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Optimization-based method to develop practical driving cycle for application in electric vehicle power management: A case study in Shenyang, China

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  • Chen, Zeyu
  • Zhang, Qing
  • Lu, Jiahuan
  • Bi, Jiangman

Abstract

In this study, a novel method for the construction of a driving cycle based on a two-layer optimization process is proposed with a case study in Shenyang, China. First, the statistical data is obtained and divided into many micro-trips, namely the speed profiles between two successive stops; then, three representative parameters are derived from the vehicular model. Second, the development of the driving cycle is transferred to an optimization problem, and a two-layer optimization method is proposed to construct the typical driving cycle. In the first layer, the optimal combination of micro-trips is determined using a genetic algorithm (GA) with varying quantity of micro-trips, whereas in the second layer, the best quantity of micro-trips is determined according to the speed–acceleration probability distribution (SAPD) and average energy consumption (AEC). The results indicate that the proposed method can produce a more representative driving cycle, 2.49% closer to the statistical data than the traditional Markov chain method. Finally, the established driving cycle is applied to power management design with three different vehicle types. The results indicate that the established driving cycle can help in reducing the energy cost by up to 19.8% under the real-world Shenyang driving condition.

Suggested Citation

  • Chen, Zeyu & Zhang, Qing & Lu, Jiahuan & Bi, Jiangman, 2019. "Optimization-based method to develop practical driving cycle for application in electric vehicle power management: A case study in Shenyang, China," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314379
    DOI: 10.1016/j.energy.2019.07.096
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    Cited by:

    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. Iwona Komorska & Andrzej Puchalski & Andrzej Niewczas & Marcin Ślęzak & Tomasz Szczepański, 2021. "Adaptive Driving Cycles of EVs for Reducing Energy Consumption," Energies, MDPI, vol. 14(9), pages 1-18, May.
    3. Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    4. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

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