IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i18p5033-d1755015.html
   My bibliography  Save this article

Driver-Oriented Adaptive Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Buses

Author

Listed:
  • Xiang Tian

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
    Chery New Energy Automobile Co., Ltd., Wuhu 241000, China)

  • Ma Wan

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Xinqiang Chen

    (Chery New Energy Automobile Co., Ltd., Wuhu 241000, China)

  • Yingfeng Cai

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Xiaodong Sun

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Zhen Zhu

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

Abstract

The adaptability of the supervisory control strategy of plug-in hybrid electric buses (PHEBs) to different driving styles determines the energy-saving performance. This paper proposes a driver-oriented adaptive equivalent consumption minimization strategy (ECMS) for PHEBs. The strategy aims to improve the fuel economy of PHEBs as much as possible by adapting to different driving styles while satisfying the physical constraints of the hybrid power system. Firstly, an online driving style recognition algorithm based on the Fuzzy K -means (FKM) algorithm and the random forest (RF) method is devised, in which the FKM algorithm is used to preprocess the feature parameters related to driving styles and the RF method is utilized to identify the driver’s driving style. Secondly, the driving style recognition results are introduced into the ECMS framework to form a driver-oriented energy management strategy. Finally, the proposed control strategy is verified using both Matlab/Simulink and Hardware-in-the-Loop. The verification results demonstrate that the proposed control strategy improves the fuel economy of PHEBs.

Suggested Citation

  • Xiang Tian & Ma Wan & Xinqiang Chen & Yingfeng Cai & Xiaodong Sun & Zhen Zhu, 2025. "Driver-Oriented Adaptive Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Buses," Energies, MDPI, vol. 18(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5033-:d:1755015
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/18/5033/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/18/5033/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shi, Dehua & Xu, Han & Wang, Shaohua & Hu, Jia & Chen, Long & Yin, Chunfang, 2024. "Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network," Energy, Elsevier, vol. 305(C).
    2. Xu, Bin & Rathod, Dhruvang & Zhang, Darui & Yebi, Adamu & Zhang, Xueyu & Li, Xiaoya & Filipi, Zoran, 2020. "Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    3. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    4. Amir Ansari & Hamidreza Abediasl & Mahdi Shahbakhti, 2024. "Ambient Temperature Effects on Energy Consumption and CO 2 Emissions of a Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 17(14), pages 1-21, July.
    5. Tang, Xiaolin & Zhang, Dejiu & Liu, Teng & Khajepour, Amir & Yu, Haisheng & Wang, Hong, 2019. "Research on the energy control of a dual-motor hybrid vehicle during engine start-stop process," Energy, Elsevier, vol. 166(C), pages 1181-1193.
    6. Duhr, Pol & Christodoulou, Grigorios & Balerna, Camillo & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher H., 2021. "Time-optimal gearshift and energy management strategies for a hybrid electric race car," Applied Energy, Elsevier, vol. 282(PA).
    7. Zhen Zhu & Lingxin Zeng & Long Chen & Rong Zou & Yingfeng Cai, 2022. "Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    8. 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).
    9. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    10. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    11. Jinyang Li & Zhaozhao Wu & Meiqing Li & Zhijian Shang, 2024. "Dynamic Measurement Method for Steering Wheel Angle of Autonomous Agricultural Vehicles," Agriculture, MDPI, vol. 14(9), pages 1-21, September.
    12. Zhen Zhu & Yanpeng Yang & Dongqing Wang & Yingfeng Cai & Longhui Lai, 2022. "Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System," Agriculture, MDPI, vol. 12(3), pages 1-22, March.
    13. Shi, Dehua & Li, Shiqi & Xu, Han & Wang, Shaohua & Wang, Limei, 2025. "Design and test of adaptive energy management strategy for plug-in hybrid electric vehicle considering traffic information," Energy, Elsevier, vol. 325(C).
    14. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    15. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    16. 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).
    Full references (including those not matched with items on IDEAS)

    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. Dawei Zhong & Bolan Liu & Liang Liu & Wenhao Fan & Jingxian Tang, 2025. "Artificial Intelligence Algorithms for Hybrid Electric Powertrain System Control: A Review," Energies, MDPI, vol. 18(8), pages 1-30, April.
    2. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    3. Zifeng Pei & Li Zhang & Haijun Fu & Yucheng Wang, 2025. "New Fault-Tolerant Sensorless Control of FPFTPM Motor Based on Hybrid Adaptive Robust Observation for Electric Agricultural Equipment Applications," Energies, MDPI, vol. 18(8), pages 1-22, April.
    4. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    5. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    6. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    7. Hengrui Cao & Konghao Xu & Li Zhang & Zhongqiu Liu & Ziyang Wang & Haijun Fu, 2025. "Multi-Plane Virtual Vector-Based Anti-Disturbance Model Predictive Fault-Tolerant Control for Electric Agricultural Equipment Applications," Energies, MDPI, vol. 18(14), pages 1-19, July.
    8. 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).
    9. 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).
    10. Haobin Jiang & Yang Zhao & Shidian Ma, 2025. "Dual-Layer Energy Management Strategy for a Hybrid Energy Storage System to Enhance PHEV Performance," Energies, MDPI, vol. 18(7), pages 1-20, March.
    11. Zhang, Yaoyuan & Wu, Haoqing & Mi, Shijie & He, Zhuoyao & Qian, Yong & Lu, Xingcai, 2024. "Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy," Energy, Elsevier, vol. 307(C).
    12. Yiyong Jiang & Ruochen Wang & Renkai Ding & Zeyu Sun & Yu Jiang & Wei Liu, 2025. "Research Review of Agricultural Machinery Power Chassis in Hilly and Mountainous Areas," Agriculture, MDPI, vol. 15(11), pages 1-37, May.
    13. 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).
    14. Hongxuan Wu & Xinzhong Wang & Xuegeng Chen & Yafei Zhang & Yaowen Zhang, 2025. "Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery," Agriculture, MDPI, vol. 15(12), pages 1-28, June.
    15. Yifei Yang & Yifang Wen & Xiaodong Sun & Renzhong Wang & Ziyin Dong, 2025. "A Review of Green Agriculture and Energy Management Strategies for Hybrid Tractors," Energies, MDPI, vol. 18(13), pages 1-23, June.
    16. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    17. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    18. Yu, Xiao & Lin, Cheng & Tian, Yu & Zhao, Mingjie & Liu, Huimin & Xie, Peng & Zhang, JunZhi, 2023. "Real-time and hierarchical energy management-control framework for electric vehicles with dual-motor powertrain system," Energy, Elsevier, vol. 272(C).
    19. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    20. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:18:y:2025:i:18:p:5033-:d:1755015. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.