed system utilizes time-series databases to collect and store dynamic parameters such as battery health, temperature, charge-discharge cycles, and energy consumption patterns. By structuring this data within a robust database framework, the system enables comprehensive analysis and predictive modeling. A comparative study between SQL (Structured Query Language) and NoSQL (Not Only SQL) database architectures is conducted to evaluate their effectiveness in handling large-scale, heterogeneous EV data. The paper discusses the strengths and limitations of each model in terms of scalability, query performance, and data flexibility.

Furthermore, the integration of predictive analytics and machine learning techniques within the DBEMS framework is explored to forecast energy demands, detect anomalies, and optimize charging strategies. This approach not only improves operational efficiency but also contributes to the long-term sustainability of EV systems. The findings underscore the importance of data-centric energy management in advancing the next generation of smart electric vehicles.

Keywords: Pollution, Electric vehicles, Energy Management, DBMS, SQL NoSQL, Predective Analysis

[This article belongs to Journal of Automobile Engineering and Applications ]

How to cite this article:
PRIYA SHUKLA, MOHIT SHUKLA. DATABASE-DRIVEN ENERGY MANAGEMENT IN ELECTRIC VEHICLES. Journal of Automobile Engineering and Applications. 2025; 12(03):19-24.
How to cite this URL:
PRIYA SHUKLA, MOHIT SHUKLA. DATABASE-DRIVEN ENERGY MANAGEMENT IN ELECTRIC VEHICLES. Journal of Automobile Engineering and Applications. 2025; 12(03):19-24. Available from: https://journals.stmjournals.com/joaea/article=2025/view=230849


References

  1. André Hemmelder, Anne Sehnal, Simon Lux, Jens Leker “Big data analytics for strategic decision-making across the battery value chain: a review of status, trends, and future directions” Energy Strategy Reviews, Volume 60, July 2025, 101797. https://doi.org/10.1016/j.esr.2025.101797
  2. Dan Wu, Zhihong Xu, Qingli Wang, Zheyu Jin, Yulan Xu, Chongwei Wang & Xinping He “A Brief Review of Key Technologies for Cloud-Based Battery Management Systems” High-Energy Battery Materials, Published: 07 October 2024, Volume 53, pages 7334–7354, (2024).
  3. Yong Wang, Jingda Wu, Hongwen He, Zhongbao Wei & Fengchun Sun “Data-driven energy management for electric vehicles using offline reinforcement learning” Nature Communications volume 16, Article number: 2835 (2025), Published: 22 March 2025.
  4. Li, Y. et al. Deep reinforcement learning for intelligent energy management systems of hybrid-electric powertrains: Recent advances, open issues, and prospects. IEEE Transactions on Transportation Electrification (2024).
  5. Han, Q. et al. Hierarchical coordinated optimization and energy management control for plug-in hybrid electric heavy-duty truck platoon in coal mine transportation system. IEEE Transactions on Vehicular Technology (2024).
  6. Vlatko Milic “Next-generation data center energy management: a data-driven decision-making framework” Frontiers Frontiers in Energy Research; September 202412
  7. Xing He , Shanghai Jiao Tong University , Qian Ai , C. Qiu , Wentao Huang “A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory” January 2015IEEE Transactions on Smart Grid 8 (2); DOI:10.1109/TSG.2015.2445828
  8. Ganesh, A. H. & Xu, B. A review of reinforcement learning based energy management systems for electrified powertrains: progress, challenge, and potential solution. Renew. Sustain. Energy Rev. 154, 111833 (2022).
  9. Wang, J., Kang, L. & Liu, Y. Optimal scheduling for electric bus fleets based on dynamic programming approach by considering battery capacity fade. Renew. Sustain. Energy Rev. 130, 109978 (2020).
  10. Quan, S. et al. Customized energy management for fuel cell electric vehicle based on deep reinforcement learning-model predictive control self-regulation framework. IEEE Transactions on Industrial Informatics (2024).
  11. Wang, Y., Wu, Y., Tang, Y., Li, Q. & He, H. Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning. Appl. Energy 332, 120563 (2023).
  12. Chen, W. et al. Health-considered energy management strategy for fuel cell hybrid electric vehicle based on improved soft actor critic algorithm adopted with beta policy. Energy Convers. Manag. 292, 117362 (2023).
  13. Wang, Y., Lian, R., He, H., Betz, J. & Wei, H. Auto-tuning dynamics parameters of intelligent electric vehicles via Bayesian optimization. IEEE Transactions on Transportation Electrification (2023).
  14. Block, A., Jadbabaie, A., Pfrommer, D., Simchowitz, M. & Tedrake, R. Provable guarantees for generative behavior cloning: Bridging low-level stability and high-level behavior. Advances in Neural Information Processing Systems 36 (2024).
  15. Saiteja, P. & Ashok, B. Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles. Renew. Sustain. Energy Rev. 157, 112038 (2022).
  16. Hu, B., Liu, B. & Zhang, S. A data-driven reinforcement learning based energy management strategy via bridging offline initialization and online fine-tuning for a hybrid electric vehicle. IEEE Trans. Ind. Electron. 71, 12869–12878 (2024).
  17. Liu, B., Wei, X., Sun, C., Wang, B. & Huo, W. A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles. Int. J. Hydrog. Energy 55, 1371–1382 (2024).
  18. Millo, F., Rolando, L., Tresca, L. & Pulvirenti, L. Development of a neural network-based energy management system for a plug-in hybrid electric vehicle. Transport. Eng. 11, 100156 (2023).
  19. Li, Q. et al. A hybrid physics-data driven approach for vehicle dynamics state estimation. Mech. Syst. Signal Process. 225, 112249 (2025).
  20. Lu, J., Xiong, R., Tian, J., Wang, C. & Sun, F. Deep learning to estimate lithium-ion battery state of health without additional degradation experiments. Nat. Commun. 14, 2760 (2023).
Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 11/07/2025
Accepted 21/10/2025
Published 31/10/2025
Publication Time 112 Days
203

Login


My IP

PlumX Metrics