Skip to main content

Advertisement

SpringerLink
Log in

Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is transformed into static maps and fed into a neural network for prototype energy optimization for CAN bus and media control in electric vehicles. The proposed model includes the prediction of battery state-of-charge as well as the consumption of fuel-at-destination. The controller area network (CAN) bus is the most important element in EV, ensuring its protection is the most difficult task. The abnormal messages of the CAN bus are detected using DNN. The suggested DNN model is an integrated triplet network loss which minimizes the length among the anchor sample as well as the positive sample is comparably minimum than the length measured between anchor sample and negative sample. The proposed DNN model is utilized for CAN bus and various media control in electric vehicles for effective performance.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

References

  1. Skouras TA, Gkonis PK, Ilias CN, Trakadas PT, Tsampasis EG, Zahariadis TV (2020) Electrical vehicles: current state of the art, future challenges, and perspectives. Clean Technol 2:1–16

    Article  Google Scholar 

  2. Markkandan S, Sharma A, Singh SP et al (2021) SVM-based compliance discrepancies detection using remote sensing for organic farms. Arab J Geosci 14(1334):1–8. https://doi.org/10.1007/s12517-021-07700-4

    Article  Google Scholar 

  3. Sthel M, Tostes JGR, Tavares JR (2013) Current energy crisis and its economic and environmental consequences: Intense human cooperation. Nat Sci 5:244–252

    Google Scholar 

  4. Rajamani R (2012) Longitudinal vehicle dynamics. In: Rajamani R (ed) Vehicle dynamics and control. Springer, Berlin, pp 87–111

    Chapter  Google Scholar 

  5. Ye K, Li P, Li H (2020) Optimization of hybrid energy storage system control strategy for pure electric vehicle based on typical driving cycle. Math Probl Eng 2020:1365195

    MathSciNet  Google Scholar 

  6. Lei Z, Qin D, Liu Y, Peng Z, Lu L (2017) Dynamic energy management for a novel hybrid electric system based on driving pattern recognition. Appl Math Model 45:940–954

    Article  MathSciNet  Google Scholar 

  7. So KM, Gruber P, Tavernini D, Karci AEH, Sorniotti A, Motaln T (2020) On the optimal speed profile for electric vehicles. IEEE Access 8:78504–78518

    Article  Google Scholar 

  8. Zeng X, Cui C, Wang Y, Li G, Song D (2019) Segemented driving cycle based optimization of control parameters for power-split hybrid electric vehicle with ultracapacitors. IEEE Access 7:90666–90677

    Article  Google Scholar 

  9. Moura SJ, Fathy HK, Callaway DS, Stein JL (2011) A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Trans Control Syst Technol 19:545–555

    Article  Google Scholar 

  10. Chen Z, Xiong R, Wang K, Jiao B (2015) Optimal energy management strategy of a plug-in hybrid electric vehicle based on a particle swarm optimization algorithm. Energies 8:3661–3678

    Article  Google Scholar 

  11. Markkandan S, Logeshwaran R, Venkateswaran N (2021) Analysis of precoder decomposition algorithms for MIMO system design. IETE J Res. https://doi.org/10.1080/03772063.2021.1920848

    Article  Google Scholar 

  12. Müter M. Asaj N (2011) Entropy-based anomaly detection for in-vehicle networks. In Proceedings of the IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany, pp. 5–9

  13. Nilsson D, Larson UE (2008) Simulated attacks on CAN buses: vehicle virus. In: Proceedings of the 5th IASTED International Conference on Communication Systems and Networks, Palma de Mallorca, Spain, pp. 1–3

  14. Miller C, Valasek C (2013) Adventures in automotive networks and control units. Def Con 21:260–264

    Google Scholar 

  15. Miller C, Valasek C (2014) A survey of remote automotive attack surfaces. In: Proceedings of the Black Hat, Las Vegas, NV, USA, 2–7 p. 94.

  16. Othmane LB, Weffers H, Mohamad MM, Wolf M (2015) A survey of security and privacy in connected vehicles. In: Benhaddou D, Al-Fuqaha A (eds) Wireless Sensor and Mobile Ad-Hoc Networks. New York, Springer, pp 217–247

    Google Scholar 

  17. Markovitz M, Wool A (2017) Field classification, modeling and anomaly detection in unknown can bus networks. Veh Commun 9:43–52

    Google Scholar 

  18. Wang J, Ma H (2004) Humanoid force information detection system based on can bus. J Huazhong Univ Sci Technol 32:164–166

    Google Scholar 

  19. Lv C, Hu X, Sangiovanni-Vincentelli A, Li Y, Martinez CM, Cao D (2019) Driving-style-based codesign optimization of an automated electric vehicle: a cyber-physical system approach. IEEE Trans Ind Electron 66:2965–2975

