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Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle

Author

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  • Baodi Zhang

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Sheng Guo

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xin Zhang

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Qicheng Xue

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Lan Teng

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The series hybrid electric powertrain is the main architecture of the hybrid electric tracked vehicle. For a series tracked hybrid electric bulldozer (HEB), frequent fluctuations of the engine working points, deviation of the genset working points from the pre-set target trajectory due to an insufficient response, or interference of the hydraulic pump consumed torque, will all result in increased fuel consumption. To solve the three problems of fuel economy, an adaptive smooth power following (ASPF) control strategy based on an optimal efficiency map is proposed. The strategy combines a fuzzy adaptive filter algorithm with a genset’s optimal efficiency, which can adaptively smooth the working points of the genset and search the trajectory for the genset’s best efficiency when the hydraulic pump torque is involved. In this study, the proposed strategy was compared on the established HEB hardware in loop (HIL) platform with two other strategies: a power following strategy in a preliminarily practical application (PF1) and a typical power following strategy based on the engine minimum fuel consumption curve (PF2). The results of the comparison show that (1) the proposed approach can significantly reduce the fluctuation and pre-set trajectory deviation of the engine and generator working points; (2) the ASPF strategy achieves a 7.8% improvement in the equivalent fuel saving ratio (EFSR) over the PF1 strategy, and a 3.4% better ratio than the PF2 strategy; and (3) the ASPF strategy can be implemented online with a practical controller.

Suggested Citation

  • Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1893-:d:344928
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    References listed on IDEAS

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    Cited by:

    1. Aaron Shmaryahu & Nissim Amar & Alexander Ivanov & Ilan Aharon, 2021. "Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-21, August.
    2. Emad Roshandel & Amin Mahmoudi & Solmaz Kahourzade & Amirmehdi Yazdani & GM Shafiullah, 2021. "Losses in Efficiency Maps of Electric Vehicles: An Overview," Energies, MDPI, vol. 14(22), pages 1-27, November.

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