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Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode

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  • Zhang, Bo
  • Zhang, Jiangyan
  • Shen, Tielong

Abstract

This paper discusses a control design for improving ride comfort while maintaining the powertrain performance and power demand of a hybrid electric vehicle in a specific acceleration scenario. A parallel hybrid powertrain system with two electric machines and a single turbo-charged engine is built. The acceleration scenario is designed under the assumption that the desired acceleration rate cannot be achieved by using an electric motor alone. Therefore, improving ride comfort while also managing the torque split is challenging. This work involves two steps. First, a black-box module that is typically used in the automotive industry is exploited to quantitatively evaluate ride comfort. A genetic algorithm (GA) is used to generate an optimal acceleration curve and gear schedule by analyzing the characteristics of the black-box module. Second, two optimization problems are formulated to provide a reasonable power split with the purpose of tracking the reference acceleration provided by the GA. The problems are solved by dynamic programming and sequential quadratic programming. Comparison of the simulation results between the two different solutions is conducted on a simulator with a practical background and demonstrates the significance and efficiency of the proposed control design.

Suggested Citation

  • Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012010
    DOI: 10.1016/j.apenergy.2021.117885
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    References listed on IDEAS

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    Cited by:

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    2. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    3. Anselma, Pier Giuseppe, 2022. "Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios," Applied Energy, Elsevier, vol. 314(C).
    4. Gao, Bingzhao & Meng, Dele & Shi, Wentong & Cai, Wenqi & Dong, Shiying & Zhang, Yuanjian & Chen, Hong, 2022. "Topology optimization and the evolution trends of two-speed transmission of EVs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).

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