    Article  Google Scholar 

  20. Jafari M, Gauchia A, Zhang K, Gauchia L (2015) Simulation and analysis of the effect of real-world driving styles in an EV battery performance and aging. IEEE Trans Transp Electrif 1:391–401

    Article  Google Scholar 

  21. Yang S, Wang W, Zhang F, Hu Y, Xi J (2018) Driving-style-oriented adaptive equivalent consumption minimization strategies for HEVs. IEEE Trans Veh Technol 67:9249–9261

    Article  Google Scholar 

  22. Neubauer J, Wood E (2013) Accounting for the variation of driver aggression in the simulation of conventional and advanced vehicles. In: Proceedings of the SAE World Congress and Exhibition, Detroit, MI, USA, pp. 16–18, Volume 1

  23. Guo Q, Zhao Z, Shen P, Zhan X, Li J (2019) Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle. Energy 186:115824

    Article  Google Scholar 

  24. Sciarretta A, De Nunzio G, Ojeda LL (2015) Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst Mag 35:71–90

    Article  MathSciNet  Google Scholar 

  25. Sarabi S, Kefsi L (2014) Electric vehicle charging strategy based on a dynamic programming algorithm. In: Proceedings of the 2014 IEEE International Conference on Intelligent Energy and Power Systems (IEPS), Kyiv, Ukraine, 2–6 pp. 1–5

  26. Yang Y, Pei H, Hu X, Liu Y, Hou C, Cao D (2019) Fuel economy optimization of power split hybrid vehicles: a rapid dynamic programming approach. Energy 166:929–938

    Article  Google Scholar 

  27. Duc D, Fujimoto H, Koseki T, Yasuda T, Kishi H, Fujita T (2018) Iterative dynamic programming for optimal control problem with isoperimetric constraint and its application to optimal eco-driving control of electric vehicle. IEEJ J Ind Appl 7:80–92

    Google Scholar 

  28. Schwarzer V, Ghorbani R (2013) Drive cycle generation for design optimization of electric vehicles. IEEE Trans Veh Technol 62:89–97

    Article  Google Scholar 

  29. Liaw BY, Dubarry M (2007) From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation. J Power Sour 174:76–88

    Article  Google Scholar 

  30. Salameh M, Brown IP, Krishnamurthy M (2019) Driving cycle analysis methods using data clustering for machine design optimization. In: Proceedings of the 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 19–21, pp. 1–6.

  31. Müter M, Groll A, Freiling FC (2010) A structured approach to anomaly detection for in-vehicle networks. In: Proceedings of the Sixth IEEE International Conference on Information Assurance and Security, Atlanta, GA, USA, 23–25, pp. 92–98

  32. Woo S, Jo HJ, Lee DH (2014) A practical wireless attack on the connected car and security protocol for in-vehicle CAN. IEEE Trans Intell Transp Syst 16:1–14

    Article  Google Scholar 

  33. Zang D, Liu J, Wang H (2018) Markov chain-based feature extraction for anomaly detection in time series and its industrial application. In: Proceedings of the Chinese Control and Decision Conference (CCDC), Shenyang, China, pp. 9–11

  34. Wang X, Zhou Q, Harer J, Brown G, Chin P (2018) Deep learning-based classification and anomaly detection of side-channel signals. Cyber Sens 10630:1063006

    Google Scholar 

  35. Nawaz S, Calefati A, Ahmed N, Gallo I (2018) Handwritten characters recognition via deep metric learning. In: Proceedings of the 13th IAPR International Workshop on Document Analysis Systems (DAS), Vienna, Austria, 24–27 pp. 417–422

Download references

Funding

None.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Terna Engineering College, Mumbai University, Mumbai, India

    Satish S. Salunkhe

  2. College of Computing Sciences and Information Technology (CCSIT), Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

    Shelendra Pal

  3. Department of Mechanical Engineering, University Institute of Technology RGPV, Bhopal, India

    Abhishek Agrawal

  4. Department of Mechanical Engineering, University Institute of Technology, RGPV, Bhopal, Madhya Pradesh, India

    Ravi Rai

  5. Department of Electronics and Communication Engineering, Christu Jyothi Institute of Institute of Technology and Science, Yeswanthapur, India

    S. S. Sreeja Mole

  6. Department of Electrical and Electronics Engineering, Mar Athanasius College of Engineering, M A College P O Ernakulam(Dist.), Kothamangalam, Kerala, India

    Bos Mathew Jos

Authors
  1. Satish S. Salunkhe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shelendra Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhishek Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ravi Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. S. Sreeja Mole
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bos Mathew Jos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satish S. Salunkhe.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salunkhe, S.S., Pal, S., Agrawal, A. et al. Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms. J Supercomput 78, 8493–8508 (2022). https://doi.org/10.1007/s11227-021-04186-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04186-5

Keywords

Search

Navigation