Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
149 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Review on AI Algorithms for Energy Management in E-Mobility Services (2309.15140v1)

Published 26 Sep 2023 in cs.LG, cs.AI, cs.SY, and eess.SY

Abstract: E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the imperative to combat climate change underscore the significance of transitioning to electric vehicles (EVs). This paper seeks to explore the potential of AI in addressing various challenges related to effective energy management in e-mobility systems (EMS). These challenges encompass critical factors such as range anxiety, charge rate optimization, and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. Our objectives are twofold: to provide an overview of the current state-of-the-art in this research domain and propose effective avenues for future investigations. Through this analysis, we aim to contribute to the advancement of sustainable and efficient e-mobility solutions, shaping a greener and more sustainable future for transportation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. J. Zhang, Z. Wang, P. Liu, and Z. Zhang, “Energy consumption analysis and prediction of electric vehicles based on real-world driving data,” Applied Energy, vol. 275, p. 115408, Oct. 2020. [Online]. Available: https://doi.org/10.1016/j.apenergy.2020.115408
  2. C. Yang, M. Zha, W. Wang, K. Liu, and C. Xiang, “Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: review and recent advances under intelligent transportation system,” IET Intelligent Transport Systems, vol. 14, no. 7, pp. 702–711, May 2020. [Online]. Available: https://doi.org/10.1049/iet-its.2019.0606
  3. M. Nigro, M. Ferrara, R. D. Vincentis, C. Liberto, and G. Valenti, “Data driven approaches for sustainable development of e-mobility in urban areas,” Energies, vol. 14, no. 13, p. 3949, Jul. 2021. [Online]. Available: https://doi.org/10.3390/en14133949
  4. H. İnaç, Y. E. Ayözen, A. Atalan, and C. Ç. Dönmez, “Estimation of postal service delivery time and energy cost with e-scooter by machine learning algorithms,” Applied Sciences, vol. 12, no. 23, p. 12266, Nov. 2022. [Online]. Available: https://doi.org/10.3390/app122312266
  5. E. Burani, G. Cabri, and M. Leoncini, “An algorithm to predict e-bike power consumption based on planned routes,” Electronics, vol. 11, no. 7, p. 1105, Mar. 2022. [Online]. Available: https://doi.org/10.3390/electronics11071105
  6. X. Hu, T. Liu, X. Qi, and M. Barth, “Reinforcement learning for hybrid and plug-in hybrid electric vehicle energy management: Recent advances and prospects,” IEEE Industrial Electronics Magazine, vol. 13, no. 3, pp. 16–25, Sep. 2019. [Online]. Available: https://doi.org/10.1109/mie.2019.2913015
  7. F. Zhang, X. Hu, R. Langari, and D. Cao, “Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook,” Progress in Energy and Combustion Science, vol. 73, pp. 235–256, Jul. 2019. [Online]. Available: https://doi.org/10.1016/j.pecs.2019.04.002
  8. Y. Chen, G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese, “A review and outlook on energy consumption estimation models for electric vehicles,” SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy, vol. 2, no. 1, Mar. 2021. [Online]. Available: https://doi.org/10.4271/13-02-01-0005
  9. Z. Wang, G. Feng, D. Zhen, F. Gu, and A. Ball, “A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles,” Energy Reports, vol. 7, pp. 5141–5161, Nov. 2021. [Online]. Available: https://doi.org/10.1016/j.egyr.2021.08.113
  10. M. Adaikkappan and N. Sathiyamoorthy, “Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review,” International Journal of Energy Research, vol. 46, no. 3, pp. 2141–2165, Oct. 2021. [Online]. Available: https://doi.org/10.1002/er.7339
  11. W. Liu, T. Placke, and K. Chau, “Overview of batteries and battery management for electric vehicles,” Energy Reports, vol. 8, pp. 4058–4084, Nov. 2022. [Online]. Available: https://doi.org/10.1016/j.egyr.2022.03.016
