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Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle

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  • Wei, Changyin
  • Chen, Yong
  • Li, Xiaoyu
  • Lin, Xiaozhe

Abstract

Improving the fuel economy and optimizing power allocation of the extender range electric logistics vehicles importantly relies on reasonable energy management strategies and accurate driving pattern recognition. Herein, an adaptive equivalent consumption minimization strategy for extender range electric logistics vehicles is proposed. Firstly, a K-means clustering algorithm has been utilized to classify driving blocks. A novel driving pattern recognition approach is designed by using combination of variational mode decomposition (VMD) and extreme learning machine (ELM). Subsequently, a state-of-charge reference planning approach is proposed based on traffic information with dynamic programing for guiding battery energy allocation. Combining the driving pattern recognition results with the reference state-of-charge value, the proposed method acquires the optimal control action through minimizing the objective function. A hardware-in-the-loop test platform by Model Based Design (MBD) approach is implemented to validate the performance of controller. It has been verified that the proposed method can conserve the equivalent fuel consumption by respectively over 6.04% and 3.79%, and suppress the average battery power transients by over 11.61% and 2.85%, compared with the conventional energy management strategies.

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  • 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).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003814
    DOI: 10.1016/j.energy.2022.123478
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    as
    1. 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.
    2. Ren, Guizhou & Wang, Jinzhong & Chen, Changlei & Wang, Haoran, 2021. "A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle," Energy, Elsevier, vol. 231(C).
    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. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    5. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    6. Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
    7. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    8. 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.
    9. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    10. 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).
    11. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    12. 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).
    13. 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.
    14. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    15. 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.
    16. 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).
    17. 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.
    18. 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.
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