Skip to main content
SpringerLink
Log in

Sensor based Battery Management System in Electric Vehicle using IoT with Optimized Routing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

IoT based BMS (battery management system) is becoming an essential factor of an EV (electric vehicle) in recent years. The BMS is responsible for monitoring and controlling the state of the battery pack in an EV using appropriate. The IoT based BMS continuously monitors the voltage, temperature, and current of each battery cell and adjusts the charging and discharging of the battery pack accordingly from remote areas. The integration of IoT with a BMS can allow isolated monitoring of the EV's battery pack, enabling fleet managers or EV owners to track the performance of their vehicles and monitor their battery health. This paper primarily focus on IoT-Optimized Battery Management System (IoT-OBMS), which comprises two modules, IoT and charging, for effective energy storage management in electric vehicles. With particle filtering, the SOC of the battery in an EV is calculated, along with an estimate of the temperature inside the cell, and the cell parameters are directly estimated to the SOC. The data is stored in the Software Defined Network (SDN), and the Spider Swarm Monkey Optimization (SSMO) algorithm is used to estimate the optimal routing path. In the proposed charging module, the Mamdani fuzzy system rules are implemented with a Boltzmann neural network for BMS. The integration of fuzzy rules in the deep learning model controls and manages the EV battery. Therefore, simulation results illustrate the reasonable value of the proposed system in terms of SOC estimation accuracy, charging time, and cost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

No data were used to support this article.

References

  1. Han X, Zhang C, Wei X, Wang L (2020) Battery management system for electric vehicles: A review. J Power Sources 450:227601. https://doi.org/10.1016/j.jpowsour.2020.227601

    Article  Google Scholar 

  2. Chen S, Xiong R, He H, Zhang Y (2021) A review of battery management system for electric vehicles based on hierarchical architecture. J Energy Storage 33:102032. https://doi.org/10.1016/j.est.2020.102032

    Article  Google Scholar 

  3. Leung SC, Sze TF (2021) Electric vehicle battery management system: Challenges and solutions. J Energy Storage 33:101999. https://doi.org/10.1016/j.est.2020.101999

    Article  Google Scholar 

  4. Cui Y, Xie Z, Xiong R (2020) A review of battery management systems in electric vehicles: State-of-the-art and future trends. Energies 13(17):4444. https://doi.org/10.3390/en13174444

    Article  Google Scholar 

  5. Wang L, Zhang C, Wei X, Han X (2020) Battery management system for electric vehicles: Challenges and opportunities. J Energy Storage 30:101501. https://doi.org/10.1016/j.est.2020.101501

    Article  Google Scholar 

  6. Chen Z, Peng L, Guo X, Cheng P (2020) Internet of Things for electric vehicle battery management system: A review. Energies 13(17):4309. https://doi.org/10.3390/en13174309

    Article  Google Scholar 

  7. Wang C, Li X, Li X, Zhang Y, Liu B (2021) IoT-based battery management system for electric vehicles: A review. J Clean Prod 306:127048. https://doi.org/10.1016/j.jclepro.2021.127048

    Article  Google Scholar 

  8. Yoo J, Kim Y, Kim M, Kim D (2021) Internet of things-based battery management system for electric vehicles: A review. Energies 14(1):47. https://doi.org/10.3390/en14010047

    Article  Google Scholar 

  9. Hu Y, Li Y (2020) Cloud internet of things in battery management system for electric vehicles: A review. J Energy Storage 28:101235. https://doi.org/10.1016/j.est.2019.101235

    Article  Google Scholar 

  10. Kim HJ, Lee H (2020) IoT-based battery management system for electric vehicles: A review. Energies 13(22):5871. https://doi.org/10.3390/en13225871

    Article  Google Scholar 

  11. Zhao B, Zhang Y, Sun H, Zhou J (2021) Cloud-enabled battery management system for electric vehicles: A review. IEEE Access 9:59387–59400. https://doi.org/10.1109/ACCESS.2021.3079355

    Article  Google Scholar 

  12. Zhang Y, Song B, Xu W, Lu Z, Wu C (2021) Battery management system for electric vehicles based on Internet of Things and machine learning. IEEE Trans Industr Electron 68(9):7408–7418. https://doi.org/10.1109/TIE.2020.3043021

