Покращення споживання енергії за допомогою ADAS в автономному водінні комунікаційної системи
DOI:
https://doi.org/10.30837/bi.2024.1(100).03Ключові слова:
ЕКОНОМІЯ ЕНЕРГОСПОЖИВАННЯ, ЕЛЕКТРОТРАНСПОРТ, АВТОНОМНІ АВТОМОБІЛІ, ADAS, ЕКО-ВОДІННЯ, РЕКУПЕРАЦІЯАнотація
У статті проведений аналіз тенденцій розвитку технологій енергозбереження та рекуперації на електричному автомобільному транспорті. Розглянуто та визначено перспективні напрямки розвитку технологій енергозбереження зокрема з використанням систем допомоги водієві. Збільшення наповненості автомобілів електронними системами допомоги водієві збільшує загальні витрати енергії. Наведені рекомендації з вибору оптимальних технологій та методів енергозбереження для автотранспорту. Для деяких методів економія спожитої енергії є доповненням до основного напрямку роботи. Поєднання інтелектуальних транспортних засобів та відповідних засобів організації дорожнього руху може сприяти подальшій реалізації транспортних переваг інтелектуальних транспортних засобів.
Посилання
Z. Liu, X. Liu, and F. Zhao, “Research on NEV Platform Development Strategies for Automotive Companies,” World Electric Vehicle Journal, vol. 12, no. 4, p. 201, Dec. 2021, [Online]: https://doi.org/10.3390/wevj12040201.
Rolando Campos Canales, and Gabriel Pérez. “Autonomous Vehicles and Alternative Energies for Logistics in the Wake of the Pandemic.” Facilitation of Transport and Trade in Latin America and the Caribbean, vol. 4, no. 338, 8 Nov. 2021. ISSN: 1564-4243.
Yadlapalli, Ravindranath Tagore, et al. “A Review on Energy Efficient Technologies for Electric Vehicle Applications.” Journal of Energy Storage, vol. 50, June 2022, https://doi.org/10.1016/j.est.2022.104212
Shih-Chieh Lin at al. 2018. The Architectural Implications of Autonomous Driving: Constraints and Acceleration. SIGPLAN Not. 53, 2 (February 2018), 751–766. https://doi.org/10.1145/3296957.3173191
Sarnago, H.; Lucía, Ó. Popa, I.O.; Burdío, J.M. Constant-Current Gate Driver for GaN HEMTs Applied to Resonant Power Conversion. Energies 2021, 14, 2377. https://doi.org/10.3390/
VAN DER AALST, Wil. Six Levels of Autonomous Process Execution Management (APEM). arXiv preprint arXiv:2204.11328, 2022. https://doi.org/10.48550/arXiv.2204.11328
D. Katare, D. Perino, J. Nurmi, M. Warnier, M. Janssen and A. Y. Ding, "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services," in IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2714-2754, Fourthquarter 2023, doi: 10.1109/COMST.2023.3302474.
Hamed Faghihian, and Arman Sargolzaei. “Energy Efficiency of Connected Autonomous Vehicles: A Review.” Electronics, vol. 12, no. 19, 29 Sept. 2023. [Online]: https://doi.org/10.3390/electronics12194086.
Jiang Y, Zhao B, Li M, Yao Z. A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment. Journal of Advanced Transportation. 2021 Apr 26;2021:1–13. [Online]: https://doi.org/10.1155/2021/9945398
Gee, K. Faust, and M. Webber, “A framework for determining energy use in rural food delivery services: capturing system interdependencies through an agent-based discrete-event approach,” Environmental Research: Infrastructure and Sustainability, Sep. 2021, doi: https://doi.org/10.1088/2634-4505/ac2b10.
Á. Halldórsson and J. Wehner, “Last-mile logistics fulfilment: A framework for energy efficiency,” Research in Transportation Business & Management, vol. 37, p. 100481, Apr. 2020, doi: https://doi.org/10.1016/j.rtbm.2020.100481.
Ma, Hao, et al. “Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control.” Electrochem, vol. 3, no. 3, 1 Sept. 2022, pp. 337–346, www.mdpi.com/2673-3293/3/3/23, https://doi.org/10.3390/electrochem3030023. Accessed 3 Dec. 2023.
