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A Novel Adaptive Equivalence Fuel Consumption Minimisation Strategy for a Hybrid Electric Two-Wheeler

Author

Listed:
  • Naga Kavitha Kommuri

    (Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK)

  • Andrew McGordon

    (Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK)

  • Antony Allen

    (Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK)

  • Dinh Quang Truong

    (Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK)

Abstract

One of the major challenges in implementing the equivalent fuel consumption minimisation strategy in hybrid electric vehicles is the adaptation of the equivalence factor to real-world driving. In this paper, a novel adaptive equivalent fuel consumption minimisation strategy (A-ECMS) has been developed for a hybrid two-wheeler to further improve fuel savings by predicting the drive cycles and thereby estimating and adapting the equivalence factor online for the ECMS energy management control. A learning vector quantitative neural network (LVQNN)-based classifier was first proposed to recognise the real-world driving cycle based on a fixed time window of past driving information. Along with standardised drive cycles, real-world driving data were used in the learning process to increase the robustness of the learning. The A-ECMS is then capable of regulating its equivalence factors online based on the LVQNN controller output. Numerical simulation results indicated that there was considerable improvement in fuel economy of the vehicle with the proposed methodology, up to 10.7%, compared to the use of traditional ECMS which was manually optimised for a single drive cycle. The average improvement in fuel economy over the ten drive cycles considered for testing is 3.93%.

Suggested Citation

  • Naga Kavitha Kommuri & Andrew McGordon & Antony Allen & Dinh Quang Truong, 2022. "A Novel Adaptive Equivalence Fuel Consumption Minimisation Strategy for a Hybrid Electric Two-Wheeler," Energies, MDPI, vol. 15(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3192-:d:803362
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    References listed on IDEAS

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    1. Tri, Nguyen Minh & Truong, Dinh Quang & Thinh, Do Hoang & Binh, Phan Cong & Dung, Dang Tri & Lee, Seyoung & Park, Hyung Gyu & Ahn, Kyoung Kwan, 2016. "A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter," Renewable Energy, Elsevier, vol. 97(C), pages 518-531.
    2. Yuping Zeng & Yang Cai & Guiyue Kou & Wei Gao & Datong Qin, 2018. "Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    3. Naga Kavitha Kommuri & Andrew McGordon & Antony Allen & Dinh Quang Truong, 2020. "Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler," Energies, MDPI, vol. 13(12), pages 1-26, June.
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    Cited by:

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