    Article  Google Scholar 

  13. Hu J, Sun Y, Li H (2020) A review on battery management system in electric vehicles. IEEE Trans Smart Grid 11(5):4575–4584. https://doi.org/10.1109/TSG.2020.2978645

    Article  Google Scholar 

  14. Dincer I (2020) Electric vehicles and renewable energy: A review. Renew Sustain Energy Rev 133:110240. https://doi.org/10.1016/j.rser.2020.110240

    Article  Google Scholar 

  15. Chen K, Zhang K, Xiong R, Wu B (2021) Battery management systems for electric vehicles: Challenges and opportunities. Renew Sustain Energy Rev 137:110614. https://doi.org/10.1016/j.rser.2020.110614

    Article  Google Scholar 

  16. Burgio A, Cimmino D, Nappo A, Smarrazzo L, Donatiello G (2023) An IoT-Based Solution for Monitoring and Controlling Battery Energy Storage Systems at Residential and Commercial Levels. Energies 16(7):3140

    Article  Google Scholar 

  17. Shi D, Zhao J, Eze C, Wang Z, Wang J, Lian Y, Burke AF (2023) Cloud-Based Artificial Intelligence Framework for Battery Management System. Energies 16(11):4403

    Article  Google Scholar 

  18. Shi D, Zhao J, Wang Z, Zhao H, Eze C, Wang J, Lian Y, Burke AF (2023) Cloud-based deep learning for co-estimation of battery state of charge and state of health. Energies 16(9):3855. https://doi.org/10.3390/en16093855

    Article  Google Scholar 

  19. Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S (2023) A hybrid approach for latency and battery lifetime optimization in iot devices through offloading and CNN learning. Sustain Comput: Inf Syst 39:100899. https://doi.org/10.1016/j.suscom.2023.100899

  20. Eldeeb E, de Souza Sant’Ana JM, Pérez DE, Shehab M, Mahmood NH, Alves H (2022) Multi-UAV path learning for age and power optimization in IoT with UAV battery recharge. IEEE Trans Veh Technol 72(4):5356–5360

    Article  Google Scholar 

  21. Sreenivasu SVN, Sathesh Kumar T, Bin Hussain O, Yeruva AR, Kabat SR, Chaturvedi A (2023) Cloud based electric vehicle’s temperature monitoring system using IOT. Cybern Syst. https://doi.org/10.1080/01969722.2023.2176649

    Article  Google Scholar 

  22. Mondal A, Routray A, Puravankara S (2022) Parameter identification and co-estimation of state-of-charge of Li-ion battery in real-time on Internet-of-Things platform. J Energy Storage 51:104370

    Article  Google Scholar 

  23. Goldsworthy M, Moore T, Peristy M, Grimeland M (2022) Cloud-based model-predictive-control of a battery storage system at a commercial site. Appl Energy 327:120038

    Article  Google Scholar 

  24. Liu J, Tang K, Yang J, Yu Q, Wang X (2021) A cloud-based battery management system for electric vehicles using IoT and deep learning. Energies 14(7):1944

    Google Scholar 

  25. Liu J, Tang K, Wang X, Yang J, Yu Q (2021) An IoT-based battery management system for electric vehicles using cloud computing. J Energy Storage 41:102963

    Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, College of Engineering and Technology, SRM Institute of Science and Technolo-Gy, Kattankulathur, Chennai, 603203, Tamil Nadu, India

    Anbazhagan Geetha

  2. Department of Information Technology, Kalasalingam Academy of Research and Education, Anand Nagar, Krishna Koil, 626126, Tamil Nadu, India

    S. Suprakash

  3. Division of Convergence, Honam University, 120, Honamdae-Gil, Gwangsan-Gu, Gwangju-Si, 62399, Republic of Korea

    Se-Jung Lim

Authors
  1. Anbazhagan Geetha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Suprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Se-Jung Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Se-Jung Lim.

Ethics declarations

Conflict of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geetha, A., Suprakash, S. & Lim, SJ. Sensor based Battery Management System in Electric Vehicle using IoT with Optimized Routing. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-023-02262-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02262-z

Keywords

Search

Navigation