  12. S. A. Anbaran, N. R. N. Idris, M. Jannati, M. J. Aziz, and I. Alsofyani, “Rule-based supervisory control of split-parallel hybrid electric vehicle,” in 2014 IEEE Conference on Energy Conversion (CENCON).   IEEE, Oct. 2014. [Online]. Available: https://doi.org/10.1109/cencon.2014.6967468
  13. F. R. Salmasi, “Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends,” IEEE Transactions on Vehicular Technology, vol. 56, no. 5, pp. 2393–2404, Sep. 2007. [Online]. Available: https://doi.org/10.1109/tvt.2007.899933
  14. X. Wang, L. Li, K. He, and C. Liu, “Dual-loop self-learning fuzzy control for AMT gear engagement: Design and experiment,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 1813–1822, Aug. 2018. [Online]. Available: https://doi.org/10.1109/tfuzz.2017.2779102
  15. C.-C. Lin, H. Peng, J. Grizzle, and J.-M. Kang, “Power management strategy for a parallel hybrid electric truck,” IEEE Transactions on Control Systems Technology, vol. 11, no. 6, pp. 839–849, Nov. 2003. [Online]. Available: https://doi.org/10.1109/tcst.2003.815606
  16. L. Xu, M. Ouyang, J. Li, F. Yang, L. Lu, and J. Hua, “Application of pontryagin's minimal principle to the energy management strategy of plugin fuel cell electric vehicles,” International Journal of Hydrogen Energy, vol. 38, no. 24, pp. 10 104–10 115, Aug. 2013. [Online]. Available: https://doi.org/10.1016/j.ijhydene.2013.05.125
  17. C. Yang, Y. Shi, L. Li, and X. Wang, “Efficient mode transition control for parallel hybrid electric vehicle with adaptive dual-loop control framework,” IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 1519–1532, Feb. 2020. [Online]. Available: https://doi.org/10.1109/tvt.2019.2962509
  18. C. Dextreit, F. Assadian, I. V. Kolmanovsky, J. Mahtani, and K. Burnham, “Hybrid electric vehicle energy management using game theory,” in SAE Technical Paper Series.   SAE International, Apr. 2008. [Online]. Available: https://doi.org/10.4271/2008-01-1317
  19. B. Škugor, J. Deur, M. Cipek, and D. Pavković, “Design of a power-split hybrid electric vehicle control system utilizing a rule-based controller and an equivalent consumption minimization strategy,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 228, no. 6, pp. 631–648, Jan. 2014. [Online]. Available: https://doi.org/10.1177/0954407013517220
  20. C. Yang, S. You, W. Wang, L. Li, and C. Xiang, “A stochastic predictive energy management strategy for plug-in hybrid electric vehicles based on fast rolling optimization,” IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9659–9670, Nov. 2020. [Online]. Available: https://doi.org/10.1109/tie.2019.2955398
  21. H. Hu, W.-W. Yuan, M. Su, and K. Ou, “Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems,” Energy Conversion and Management, vol. 291, p. 117288, Sep. 2023. [Online]. Available: https://doi.org/10.1016/j.enconman.2023.117288
  22. H. Mediouni, A. Ezzouhri, Z. Charouh, K. E. Harouri, S. E. Hani, and M. Ghogho, “Energy consumption prediction and analysis for electric vehicles: A hybrid approach,” Energies, vol. 15, no. 17, p. 6490, Sep. 2022. [Online]. Available: https://doi.org/10.3390/en15176490
  23. I. Ullah, K. Liu, T. Yamamoto, R. E. A. Mamlook, and A. Jamal, “A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability,” Energy & Environment, vol. 33, no. 8, pp. 1583–1612, Oct. 2021. [Online]. Available: https://doi.org/10.1177/0958305x211044998
  24. F. C. López and R. Á. Fernández, “Predictive model for energy consumption of battery electric vehicle with consideration of self-uncertainty route factors,” Journal of Cleaner Production, vol. 276, p. 124188, Dec. 2020. [Online]. Available: https://doi.org/10.1016/j.jclepro.2020.124188
  25. H. Abdelaty, A. Al-Obaidi, M. Mohamed, and H. E. Farag, “Machine learning prediction models for battery-electric bus energy consumption in transit,” Transportation Research Part D: Transport and Environment, vol. 96, p. 102868, Jul. 2021. [Online]. Available: https://doi.org/10.1016/j.trd.2021.102868
