SpringerLink
Log in

Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

Abstract

The growing demand for electric vehicles (EVs) has necessitated the development of advanced maintenance systems to ensure their reliability, longevity, and cost-effectiveness. This paper presents an innovative approach for the predictive maintenance of EV components by integrating optical and quantum-enhanced artificial intelligence (AI) techniques. The proposed system employs fiber Bragg grating (FBG) sensors to capture high-resolution, real-time data on critical EV components such as the battery, electric motor, and power electronics. These sensors offer numerous advantages, including immunity to electromagnetic interference, high sensitivity, and multiplexing capabilities. To process the acquired data, we employ a quantum-enhanced machine learning algorithm, harnessing the power of quantum computing to handle large-scale data sets and improve prediction accuracy. Our AI model is trained to detect early signs of component degradation and predict potential failures, allowing for proactive maintenance and minimal downtime. The experimental results demonstrate the effectiveness of our approach in achieving accurate, timely predictions, thereby enhancing the overall performance and durability of electric vehicle components. This research paves the way for the development of advanced, efficient, and environmentally friendly transportation systems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

All the data’s available in the manuscript.

References

  • Chen, W., Yang, S., Li, Y., Zhang, Y.: Intelligent fault diagnosis of electric vehicle battery system based on stacked denoising autoencoder and random forest algorithm. IEEE Trans. Industr. Electron. 67(9), 7478–7488 (2020a)

    Google Scholar 

  • Chen, Y., Zhang, W., Chen, H.: A robust data-driven model predictive control for electric vehicle charging under uncertainty. IEEE Trans. Industr. Inf. 16(3), 1823–1833 (2020b)

    Article  Google Scholar 

  • Chen, L., Yang, W., Liu, F.: Design and analysis of a current-sharing-based electric vehicle charger. Int. J. Electr. Power Energy Syst. 121, 106013 (2020c)

    Google Scholar 

  • Chen, X., Zhang, J., Chen, Z.: Charging infrastructure planning for electric vehicles: a review of modeling approaches and optimization methods. Renew. Sustain. Energy Rev. 138, 110557 (2021a)

    Google Scholar 

  • Chen, T., Liu, Z., Wang, L., Chen, Y.: A novel distributed optimization algorithm for cooperative charging of electric vehicles. IEEE Trans. Smart Grid 12(2), 1873–1883 (2021b)

    Google Scholar 

  • Chen, Y., Liu, X., Xu, Z., Zhang, H.: Multiobjective charging strategy for electric vehicles considering charging station management. IEEE Trans. Industr. Electron. 68(4), 2823–2833 (2021c)

    Google Scholar 

  • Chen, T., Li, Y., Liu, Z., Chen, Y.: Dynamic clustering and task allocation for cooperative charging of electric vehicles in smart grids. J. Clean. Prod. 285, 125243 (2021d)

    Google Scholar 

  • Cui, Y., Chen, N., **e, J., Huang, H.: Online fast-charging strategy of electric vehicle battery based on improved ant colony algorithm. J. Clean. Prod. 291, 125793 (2021)

    Google Scholar 

  • Du, P., Zheng, Y., Dong, Z., Zhang, Y.: A novel charging strategy for electric vehicle with intermittent renewable energy sources. Appl. Energy 279, 115906 (2020)

    Google Scholar 

  • Gao, S., Zheng, Z., Wu, J.: Joint optimization of electric vehicle charging and energy storage system operation for load leveling in distribution grids. IEEE Trans. Smart Grid 12(2), 1356–1368 (2021)

    Google Scholar 

  • He, H., Wang, X., Chen, Z.: A comprehensive survey of data-driven battery modeling and diagnosis techniques for electric vehicles. IEEE Trans. Transp. Electrif. 7(1), 366–385 (2021)

    Google Scholar 

  • Hu, W., Gao, S., Wang, Y., Chen, G.: Probabilistic optimal electric vehicle charging planning considering battery degradation and supply-demand uncertainty. IEEE Trans. Industr. Electron. 68(8), 6712–6721 (2021)

