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Energy optimization strategy of vehicle DCS system based on APSO algorithm

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  • Zou, Songchun
  • Zhao, Wanzhong

Abstract

In order to improve the reliability and safety of the steer-by-wire (SBW) system, this paper presents a vehicle dual-motor coupling-drive steer-by-wire (DCS) system. However, the introduction of dual-motor will change the energy consumption of the system. Aiming at the energy consumption problem, an energy optimization strategy is proposed to improve the economic performance of the DCS system. Then, the energy optimization model of the DCS system with minimum power consumption is established based on the required torque, the rotating speed of steering wheel and the map of motor efficiency, and the adaptive particle swarm optimization (APSO) algorithm is adopted to optimize the torque distribution coefficient. Finally, combined with Matlab/Simulink, Prescan, Carsim and steering data acquisition test bench, the co-simulation is conducted under sinusoidal condition, double-lane change condition and intelligent transportation environment. The results indicate that compared with traditional single-motor SBW system and DCS system with average torque distribution, the energy consumption of the DCS system with optimized torque distribution is reduced by more than 5.2% and 4.3% under sinusoidal condition and double-lane change condition, while the corresponding values under the intelligent transportation environment are decreased by 5.2% and 7.5% respectively, which demonstrates the effectiveness of the proposed energy optimization strategy.

Suggested Citation

  • Zou, Songchun & Zhao, Wanzhong, 2020. "Energy optimization strategy of vehicle DCS system based on APSO algorithm," Energy, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:energy:v:208:y:2020:i:c:s0360544220315115
    DOI: 10.1016/j.energy.2020.118404
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    References listed on IDEAS

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    1. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).

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