IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v230y2021ics0360544221011063.html
   My bibliography  Save this article

Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle

Author

Listed:
  • Wei, Changyin
  • Sun, Xiuxiu
  • Chen, Yong
  • Zang, Libin
  • Bai, Shujie

Abstract

This paper deals with the comparison of architecture and adaptive energy management strategy (EMS) for hybrid powertrain system (HPS), including one or two electric motor, an engine and a battery, for a plug-in hybrid electric logistics vehicle (PHELV). The most attractive advantage deriving from HPSs is the possibility of reducing emission and improving fuel economic. For comparison purposes, the series, parallel, and series-parallel hybrid powertrain system are examined by dynamic programming (DP) algorithm using the same vehicular parameters. The approach of adaptive EMS is driving pattern recognition (DPR) to obtain optimum estimation of EMS parameters under different driving cycle. A back propagation (BP) neural network DPR optimized model by an improved genetic algorithm (IGA) has been proposed. Taking the costs of fuel consumption, the parameters of the fuzzy logic controller (FLC) and equivalent consumption minimization strategy (ECMS) are optimized. The comparative results show that the series PHELV fuel economy improvements are 7.60% and 6.53%, compared with parallel and series-parallel PHELV. The difference between the optimal fuzzy energy management strategy and the global optimization is 4.74%, and the ECMS is 4.66%.

Suggested Citation

  • Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221011063
    DOI: 10.1016/j.energy.2021.120858
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221011063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120858?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    2. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    3. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2020. "Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion," Applied Energy, Elsevier, vol. 272(C).
    4. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    5. Baodi Zhang & Fuyuan Yang & Lan Teng & Minggao Ouyang & Kunfang Guo & Weifeng Li & Jiuyu Du, 2019. "Comparative Analysis of Technical Route and Market Development for Light-Duty PHEV in China and the US," Energies, MDPI, vol. 12(19), pages 1-23, September.
    6. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    7. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    8. Chambon, Paul & Curran, Scott & Huff, Shean & Love, Lonnie & Post, Brian & Wagner, Robert & Jackson, Roderick & Green, Johney, 2017. "Development of a range-extended electric vehicle powertrain for an integrated energy systems research printed utility vehicle," Applied Energy, Elsevier, vol. 191(C), pages 99-110.
    9. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    10. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    11. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    12. Yonggang Liu & Jie Li & Ming Ye & Datong Qin & Yi Zhang & Zhenzhen Lei, 2017. "Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information," Energies, MDPI, vol. 10(4), pages 1-20, March.
    13. Rishikesh Mahesh Bagwe & Andy Byerly & Euzeli Cipriano dos Santos & Zina Ben-Miled, 2019. "Adaptive Rule-Based Energy Management Strategy for a Parallel HEV," Energies, MDPI, vol. 12(23), pages 1-17, November.
    14. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    2. Wu, Wei & Luo, Junlin & Zou, Tiangang & Liu, Yin & Yuan, Shihua & Xiao, Bingqing, 2022. "Systematic design and power management of a novel parallel hybrid electric powertrain for heavy-duty vehicles," Energy, Elsevier, vol. 253(C).
    3. Wang, Kai-Hua & Su, Chi-Wei & Xiao, Yidong & Liu, Lu, 2022. "Is the oil price a barometer of China's automobile market? From a wavelet-based quantile-on-quantile regression perspective," Energy, Elsevier, vol. 240(C).
    4. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    5. Tomáš Settey & Jozef Gnap & František Synák & Tomáš Skrúcaný & Marek Dočkalik, 2021. "Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    6. Bolin He & Yong Chen & Qiang Wei & Cong Wang & Changyin Wei & Xiaoyu Li, 2023. "Performance Comparison of Pure Electric Vehicles with Two-Speed Transmission and Adaptive Gear Shifting Strategy Design," Energies, MDPI, vol. 16(7), pages 1-21, March.
    7. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    2. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    3. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2022. "Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management," Energy, Elsevier, vol. 245(C).
    4. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).
    5. Yang, Chao & Du, Xuelong & Wang, Weida & Yuan, Lijuan & Yang, Liuquan, 2024. "Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 290(C).
    6. Zhang, Hao & Chen, Boli & Lei, Nuo & Li, Bingbing & Chen, Chaoyi & Wang, Zhi, 2024. "Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency," Applied Energy, Elsevier, vol. 360(C).
    7. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    8. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Jen-Chiun Guan & Bo-Chiuan Chen & Yuh-Yih Wu, 2019. "Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles," Energies, MDPI, vol. 12(9), pages 1-24, April.
    10. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    11. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).
    12. Zhuang, Weichao & Zhang, Xiaowu & Li, Daofei & Wang, Liangmo & Yin, Guodong, 2017. "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle," Applied Energy, Elsevier, vol. 204(C), pages 476-488.
    13. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    14. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    15. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    16. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    17. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Xu, Nan & Kong, Yan & Zhang, Yuanjian & Yue, Fenglai & Sui, Yan & Li, Xiaohan & Liu, Heng & Xu, Zhe, 2022. "Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy," Energy, Elsevier, vol. 251(C).
    19. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    20. Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221011063. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.