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Development of multiple driving cycles with equivalent energy consumption to expand a standard driving cycle

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  • Xu, Nan
  • Liu, Qiao
  • Zhang, Zhaomao
  • Li, Jincheng

Abstract

Standard driving cycles have been developed worldwide to provide a universal norm for evaluating and optimizing vehicle economics. These cycles are generated via methods based on the idea of averaging assumptions, expressed in a single and fixed form as a speed-time profile and open to the public, resulting in lacking representation of the real world, risk of cheating during tests, and suboptimization during vehicle design and development. Therefore, in this paper, a novel method for developing multiple driving cycles with equivalent energy consumption is proposed, with the aim of ensuring that test cycles are no longer singular and fixed but can be randomly chosen with guaranteed comparability. Multiple driving cycles are generated with comparable effect to a standard driving cycle. Both simulation and dynamometer tests were performed for validation. The results revealed that the maximum deviations reached 0.77 % for the simulations and 1.79 % for the dynamometer tests. The lack of representation can also be addressed by using user data as a reference. This study highlighted the importance of the driving cycle being stochastic and selectable in vehicle energy consumption testing. The proposed method and conclusions can be referenced as a direction for improving future vehicle economic evaluation and optimization methods.

Suggested Citation

  • Xu, Nan & Liu, Qiao & Zhang, Zhaomao & Li, Jincheng, 2025. "Development of multiple driving cycles with equivalent energy consumption to expand a standard driving cycle," Energy, Elsevier, vol. 319(C).
  • Handle: RePEc:eee:energy:v:319:y:2025:i:c:s0360544225005936
    DOI: 10.1016/j.energy.2025.134951
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    References listed on IDEAS

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    1. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    2. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    3. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    4. Tao, Siyou & Ding, Ke & Li, Zhuoyun & Zhang, Hui, 2022. "Development of a representative driving cycle for evaluating exhaust emission and fuel consumption for Chinese switcher locomotives," Applied Energy, Elsevier, vol. 322(C).
    5. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    6. Kangda Chen & Fuquan Zhao & Xinglong Liu & Han Hao & Zongwei Liu, 2021. "Impacts of the New Worldwide Light-Duty Test Procedure on Technology Effectiveness and China’s Passenger Vehicle Fuel Consumption Regulations," IJERPH, MDPI, vol. 18(6), pages 1-20, March.
    7. Hull, Christopher & Collett, Katherine A. & McCulloch, Malcolm D., 2024. "Developing a representative driving cycle for paratransit that reflects measured data transients: Case study in Stellenbosch, South Africa," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    8. Fan, Pengfei & Yin, Hang & Lu, Hongyu & Wu, Yizheng & Zhai, Zhiqiang & Yu, Lei & Song, Guohua, 2023. "Which factor contributes more to the fuel consumption gap between in-laboratory vs. real-world driving conditions? An independent component analysis," Energy Policy, Elsevier, vol. 182(C).
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