    Google Scholar 

  • Huang, Z., Liu, J.: Anomaly detection of electric vehicle battery system based on deep belief network. IEEE Trans. Transp. Electrif. 6(3), 1099–1107 (2020)

    Google Scholar 

  • Huang, X., Sun, J., Yu, Y.: An intelligent control framework for electric vehicle charging in distribution grids based on multi-objective optimization. IEEE Trans. Industr. Inf. 16(12), 7853–7862 (2020)

    Google Scholar 

  • Jia, X., Wang, B., Zhang, X., Wang, Y.: A comprehensive review of intelligent vehicle charging strategies in the smart grid. Renew. Sustain. Energy Rev. 137, 110613 (2021a)

    Google Scholar 

  • Jia, X., Wang, B., Zhang, X., Wang, Y.: Smart charging of electric vehicles in a smart grid: a review. J. Clean. Prod. 294, 126285 (2021b)

    Google Scholar 

  • Jia, X., Wang, B., Zhang, X., Wang, Y.: Electric vehicle charging in smart grid: a review of methods and models. Renew. Sustain. Energy Rev. 141, 110997 (2021c)

    Google Scholar 

  • Jiang, S., Yang, Z.: Review of electric vehicle charging strategies. IEEE Trans. Veh. Technol. 69(11), 12724–12738 (2020)

    Google Scholar 

  • **, X., Qiao, W., Wang, J., Zhang, J.: an efficient and secure authentication scheme for intelligent charging of electric vehicles in a smart grid. IEEE Trans. Smart Grid 12(3), 2263–2273 (2021)

    Google Scholar 

  • Li, G., Liu, Y., Wu, W., Wang, S.: Optimal charging strategy for electric vehicles based on genetic algorithm and particle swarm optimization. IET Intel. Transp. Syst. 14(9), 992–999 (2020a)

    Google Scholar 

  • Li, X., Liu, Y., Li, Q., Xue, X.: Optimal charging strategy for electric vehicle based on improved fruit fly optimization algorithm. IET Intel. Transp. Syst. 14(9), 986–991 (2020b)

    Google Scholar 

  • Li, Q., Li, X., Li, X., Zeng, P.: Bi-objective optimal charging strategy of electric vehicle based on chaotic whale optimization algorithm. IET Intel. Transp. Syst. 14(12), 1629–1636 (2020c)

    Google Scholar 

  • Li, M., Li, X., Li, Y., Li, X.: A novel electric vehicle charging method based on whale optimization algorithm. IET Intel. Transp. Syst. 14(12), 1682–1689 (2020d)

    Google Scholar 

  • Li, Y., Li, X., Li, M.: Deep learning-based adaptive energy management strategy for electric vehicles. IEEE Trans. Industr. Electron. 68(3), 2666–2674 (2021a)

    ADS  Google Scholar 

  • Li, H., **ong, T., Cao, Z., Liu, C.: Optimal charging strategy for electric vehicles based on a genetic algorithm and a group search optimization algorithm. IEEE Trans. Intell. Transp. Syst. 22(3), 1803–1813 (2021b)

    Google Scholar 

  • Lin, H., Zhang, J., Chen, S., Chen, Z.: A hybrid deep learning method for remaining useful life prediction of electric vehicle batteries. IEEE Trans. Transp. Electrif. 6(4), 1571–1582 (2020)

    Google Scholar 

  • Peng, J., Li, Y., Gao, F., Wang, J., Liu, X.: Distributed charging strategy for electric vehicles in smart grid based on multiagent reinforcement learning. IEEE Trans. Smart Grid 11(3), 2507–2515 (2020)

    Google Scholar 

  • Qian, K., Wang, H., Hu, H.: Charging coordination for electric vehicles with shared chargers: an incentive-oriented approach. Appl. Energy 283, 116404 (2021)

    Google Scholar 

  • Shao, S., Guo, H., Wang, D., Zhao, J., Tian, Z.: Deep learning-based battery health prediction for electric vehicles with online learning. IEEE Trans. Industr. Electron. 68(3), 2528–2537 (2021)

    Google Scholar 

  • Wang, K., Zhang, K., Peng, L.: A comprehensive review of demand response in electric vehicle charging: theoretical background, challenges, and future directions. Renew. Sustain. Energy Rev. 133, 110223 (2020)