Dollar, R.A.; Vahidi, A. Quantifying the impact of limited information and control robustness on connected automated platoons. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 16–19 October 2017; pp. 1–7. doi: 10.1109/ITSC.2017.8317604.
Lee, Jooyong, and Kara Kockelman. Energy Implications of Self-Driving Vehicles. The University of Texas at Austin, Jan. 2019. [Online]: https://www.caee.utexas.edu/prof/kockelman/public_html/TRB19EnergyAndEmissions.pdf
Song H, Zhao F, Zhu G, Liu Z. Impacts of Connected and Autonomous Vehicles with Level 2 Automation on Traffic Efficiency and Energy Consumption. Journal of Advanced Transportation. Apr. 2023;2023:e6348778. [Online]: https://www.hindawi.com/journals/jat/2023/6348778/ https://doi.org/10.1155/2023/6348778
"SAE: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems", SAE On-road Automated Vehicles Standards Committee and others, 2021. [Online]. Available: https://www.sae.org/standards/content/j3016_202104/
Chen B, Chen Y, Wu Y, Xiu Y, Fu X, Zhang K. The Effects of Autonomous Vehicles on Traffic Efficiency and Energy Consumption. Systems. 2023 Jul 1;11(7):347. [Online]: https://www.mdpi.com/2079-8954/11/7/347 doi.org/10.3390/systems11070347
Qu X, Yu Y, Zhou M, Lin CT, Wang X. Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach. Applied Energy. 2020 Jan;257:114030. [Online]: https://doi.org/10.1016/j.apenergy.2019.114030
S. A. Fayazi and A. Vahidi, "Mixed-Integer Linear Programming for Optimal Scheduling of Autonomous Vehicle Intersection Crossing," in IEEE Transactions on Intelligent Vehicles, vol. 3, no. 3, pp. 287-299, Sept. 2018, doi: 10.1109/TIV.2018.2843163.
He, Xiaoyi, et al. “Energy Consumption Simulation for Connected and Automated Vehicles: Eco-Driving Benefits versus Automation Loads.” SAE International Journal of Connected and Automated Vehicles, vol. 6, no. 1, 9 May 2022.[Online]: https://doi.org/10.4271/12-06-01-0002.
U.S. Environment Protection Agency (EPA), “Compliance and Fuel Economy Data for Vehicles and Engines,” accessed November 12, 2019, [Online]: https://www.epa.gov/compliance-andfuel-economy-data/data-cars-used-testing-fuel-economy.
R. Ma, X. He, Y.-L. Zheng, B. Zhou, S.-N. Lu, and Y. Wu, “Real-world driving cycles and energy consumption informed by large-sized vehicle trajectory data,” Journal of Cleaner Production, vol. 223, pp. 564–574, Jun. 2019, doi: https://doi.org/10.1016/j.jclepro.2019.03.002.
Zhao,W.;Wu, G.;Wang, C.; Yu, L.; Li, Y. Energy transfer and utilization efficiency of regenerative braking with hybrid energy storage system. J. Power Sources 2019, 427, 174–183.
J. Valladolid, M. Calle, and A. Guiracocha, “Analysis of regenerative braking efficiency in an electric vehicle through experimental tests,” Ingenius, no. 29, pp. 24–31, Jan. 2023, doi: https://doi.org/10.17163/ings.n29.2023.02.
Kim, D.; Eo, J.S.; Kim, Y.; Guanetti, J.; Miller, R.; Borrelli, F. Energy-Optimal Deceleration Planning System for Regenerative Braking of Electrified Vehicles with Connectivity and Automation; Technical Report; SAE:Warrendale, PA, USA, 2020.
Abdelkareem, M., Lin, X., Ahmed, A., Ahmed, E., Jia, M. et al., “Vibration energy harvesting in automotive suspension system: A detailed review,” Applied Energy, vol. 229, pp. 672–699, Nov. 2018, doi: https://doi.org/10.1016/j.apenergy.2018.08.030.
R. Zhang, X. Wang, and S. John, “A Comprehensive Review of the Techniques on Regenerative Shock Absorber Systems,” Energies, vol. 11, no. 5, p. 1167, May 2018, doi: https://doi.org/10.3390/en11051167.
