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The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle

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  • Ruan, Jiageng
  • Walker, Paul D.
  • Watterson, Peter A.
  • Zhang, Nong

Abstract

As motor-supplied braking torque is applied to the wheels in an entirely different way to hydraulic friction braking systems and it is usually only connected to one axle complicated effects such as wheel slip and locking, vehicle body bounce and braking distance variation will inevitability impact on the performance and safety of braking. The potential for braking energy recovery in typical driving cycles is presented to show its benefit in this study. A general predictive model is designed to analysis the economic and dynamic performance of blended braking systems, satisfying the relevant regulations/laws and critical limitations. Braking strategies for different purposes are proposed to achieve a balance between braking performance, driving comfort and energy recovery rate. Special measures are taken to avoid any effects of motor failure. All strategies are analyzed in detail for various braking events. Advanced driver assistance systems (ADAS), such as ABS and EBD, are properly integrated to work with the regenerative braking system (RBS) harmoniously. Different switching plans during braking are discussed. The braking energy recovery rates and brake force distribution details for different driving cycles are simulated. Results for two of the cycles in an ‘Eco’ mode are measured on a drive train test rig and found to agree with the simulated results to within approximately 10%. Reliable conclusions can thus be gained on the economic benefit and dynamic braking performance. The strategies proposed in this paper are shown to not only achieve comfortable and safe braking during all driving conditions, but also to significantly reduce cost in both the short and long term.

Suggested Citation

  • Ruan, Jiageng & Walker, Paul D. & Watterson, Peter A. & Zhang, Nong, 2016. "The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle," Applied Energy, Elsevier, vol. 183(C), pages 1240-1258.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:1240-1258
    DOI: 10.1016/j.apenergy.2016.09.057
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    References listed on IDEAS

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    Cited by:

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    2. Boyi Xiao & Huazhong Lu & Hailin Wang & Jiageng Ruan & Nong Zhang, 2017. "Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis," Energies, MDPI, vol. 10(11), pages 1-19, November.
    3. Yang, Jian & Liu, Bo & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin, 2023. "Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation," Energy, Elsevier, vol. 263(PC).
    4. Ruan, Jiageng & Wu, Changcheng & Liang, Zhaowen & Liu, Kai & Li, Bin & Li, Weihan & Li, Tongyang, 2023. "The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode," Energy, Elsevier, vol. 269(C).
    5. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng & Li, Xiaoyu & Qu, Changhui, 2019. "Driving cycles construction for electric vehicles considering road environment: A case study in Beijing," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    7. Sánchez, Marcelino & Delprat, Sébastien & Hofman, Theo, 2020. "Energy management of hybrid vehicles with state constraints: A penalty and implicit Hamiltonian minimization approach," Applied Energy, Elsevier, vol. 260(C).
    8. Emilia M. Szumska & Rafał Jurecki, 2022. "The Analysis of Energy Recovered during the Braking of an Electric Vehicle in Different Driving Conditions," Energies, MDPI, vol. 15(24), pages 1-16, December.
    9. Xu Hu & Yisong Chen & Zhensen Ding & Liang Gu, 2019. "Vehicle Optimal Control Design to Meet the 1.5 °C Target: A Control Design Framework for Vehicle Subsystems," Energies, MDPI, vol. 12(16), pages 1-21, August.
    10. Guwen Tang & Meng Zhang & Fei Bu, 2023. "Vehicle Environmental Efficiency Evaluation in Different Regions in China: A Combination of the Life Cycle Analysis (LCA) and Two-Stage Data Envelopment Analysis (DEA) Methods," Sustainability, MDPI, vol. 15(15), pages 1-24, August.

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