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Research and Simulation Analysis of Brake Energy Recovery Control Strategy for Pure Electric Vehicles

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Genetic and Evolutionary Computing (ICGEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1107))

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Abstract

With environmental pollution and energy crisis, energy conservation and emission reduction are the focus of new energy vehicle development, and braking energy recovery is one of the effective ways. In this paper, AVL Cruise is used to build a pure electric vehicle model, Matlab/Simulink to establish a braking energy recovery control strategy, and finally a joint simulation analysis of a NEDC operating cycle, Compared with the pure electric vehicle without braking energy recovery. the results show that the electric power consumption of the pure electric vehicle with braking energy recovery is reduced by 100 km, which verifies the feasibility of the control strategy.

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Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Xiamen University of Technology, No. 600, Ligong Road, Xiamen, 361024, Fujian, China

    Qi-Chao Li & Yi-Jui Chiu

Authors
  1. Qi-Chao Li
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  2. Yi-Jui Chiu
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Corresponding author

Correspondence to Yi-Jui Chiu .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jeng-Shyang Pan

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, Australia

    Shu-Chuan Chu

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Li, QC., Chiu, YJ. (2020). Research and Simulation Analysis of Brake Energy Recovery Control Strategy for Pure Electric Vehicles. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_12

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