  26. I. Ullah, K. Liu, T. Yamamoto, M. Zahid, and A. Jamal, “Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach,” International Journal of Green Energy, vol. 18, no. 9, pp. 896–909, Feb. 2021. [Online]. Available: https://doi.org/10.1080/15435075.2021.1881902
  27. M. Ragone, V. Yurkiv, A. Ramasubramanian, B. Kashir, and F. Mashayek, “Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling,” Journal of Power Sources, vol. 483, p. 229108, Jan. 2021. [Online]. Available: https://doi.org/10.1016/j.jpowsour.2020.229108
  28. P. Li, Y. Zhang, Y. Zhang, Y. Zhang, and K. Zhang, “Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data,” Applied Energy, vol. 298, p. 117204, Sep. 2021. [Online]. Available: https://doi.org/10.1016/j.apenergy.2021.117204
  29. S. Gadri, S. O. Mehieddine, K. Herizi, and S. Chabira, “An efficient system to predict customers’ satisfaction on touristic services using ML and DL approaches,” in 2021 22nd International Arab Conference on Information Technology (ACIT).   IEEE, Dec. 2021. [Online]. Available: https://doi.org/10.1109/acit53391.2021.9677167
  30. J. P. Trovão, P. G. Pereirinha, H. M. Jorge, and C. H. Antunes, “A multi-level energy management system for multi-source electric vehicles – an integrated rule-based meta-heuristic approach,” Applied Energy, vol. 105, pp. 304–318, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306261913000081
  31. B. Zheng, L. Ming, Q. Hu, Z. Lü, G. Liu, and X. Zhou, “Supply-demand-aware deep reinforcement learning for dynamic fleet management,” ACM Trans. Intell. Syst. Technol., vol. 13, no. 3, jan 2022. [Online]. Available: https://doi.org/10.1145/3467979
  32. D. N. T. How, M. A. Hannan, M. S. H. Lipu, K. S. M. Sahari, P. J. Ker, and K. M. Muttaqi, “State-of-charge estimation of li-ion battery in electric vehicles: A deep neural network approach,” IEEE Transactions on Industry Applications, vol. 56, no. 5, pp. 5565–5574, Sep. 2020. [Online]. Available: https://doi.org/10.1109/tia.2020.3004294
  33. J. Hong, Z. Wang, W. Chen, and Y. Yao, “Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks,” Applied Energy, vol. 254, p. 113648, Nov. 2019. [Online]. Available: https://doi.org/10.1016/j.apenergy.2019.113648
  34. S. Modi, J. Bhattacharya, and P. Basak, “Estimation of energy consumption of electric vehicles using deep convolutional neural network to reduce driver’s range anxiety,” ISA Transactions, vol. 98, pp. 454–470, Mar. 2020. [Online]. Available: https://doi.org/10.1016/j.isatra.2019.08.055
  35. X. Qu, Y. Yu, M. Zhou, C.-T. Lin, and X. Wang, “Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach,” Applied Energy, vol. 257, p. 114030, Jan. 2020. [Online]. Available: https://doi.org/10.1016/j.apenergy.2019.114030
  36. H. Lee and S. W. Cha, “Energy management strategy of fuel cell electric vehicles using model-based reinforcement learning with data-driven model update,” IEEE Access, vol. 9, pp. 59 244–59 254, 2021. [Online]. Available: https://doi.org/10.1109/access.2021.3072903
  37. M. H. Lipu, M. Hannan, A. Hussain, A. Ayob, M. H. Saad, T. F. Karim, and D. N. How, “Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends,” Journal of Cleaner Production, vol. 277, p. 124110, Dec. 2020. [Online]. Available: https://doi.org/10.1016/j.jclepro.2020.124110
  38. X. Tang, T. Jia, X. Hu, Y. Huang, Z. Deng, and H. Pu, “Naturalistic data-driven predictive energy management for plug-in hybrid electric vehicles,” IEEE Transactions on Transportation Electrification, vol. 7, no. 2, pp. 497–508, Jun. 2021. [Online]. Available: https://doi.org/10.1109/tte.2020.3025352
  39. H. Sun, Z. Fu, F. Tao, L. Zhu, and P. Si, “Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles,” Journal of Power Sources, vol. 455, p. 227964, Apr. 2020. [Online]. Available: https://doi.org/10.1016/j.jpowsour.2020.227964
  40. Z. Deng, X. Hu, X. Lin, Y. Che, L. Xu, and W. Guo, “Data-driven state of charge estimation for lithium-ion battery packs based on gaussian process regression,” Energy, vol. 205, p. 118000, Aug. 2020. [Online]. Available: https://doi.org/10.1016/j.energy.2020.118000
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now