    Google Scholar 

  • Wang, M., Xu, L., Lv, H.: Joint optimization of charging scheduling and driving routes for electric vehicles based on reinforcement learning. IEEE Trans. Smart Grid 12(2), 1863–1872 (2021)

    Google Scholar 

  • **ao, M., Yu, W., Li, C., Li, S.: Multiobjective optimization-based online battery management system for electric vehicles. IEEE Trans. Veh. Technol. 69(5), 5245–5256 (2020)

    Google Scholar 

  • Yang, S., Yuan, X., Zhang, Y., Zeng, R.: A novel hybrid model of deep learning and grey wolf optimizer for battery remaining useful life prediction in electric vehicles. IEEE Access 9, 64792–64806 (2021a)

    Google Scholar 

  • Yang, Y., Liu, M., Cao, J., Zeng, J.: The electric vehicle charging infrastructure layout problem considering driving range and traffic congestion. IEEE Trans. Intell. Transp. Syst. 22(7), 4498–4508 (2021b)

    Google Scholar 

  • Yu, C., Shi, Z., Huang, Y., Liu, H.: Optimal charging strategy for electric vehicles based on differential evolution algorithm. IEEE Trans. Smart Grid 11(3), 2653–2662 (2020)

    Google Scholar 

  • Yu, Y., Huang, X., Hu, Z.: An enhanced artificial intelligence system for fault diagnosis of electric vehicle battery. J. Intell. Rob. Syst. 101(2), 27 (2021)

    Google Scholar 

  • Zhang, Y., Hu, J.: Optimal charging strategy of electric vehicle battery based on improved improved bat algorithm. IEEE Access 8, 191180–191189 (2022)

    Google Scholar 

  • Zhang, S., Liu, C., Chen, L., Hu, Y.: Dynamic power allocation for electric vehicle charging station considering uncertain vehicle arrival and departure: a mean field game approach. Int. J. Electr. Power Energy Syst. 118, 105862 (2020)

    Google Scholar 

  • Zhao, Y., Zheng, X., Chen, N., **e, J.: An improved differential evolution algorithm for optimal charging of electric vehicles considering uncertain parameters. Appl. Energy 279, 115694 (2020)

    Google Scholar 

  • Zhao, L., Zhao, F., Liu, X., Zeng, X.: Optimal charging strategy for electric vehicles based on improved deep Q-learning algorithm. Energy 226, 120454 (2021)

    Google Scholar 

Download references

Funding

This research not received any fund.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Sree Dattha Institute of Science and Engineering, Hyderabad, India

    P. Srinivasa Rao

  2. School of Computer Science, Dr.Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India

    Syed Irfan Yaqoob

  3. Department of Computer Engineering, College of Computer Engineering & Sciences, Sattam Bin Abdulaziz University, PrinceAl-Kharj, 11942, Saudi Arabia

    Mohammed Altaf Ahmed

  4. The Department of Bank Accounting and Audit, Tashkent Institute of Finance, Tashkent, Uzbekistan

    Pardaeva Shakhnoza Abdinabievna

  5. Faculty of Computational Science, GNA University, Phagwara, Jalandhar, India

    Syed Mufassir Yaseen

  6. Center for Transdisciplinary Research, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India

    Mahendran Arumugam

Authors
  1. P. Srinivasa Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Syed Irfan Yaqoob
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Altaf Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pardaeva Shakhnoza Abdinabievna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Syed Mufassir Yaseen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mahendran Arumugam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PSR Conceived and design the analysis, Writing—Original draft preparation. SIY Collecting the Data, MAA Contributed data and analysis stools, MAA Performed and analysis, SMY Performed and analysis, MA Wrote the Paper, Editing and Figure Design.

Corresponding author

Correspondence to P. Srinivasa Rao.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

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

Rao, P.S., Yaqoob, S.I., Ahmed, M.A. et al. Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements. Opt Quant Electron 55, 855 (2023). https://doi.org/10.1007/s11082-023-05135-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11082-023-05135-7

Keywords

Search

Navigation