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  4. Vol 4, Issue 2, 2023

Research progress and prospect of energy-saving optimal control for intelligent and connected electric vehicles

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DOI: 10.23977/autml.2023.040208 | Downloads: 27 | Views: 502

Author(s)

Wang Shuang 1

Affiliation(s)

1 China Academy of Transportation Science, Beijing, 100013, China

Corresponding Author

Wang Shuang

ABSTRACT

Modern communication and network technology are integrated to realize the exchange and sharing of intelligent information between vehicles, roads, people and clouds. It has the functions of complex environment perception, intelligent decision-making, collaborative control, etc., and can realize safe, efficient, comfortable and energy-saving driving, and finally realize a new generation of ICEV (Intelligent and Connected Electric Vehicles)operated instead of people. For ICEV, it is important to optimize the speed trajectory by using the information of the road ahead to realize the predictive energy-saving control, which will improve the economy of the vehicle. In order to fully understand the research progress of the optimal control of ICEV, the key issues of the optimal control of vehicle energy consumption and emissions based on the information of intelligent network are summarized. Finally, the future challenges in intelligent vehicle optimization are prospected, which provides a reference for further extensive research.

KEYWORDS

Intelligent and connected; Electric vehicles; Energy-saving

CITE THIS PAPER

Wang Shuang, Research progress and prospect of energy-saving optimal control for intelligent and connected electric vehicles. Automation and Machine Learning (2023) Vol. 4: 55-60. DOI: http://dx.doi.org/10.23977/autml.2023.040208.

REFERENCES

[1] Dongxin, L., Qiqige, W., Wenbo, C., Huilong, Y., & Xiaoping, D. (2021). A priority tree based coordination method for intelligent and connected vehicles at unsignalized intersections. IET intelligent transport systems, 2021(8), 15.
[2] Zhang, X., Cheng, Z., Ma, J., Huang, S., Lewis, F. L., & Lee, T. H. (2022). Semi-definite relaxation-based admm for cooperative planning and control of connected autonomous vehicles. IEEE transactions on intelligent transportation systems,2022(7), 23.
[3] Mahmoud, A., Noureldin, A., & Hassanein, H. S. (2019). Integrated positioning for connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 2019(99), 1-13.
[4] Jing, S., Hui, F., Zhao, X., Rios-Torres, J., & Khattak, A. J. (2019). Cooperative game approach to optimal merging sequence and on-ramp merging control of connected and automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 2019(99), 1-11.
[5] Li, S., Shu, K., Chen, C., & Cao, D. (2021). Planning and decision-making for connected autonomous vehicles at road intersections: a review. Chinese Journal of Mechanical Engineering, 34(1), 1-18.
[6] Lijun, Q., Lihong, Q., Peng, C., Zoleikha, A., & Pierluigi, P. (2017). Fuel efficient model predictive control strategies for a group of connected vehicles incorporating vertical vibration. Science China Technological Sciences, 2017(11), 140-154.
[7] Abousleiman, R., & Rawashdeh, O. (2014). Energy efficient routing for electric vehicles using particle swarm optimization. Sae Technical Papers, 1(1), 148-155.
[8] Wang, Y., Jiang, J., & Mu, T. (2013). Context-aware and energy-driven route optimization for fully electric vehicles via crowdsourcing. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1331-1345.
[9] Nandi, A. K., Chakraborty, D., & Vaz, W. (2015). Design of a comfortable optimal driving strategy for electric vehicles using multi-objective optimization. Journal of Power Sources, 283(1), 1-18.
[10] Dong, H., Zhuang, W., Chen, B., Wang, Y., Lu, Y., & Liu, Y l. (2022). A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections. Applied Energy, 310, (10), 24.
[11] Komiyama, R., & Fujii, Y. (2013). Analysis of energy saving and environmental characteristics of electric vehicle in regionally-disaggregated world energy model. Electrical Engineering in Japan, 186(4), 20-36.
[12] Guo, H., He, H., & Sun, F. (2013). A combined cooperative braking model with a predictive control strategy in an electric vehicle. Energies, 6(12), 6455-6475.
[13] Hongwei, Zhang, W., Chen, Z., Shang, Z., Xu, Z., & Gao, Y l. (2020). Optimum driving system design for dual-motor pure electric vehicles. Journal of Beijing Institute of Technology, 29,106(04), 161-171.
[14] Xie, L., Luo, Y., Li, S., & Li, K. (2018). Coordinated control for adaptive cruise control system of distributed drive electric vehicles. Qiche Gongcheng/Automotive Engineering, 40(6), 652-658.
[15] Chasse, A., & Sciarretta, A. (2011). Supervisory control of hybrid powertrains: an experimental benchmark of offline optimization and online energy management. Control Engineering Practice, 19(11), 1253-1265.
[16] Salehi, J., Namvar, A., & Gazijahani, F. S. (2019). Scenario-based co-optimization of neighboring multi carrier smart buildings under demand response exchange. Journal of Cleaner Production, 235(20), 1483-1498.
[17] Zeng, X., Qian, Q., Chen, H., Song, D., & Li, G. (2021). A unified quantitative analysis of fuel economy for hybrid electric vehicles based on energy flow. Journal of Cleaner Production, 292(7411), 126040.
[18] Ubukata, N., Fukasawa, S., Yamashita, Y., & Kaneko, K. (2012). Approach to the optimal energy-saving system for railway vehicles. Japanese Railway Engineering, 52(3), 51.
[19] Chen, Z., Liu, W., Yang, Y., & Chen, W. (2015). Online energy management of plug-in hybrid electric vehicles for prolongation of all-electric range based on dynamic programming. Mathematical Problems in Engineering, 2015, (17)1-11.
[20] Wang, H., Jiang, Z., Wang, Y., Zhang, H., & Wang, Y. (2018). A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. Journal of Cleaner Production, 188(1), 575-588.

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