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Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles

Author

Listed:
  • Xingyue Jiang

    (State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

  • Jianjun Hu

    (State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
    College of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Meixia Jia

    (State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

  • Yong Zheng

    (State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

Abstract

In order to complete the reasonable parameter matching of the pure electric vehicle (PEV) with a hybrid energy storage system (HESS) consisting of a battery pack and an ultra-capacitor pack, the impact of the selection of the economic index and the control strategy on the parameters matching cannot be ignored. This paper applies a more comprehensive total cost of ownership (TCO) of HESS as the optimal target and proposes an optimal methodology integrating parameters and control strategy for the PEV with HESS. Through the integrated optimal methodology, the application value of HESS is analyzed under various types of driving cycles and the results indicate that the HESS can significantly improve the economic performance of PEVs under both urban and suburban driving cycles. Due to the poor adaptability of traditional control strategies to different driving cycles, a novel extreme learning machine (ELM) based controller is established. Firstly, a dynamic programming (DP) based controller is applied for the offline optimization of the HESS power allocation under several typical driving cycles. Then, an analytical method combining correlation analysis and mean impact value (MIV) is employed to deal with offline sample data from DP and obtain the characteristic variables of the ELM model. Ultimately, the instantaneous power allocation strategy of HESS is acquired by utilizing ELM to learn offline data of HESS. Comparative simulations between the ELM-based controller and the rule-based controller are conducted, and the simulation results show that compared to the rule-based controller (RBC), the ELM-based controller reduces the electricity consumption by 3.78% and battery life loss by 6.51%.

Suggested Citation

  • Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1933-:d:159780
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    References listed on IDEAS

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    2. Danijel Pavković & Mihael Cipek & Zdenko Kljaić & Tomislav Josip Mlinarić & Mario Hrgetić & Davor Zorc, 2018. "Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-26, October.
    3. Philip K. Agyeman & Gangfeng Tan & Frimpong J. Alex & Jamshid F. Valiev & Prince Owusu-Ansah & Isaac O. Olayode & Mohammed A. Hassan, 2022. "Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines," Energies, MDPI, vol. 15(19), pages 1-23, September.
    4. Jixiang Yang & Yongming Bian & Meng Yang & Jie Shao & Ao Liang, 2021. "Parameter Matching of Energy Regeneration System for Parallel Hydraulic Hybrid Loader," Energies, MDPI, vol. 14(16), pages 1-26, August.

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